exploring the development and exploitation of cutting edge ai technology

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Page 1: Exploring the development and exploitation of cutting edge AI technology

0 copy Copyright 2017 FUJITSU

Fujitsu Forum 2017

FujitsuForum

1 copy Copyright 2017 FUJITSU

Exploring the development and exploitation of cutting-edge AI technology

Shoji Suzuki

Member of the Board

Head of Artificial Intelligence Laboratory

Fujitsu Laboratories Ltd

2 copy Copyright 2017 FUJITSU

Agenda

1 Current status of AI and Fujitsu Lab research strategy

2 Implementation in society of AI technologies

3 Cutting-edge AI technology 「Explainable AI」

3 copy Copyright 2017 FUJITSU

Current status of AI and Fujitsu Lab research strategy

1

4 copy Copyright 2017 FUJITSU

History of Artificial Intelligence

1st AI Boom(SearchInference)

2nd AI Boom(Knowledge)

3rd AI Boom(Machine Learning)

(1956-1960s)

(1980s)

1st AI Winter(lsquo74-rsquo80) 2nd AI Winter(lsquo87-rsquo11)

The birth of AI【lsquo56】 (Dartmouth Conference)

The rush of big AI projects (Expert System)

Games with clear goals can be solved but not practical

Rule Base Lisp

Prolog

(2012-)

Far from the knowledge level of human experts

Deep learning surpassed conventional methods in image recognition【lsquo12】

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 2: Exploring the development and exploitation of cutting edge AI technology

1 copy Copyright 2017 FUJITSU

Exploring the development and exploitation of cutting-edge AI technology

Shoji Suzuki

Member of the Board

Head of Artificial Intelligence Laboratory

Fujitsu Laboratories Ltd

2 copy Copyright 2017 FUJITSU

Agenda

1 Current status of AI and Fujitsu Lab research strategy

2 Implementation in society of AI technologies

3 Cutting-edge AI technology 「Explainable AI」

3 copy Copyright 2017 FUJITSU

Current status of AI and Fujitsu Lab research strategy

1

4 copy Copyright 2017 FUJITSU

History of Artificial Intelligence

1st AI Boom(SearchInference)

2nd AI Boom(Knowledge)

3rd AI Boom(Machine Learning)

(1956-1960s)

(1980s)

1st AI Winter(lsquo74-rsquo80) 2nd AI Winter(lsquo87-rsquo11)

The birth of AI【lsquo56】 (Dartmouth Conference)

The rush of big AI projects (Expert System)

Games with clear goals can be solved but not practical

Rule Base Lisp

Prolog

(2012-)

Far from the knowledge level of human experts

Deep learning surpassed conventional methods in image recognition【lsquo12】

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 3: Exploring the development and exploitation of cutting edge AI technology

2 copy Copyright 2017 FUJITSU

Agenda

1 Current status of AI and Fujitsu Lab research strategy

2 Implementation in society of AI technologies

3 Cutting-edge AI technology 「Explainable AI」

3 copy Copyright 2017 FUJITSU

Current status of AI and Fujitsu Lab research strategy

1

4 copy Copyright 2017 FUJITSU

History of Artificial Intelligence

1st AI Boom(SearchInference)

2nd AI Boom(Knowledge)

3rd AI Boom(Machine Learning)

(1956-1960s)

(1980s)

1st AI Winter(lsquo74-rsquo80) 2nd AI Winter(lsquo87-rsquo11)

The birth of AI【lsquo56】 (Dartmouth Conference)

The rush of big AI projects (Expert System)

Games with clear goals can be solved but not practical

Rule Base Lisp

Prolog

(2012-)

Far from the knowledge level of human experts

Deep learning surpassed conventional methods in image recognition【lsquo12】

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 4: Exploring the development and exploitation of cutting edge AI technology

3 copy Copyright 2017 FUJITSU

Current status of AI and Fujitsu Lab research strategy

1

4 copy Copyright 2017 FUJITSU

History of Artificial Intelligence

1st AI Boom(SearchInference)

2nd AI Boom(Knowledge)

3rd AI Boom(Machine Learning)

(1956-1960s)

(1980s)

1st AI Winter(lsquo74-rsquo80) 2nd AI Winter(lsquo87-rsquo11)

The birth of AI【lsquo56】 (Dartmouth Conference)

The rush of big AI projects (Expert System)

Games with clear goals can be solved but not practical

Rule Base Lisp

Prolog

(2012-)

Far from the knowledge level of human experts

Deep learning surpassed conventional methods in image recognition【lsquo12】

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 5: Exploring the development and exploitation of cutting edge AI technology

4 copy Copyright 2017 FUJITSU

History of Artificial Intelligence

1st AI Boom(SearchInference)

2nd AI Boom(Knowledge)

3rd AI Boom(Machine Learning)

(1956-1960s)

(1980s)

1st AI Winter(lsquo74-rsquo80) 2nd AI Winter(lsquo87-rsquo11)

The birth of AI【lsquo56】 (Dartmouth Conference)

The rush of big AI projects (Expert System)

Games with clear goals can be solved but not practical

Rule Base Lisp

Prolog

(2012-)

Far from the knowledge level of human experts

Deep learning surpassed conventional methods in image recognition【lsquo12】

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 6: Exploring the development and exploitation of cutting edge AI technology

5 copy Copyright 2017 FUJITSU

Background of the 3rd AI Boom

Evolution of computing Data driven Breakthrough algorithm

The latest GPU 120TFLOPS

Realizes 1000-smartphone computing by one chip

100 GB1 person Sequence data of whole human genome

220 GB1 minute Videos uploaded to Youtube around the world

2 TB1 driving Data generated by various sensors in an automated vehicle

200 TB1 fright Data generated by various sensors in an airplane flight

Data is the new oil of the 21st century

Brilliantly overcame the prejudice that useless

Gains visual and speech recognition functions that can exceed human for the first time with deep learning

2010 2000 1990

Wo

rd e

rro

r ra

te

100

5

10 Using DL

Source Microsoft

Evolution of speech recognition

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 7: Exploring the development and exploitation of cutting edge AI technology

6 copy Copyright 2017 FUJITSU

Expected AI Related Market

AI related market reaches 598 billion dollars worldwide in 2025 30 for enterprises 70 for consumers

CAGR 50 or more

Ente

rpri

ses

Con

sum

ers

partially edited by Fujitsu Labs

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 8: Exploring the development and exploitation of cutting edge AI technology

7 copy Copyright 2017 FUJITSU

Solving Practical Social Issues Through AI Current AI applications are still in the element technology level Needs to realize the AI effect and growth through social implementations

Current Applications

Games Speech recognition

translation

Image recognition

Applications required by real world

Mobility System Healthcare System

HR System Social Infrastructure System

Financial System

写真の差し替え by Okamoto

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 9: Exploring the development and exploitation of cutting edge AI technology

8 copy Copyright 2017 FUJITSU

Strategy to Grow AI Market

① Clearly shows the effect of AI in enterprise market ② Enhance AI market by understanding AIrsquos decision

Shows the effect of AI Understands AIrsquos decision

Collects classifies and organizes events

Understands events and predicts future

Executes by human or AI and feedback its results

Makes decisions and plans actions

Growing Cycle of AI

Visualization

Execution amp Feedback

Analysis amp Prediction

Decision Making

By repeating this cycle AIrsquos performance is demonstrated

AI collaborates smoothly with people and coexists with society

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 10: Exploring the development and exploitation of cutting edge AI technology

9 copy Copyright 2017 FUJITSU

Implementation in society of AI technologies

2

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 11: Exploring the development and exploitation of cutting edge AI technology

10 copy Copyright 2017 FUJITSU

Fujitsu AI Technology Brand

Agile amp intense

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 12: Exploring the development and exploitation of cutting edge AI technology

11 copy Copyright 2017 FUJITSU

Fujitsursquos AI Goals

To create AI that collaborates with people and is human centric

To create AI that continuously evolves

To provide AI that can be incorporated in our products and services then deployed

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 13: Exploring the development and exploitation of cutting edge AI technology

12 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 14: Exploring the development and exploitation of cutting edge AI technology

13 copy Copyright 2017 FUJITSU

AI Development Overview

AI platform

AI core technologies

Implementation in society

Applications

Software Acceleration of AI programs as data preprocessing parallel processing distributed processing and so on

Sensing and Recognition

Understanding of users intention emotion environment and situation

Knowledge Processing

Acquisition of knowledge and inference and problem solving

Learning Advancement of machine learning in order to expand application range

AGI Artificial General Intelligence

Service and Embedded Incorporating AI into equipment Development amp verification for applying social infrastructure systems

Governance and Social acceptability

Privacy and security protection of intellectual property rights Social acceptability

Eco-system Ecosystem for business technology and human resource development

Hardware Hardware for high-speed AI computing such as HPC DLU and DAU

Social infra- structure Mobility

Customer center

Logistics

Main- tenance

Manu- facturing

Fintech

Digital marketing

Food

agriculture Health

care

Office living

Explainable AI

Applications to solve social problems in various fields

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 15: Exploring the development and exploitation of cutting edge AI technology

14 copy Copyright 2017 FUJITSU

Extending the Scope of Deep Learning Breakthrough technology of machine learning that extracts knowledge from data Automates feature extraction a fundamental issue of machine learning

Time-series data Graph data Image Voice Text

Vehicle Recognition

Company A White male Age 30

Cloth Gray

Backpack Pink

Person Recognition

Handwriting Recognition

Practical application

Fujitsu Laboratoriesrsquo scope Conventional scope

Original (201602)

Leveraged by topological data analysis

Original (201610)

Based on tensor expression

Learning Technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 16: Exploring the development and exploitation of cutting edge AI technology

15 copy Copyright 2017 FUJITSU

1 Blade Inspection Image Data

Purpose

Technology

Outcome

Develop ldquoNon-destructive testingrdquo to assist human operators in identifying potential defects

Detection with a combination of Deep Learning Image and Signal processing

Blades can now be checked 75 faster taking just 15 hours

To be put in production in Denmark and UK starting Autumn 2017

demonstration

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 17: Exploring the development and exploitation of cutting edge AI technology

16 copy Copyright 2017 FUJITSU

2 Bridge Inspection

Automatic detection of internal damage for bridges

Topological data analysis (TDA) for time-series data from sensor

Contribution to the optimization of bridge maintenance and management tasks

Vibration data External sensor Converting vibration data into figures

Converting figures into numerical values

degree of abnormality

The degree of abnormality and the degree of change

degree of change TDA

Chaos theory DL

intact damaged

wheel load

concrete slab

external sensor

Purpose

Technology

Outcome

Time-series Data

demonstration

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 18: Exploring the development and exploitation of cutting edge AI technology

17 copy Copyright 2017 FUJITSU

Deep Tensor

FUJITSUrsquos proprietary deep learning technology that can learn graph data representing relationships in the real world

Various relationships

SNS (personal relations)

Compound (element

combination)

Business connections

Existing error back propagation method

Extended error back propagation method

Tensor representation

Graph data

Deep Tensor

Business growth forecast

Fintech

Virtual screening

IT drug development

Malware invasion detection

Cyber attack

hellip

Neural network

Learning both neural network and tensor transformation

Communication relations

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 19: Exploring the development and exploitation of cutting edge AI technology

18 copy Copyright 2017 FUJITSU

3 Security(Malware Attack Detection)

Shorten the detection time several days by experts to a few minutes

Detect the behavior of a malware which is difficult for the expert to find out

the example of graph data that only the new method using Deep Tensor can detect the attack

The number of nodes 223

The number of nodes 249

The number of nodes 343

通信パケット長

起動コマンド種類属性

To shorten the malware attack detection time

Detection of several patterns of attacks by multiple tensor technology Conversion of various types log data to graph data and multi tensor form

DeepTensor

Learn the multiple patterns of attacks

Network log

SNS

Cheical compound

Trading histories

Graph Data

Tensor form +

Tensor form +

Tensor form

Mu

ltple Ten

sor Tech

Deep Learning

Multi Tensor

Con

vert Gra

ph

Da

ta

Graph Data

demonstration

Source IP address

Destination IP address

Source port No

Destination port No

Packet length

Command attribute

Purpose

Technology

Outcome

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 20: Exploring the development and exploitation of cutting edge AI technology

19 copy Copyright 2017 FUJITSU

Modeling Inference Optimization Matching Knowledge Processing and Decision amp Support

Modeling and Inference Modeling and Optimization Matching and Modeling

Heat Stress Level Estimation Ship Fuel Reduction Daycare Center Matching

①Accumulated heat stress status

②Heat stress level decision model

Specialistsrsquo knowledge

Workenvironmental states data Heat

stress level

Machine Learning

Heat stress accumulation status decision algorithm

Ship operational data

Voyage data Engine log climate

Performance model at sea

Learning a statistical model ca

rgo

wei

ght

win

d di

rect

ion

wav

e di

rect

ion

wav

e h

eigh

t

win

d sp

eed

Ship Performance

curr

ent

spee

d curr

ent

dire

ctio

n

agin

g

ship

de

sign

High-dimensional statistical analysis

Accurate

Mathematical Optimization

Prediction

Optimization

Fuel Efficient Routing

Maintenance Timing

Day care centers amp Applicant matching

under applicantsrsquo

requirements

Fair amp efficient

Mathematical model of the relationships of complex requirements

Game theoretical Algorithm

best matching

Risk

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 21: Exploring the development and exploitation of cutting edge AI technology

20 copy Copyright 2017 FUJITSU

4 Heat Stress Level Estimation

Safety management of workers heat stress level

Learning based on the specialists judgment using ambient temperature humidity the pulse rate and the momentum

Realize the safety management of workers under hot weather

The accuracy of heat stress risk level detection is more than 94

Started the business from this summer

The heat stress level is estimated and the alarm is

notified

Pulse rate in working

Pulse rate in a rest

Body heat environmental index

Vital sign sensing band

Specialistsrsquo findings

International standard and index related to heat stress

Heat stress accumulation level dicision algorithm

①Accumulated heat stress

Momentum

①Accumulated heat stress based on work and environmental states ②Presumption of heat stress level based on specialistsrsquo findings

②dicision model

Heat stress level

Risk is high

Risk is middle

Risk is low

Safety

Modeling and Inference

Purpose

Technology

Outcome

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 22: Exploring the development and exploitation of cutting edge AI technology

21 copy Copyright 2017 FUJITSU

5 Ship Fuel Reduction Solve the big issue of ship fuel consumption cost reaches 3 billion dollars per major shipping company

Generate statistical model with every factor learnt from operational data Visualize actual performance at see and select fuel efficient routing for each ship

Fuel consumption reduction is about 5 and estimation error is within 2

Time [sec]

Ship

sp

eed

[kn

ot]

Estimated Measured

Estimating ship performance accurately

Generating statistical model

Collecting operational data

Selecting fuel efficient routing for each ship

cargo weight

wind direction

wave direction

wave height

wind speed

Ship

p

erforman

ce

current speed current

direction

aging

ship design

Hig

h-d

ime

nsio

na

l sta

tistical a

na

lysis

Maintenance Timing Optimization Performance degradation estimation

Fuel efficiency

Optimum maintenance timing

age

VDR=Voyage Data Recorder

demonstration

VDR Data

Engine Log data

Modeling and Optimization

Purpose

Technology

Outcome

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 23: Exploring the development and exploitation of cutting edge AI technology

22 copy Copyright 2017 FUJITSU

Fair and fast matching under applicantsrsquo complex requirements

Mathematical model of the relationships of complex requirements which rapidly calculates the best matching based on game theoretical modeling

Time for admission screening for about 8000 children is reduced from several days to seconds

To be put in production as an optional service for ldquoMICJET MISALIO Child-Rearing Supportrdquo during fiscal 2017

6 Daycare Center Matching Matching and Modeling

Capacity2

Capacity2

Capacity2

1

Daycare Applicant

Child 1 Priority 1

Child 2 Priority 2

Child 3 Priority 3

Child 4 Priority 4

Meets the Rule

Assignment 1 Daycare A Daycare A Daycare B Daycare B

Assignment 2 Daycare A Daycare B Daycare A Daycare B

Assignment 3 Daycare A Daycare B Daycare B Daycare A

Assignment 4 Daycare B Daycare A Daycare A Daycare B

Assignment 5 Daycare B Daycare A Daycare B Daycare A

Assignment 6 Daycare B Daycare B Daycare A Daycare A

Sibling

Sibling

Purpose

Technology

Outcome

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 24: Exploring the development and exploitation of cutting edge AI technology

23 copy Copyright 2017 FUJITSU

Other Applications

Chatbot for call center

Voice automatic translation Business support

Labor-saving in call centers by 247 service by auto-response

Supporting of communication with foreigners

Automatic classification

AI

Business instruction

Meeting offer

Claim

Just information

Automatic detection of intention of mail sender and summary of contents

Cloud Audio signal

Microphone Speaker

Model enhance

Automatic model adjustment according to noise environment

Edge

Technology for structural semantics extraction by relations of words and high accuracy FAQ search

Learning Voice recognition Translation Voice synthesis

user

Automatic Dialog through messenger

247 Service

Digital Agent for Call Center

Natural Dialog Knowhow

Chatbot FAQ Search

Zinrai Platform

Agents

Supporting office workers for troublesome work such as schedule input

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 25: Exploring the development and exploitation of cutting edge AI technology

24 copy Copyright 2017 FUJITSU

Cutting-edge AI technology 「Explainable AI」

3

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 26: Exploring the development and exploitation of cutting edge AI technology

25 copy Copyright 2017 FUJITSU

Outline

Why Explainable AI ldquoBlack boxrdquo issue must be solved in order to expand social applications of AI

Key technologies to realize Explainable AI Deep Tensor and knowledge graphs

Technological points Explaining ldquoreasonsrdquo and ldquobasisrdquo for conclusions are identified

Specific example Genomic medicine field

Future works

New technology ldquoExplainable AIrdquo

Development of brand new AI that can explain the reasons and basis for AI-generated conclusions

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 27: Exploring the development and exploitation of cutting edge AI technology

26 copy Copyright 2017 FUJITSU

Why Explainable AI

Deep learning is a breakthrough technology for AI To enhance the field which utilize deep learning we solved itrsquos ldquoblack box problemrdquo

Explainable AI 1Rely 2Understand 3Control

Can make new findings

Can fulfill accountability Can improve AI

Human

1 2 3

Empower

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 28: Exploring the development and exploitation of cutting edge AI technology

27 copy Copyright 2017 FUJITSU

Key Technologies to Realize Explainable AI

Deep learning technology that can learn graph data

Deep Tensor

Graph data representing relationships in the real world

Knowledge Graph

LOD

Web

Tensor representation (standardized expression)

Graph data Neural network

Existing error back propagation method

Extended error back propagation method

Malware detection

Cyber attack

Learning both neural network and tensor transformation

Virtual screening

IT drug development

Gene

Mutation

PTPN11

Compound

Gene

Systematic knowledge

(utilized for a role to play)

Onset

Disease

Part of

Protein Expression

Drug class

Target Effect

Disease LEOPARD syndrome

Activation

Part of

Mutation

NM_0028344c174CgtG

Public DB Articles

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 29: Exploring the development and exploitation of cutting edge AI technology

28 copy Copyright 2017 FUJITSU

Technological Points

Inference factor Input Output

Basis formation

Output both findings and reasons (inference factors)

Findings

Knowledge Graph

Inference factor identification

Knowledge graph forms basis from input to findings

1 Explaining the reasons for findings Deep Tensor

2 Explaining the basis for findings

b d a c e f g

Deep Tensor + knowledge graph -gt Reasons and basis of AI judgments

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 30: Exploring the development and exploitation of cutting edge AI technology

29 copy Copyright 2017 FUJITSU

By understanding the cause of the disease largely reduce the time for analysis diagnosis treatment orientation by doctors

Application to Genomic Medicine

A diagnosis (drawing blood)

Gene analysis (next-generation sequencer)

The presentation of the therapeutic drug to personality (genetic

abnormality)

Analyzing and reporting

Ref Hokkaido Univ Hospital(httpwwwhuhphokudaiacjphotnewsdetail00001144html)

Identification of the cause gene medicine investigational search

judgment of the treatment

Web Medical articles Bio DB hellip

Learning from 180000 cases of disease mutation

data

Deep Tensor

Inference of disease

PubMed Medical articles

17million

Bio DB 3million records

b d a c e f

More than 10 billion knowledge built from 17 million medical

articles etc

Causative gene

Gene Ontology

Disease Ontology hellip

Forming medically-proven basis from mutation to

disease

Gene mutation

Gene mutation

demonstration

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 31: Exploring the development and exploitation of cutting edge AI technology

30 copy Copyright 2017 FUJITSU

Example of Basis Paths

Composing a path to be the basis from input ldquomutationrdquo to output ldquodiseaserdquo

Input node (gene mutation)

Output node (disease)

Inference factor of Deep Tensor (partial graph having impact on output)

Node complemented by knowledge graph

Input mutation generates abnormality in gene HGNC795 (ATM) and causes tachycardia (a type of ventricular tachycardia)

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 32: Exploring the development and exploitation of cutting edge AI technology

31 copy Copyright 2017 FUJITSU

Future Works

Aim for collaboration of humans and AI in various fields

Utilization of national projects and industry-academia collaboration

Health care

Co-creation with financial institutions

Finance

Implementation within a company

Corporate

Inferring disease from gene mutation

Providing medically reasoned explanations

Forecasting corporate growth from business performance and

economic indicators

Explaining strengths and weaknesses of companies

Detecting employeesrsquo health change from their activity

data

Explaining factors for health maintenance in work

environment

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 33: Exploring the development and exploitation of cutting edge AI technology

32 copy Copyright 2017 FUJITSU

A safer more prosperous and sustainable world

Human Centric Intelligent Society

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Page 34: Exploring the development and exploitation of cutting edge AI technology

33 copy Copyright 2017 FUJITSU

Fujitsu Sans Light ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacutethorn

yumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789

notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucircuumlyacute

thornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl

Fujitsu Sans Medium ndash abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ

0123456789 notrdquopound$^amp()_+-=[]rsquo~ltgt| copyuml~iexclcentcurrenyenbrvbarsectumlordflaquoraquonot-

regmacrdegplusmnsup2sup3microparamiddotcedilsup1ordmfrac14frac12frac34iquestAgraveAacuteAcircAtildeAumlAringCcedilEgraveAEligEacuteEcircEumlIgraveIacuteIcircIumlETHNtildeOgraveOacuteOcircOtildeOumltimesOslashUgraveUacuteUcircUumlYacuteTHORNszligagraveaacuteacircatildeaumlaringaeligccedilegraveeacuteecirceumligraveiacuteicirciumlethntildeograveoacuteocircotildeoumldivideoslashugraveuacuteucirc

uumlyacutethornyumlĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝-‒ndashmdash―lsquorsquosbquoldquordquobdquodaggerDaggerbullhellippermillsaquorsaquoolinefrasl⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉eurotradeΩrarrpart∆prodsumminusradicinfinintasympnelegesdotlozfifl