draup braindesk next generation technology spending ...next...the report provides insights on the...

Source : DRAUP

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1

May 2015

Next Generation Technology Spending Patterns in AISEPTEMBER 2017

Source : DRAUP

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Artificial Intelligence is transitioning into the mainstream industry at warp speed

~ 5 MnPotential jobs to be impacted in the US by 2025

$25 BnEstimated revenue from AI products & services in 2025, to grow exponentially at a CAGR of 61%, from existing $0.7 Bn as of financial year ending 2016

$12 Bn Deep Learning market cap in 2025, holding the largest revenue slice. Estimated to grow at a CAGR of 58% from existing market cap of $0.3 Bn

~ 10,000Global AI Start-ups expected by 2025. Predicted to increase 5 times from the current ~2,200 start-ups in 2016

Note: DRAUP - The platform tracks engineering insights in the AI ecosystem using our proprietary machine learning algorithms along with human curation. The platform is updated in real time and analysis is updated on a quarterly basis

Source : DRAUP

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3

DRAUP followed a rigorous and structured research approach to analyze nearly 500 organisations currently working on Artificial Intelligence (AI) technology

Objective

Key questions answered

DRAUP analyzed

5 Industries including Automotive, Semiconductor, Software/Internet

~50 MSA locations across regions such as US, Canada, MEA (Israel), Europe and

Asia Pacific

~500 Artificial Intelligence (AI)technology Spenders shortlisted

~2,200 Start-ups working across AI and related technology areas

Ø To understand the current state of AI Industry Landscape consisting of global organisations: Tech Mafias, Start-ups and other G-500 players across diverse industries

Ø Who are the Drivers, Leaders and Lagers in the AI industry landscape ?

Ø What has been the technology spending patterns by these players across the AI stack ?

Ø What are the strategies adopted by top AI players to accelerate AI innovations ?

Ø What are the global technology hotspots for AI innovations ?

Ø How does the AI start-up landscape look like and what are the industry adoption patterns for AI applications ?

Ø How should global engineering organisations develop AI capabilities ?

Over 30 global stakeholders were interviewed as part of the analysis

Note: DRAUP has a database of nearly 500 engineering organisations and nearly 100,000 start-ups working across AI, IoT, Bigdata technologies in Automotive, Software/ Internet and other Hi-Tech industries. For validation of data multiple government reports have been referred such as OECD, World bank R&D Data, UNESCO Institute for Statistics, International Labour Organisation (ILO), US Energy Information Administration (EIA) and others

Source : DRAUP

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4

Who should read this?

R&D Decision Makers

Ø The study provides actionable insights for executives and decision makers to support their global R&D initiatives in the emerging AI technology segments

Ø The study can be leveraged to proactively track peer organisations’ current AI capability and future technology outlook

Ø Useful findings also include assessment of ecosystem for collaboration opportunities with peers, technology providers and new age emerging players in the AI technology segments

Sales executives of Engineering

ServiceProviders

Ø The DRAUP platform and our dedicated advisory expertise which extend beyond mere human curations, can be leveraged to proactively track and understand the recent advancements in the AI Ecosystem

Ø Sales decision makers, executives and organisational leaders of Technology Service Providers (TSPs) can leverage this to capitalize on C –level opportunities. The report provides insights on the advancements in AI, current trends, spending pattern of the companies, critical investment areas by companies etc.

Ø This could be used by the sales teams to effectively target the prospects. The study provides actionable insights for TSPs in the emerging product innovation areas of the AI Sub-Segments

Source : DRAUP

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5

AI Landscape: Start-ups, Tech Mafias and other G500 players01

Recommendations to build AI Capability 02

Case Study: India AI Ecosystem: Global R&D Spenders, Service Providers & Start-ups03

ü What have been the AI technology advancements during last 5 years ?

ü What are the type of players accelerating AI innovations?

ü What are their focus spend areas across the AI stack and industry use-cases?

ü Which are the global AI technology hotspots ?

AGENDA

Source : DRAUP

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6

AI has evolved rapidly in the last few years, enabled through rapid technology innovations

Introduction of Turing test

First AI programto play Tic Tac Toe

IBM’s Deep Blue defeats Gary Kasparov

DeepMind’s self-taught AI can beat human players at 29 of 49 Atari games

Deep learning start-up Gamalon claims to be 100 times more efficient than DeepMind

1950 1960 1997 2011 2015 2016 2017

Computational Power

Data Platforms

Better Algorithms

Cost of Computing

12,000 Core GPU

$0.05 Per million transistors

BIGDATA PLATFORMS -HDFS

DEEP LEARNING -Convoluted NeuralNetwork

512 Core GPU

$200 Per million transistors

RDBMS

LOGIC THEOREMS -Single layer learning, Perceptron, Adaline

Watson became Jeopardy Champion

Note: The list above illustrates landmark events in the AI EcosystemThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Source : DRAUP

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“That’s a strange move. I thought it was a mistake.”– Lee Sedol

• 10360 possible moves• Monte Carlo tree search & Q Learning• Statistical, learned and general purpose• Learned from 30 million moves

• 1023 Trillion Possible Outcome• Brute Force Algorithm• Symbolic, hand crafted and domain specific• 700,000 Grandmaster chess games

Deep Blue AlphaGo

And is able to beat human champions in complex board games..

It may be a hundred years before a computer beats humans at Go — maybe even longer!!

-AI Experts in 1997, NY times

Source : DRAUP

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AI has moved on from games to the real world, disrupting all industry verticals…

NLP platform

Alexa

BFSI

Healthcare

Pote

ntia

l to

Dis

rupt

1

AI Maturity 2

Retail

Predictive diabetes

management

AI-based Robo advisory service

Enterprise Software

Semicon

Consumer Electronics Microsoft Cortana and Intelligent

Cloud

Machine Learning Enabled

Hardware

NASA software to enable damaged aircrafts, find a safe

landing spot.

Recommendation based on

photographs

Autonomous driving

Auto

Aerospace

Consumer Software

Machine Learning

enabled

Advertising

Note: 1- Analyzed basis data maturity, software penetration, regulatory restrictions across the value chain representing disruption potential over next 5 years

2 – Analyzed basis current investments ( talent + acquisition) for all players

The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Source : DRAUP

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9

MANUFACTURING FINANCIINGOWNERSHIPEXPERIENCE

DESIGN & DEVELOPMENT

AI-Designed Car

Developing the first AI designed car

Partnered with the robotics and intelligent systems group to drive innovations in cognitive systems in factories

Intelligent Systems

PRODUCT FEATURES

Set up a $25 Mn research center in collaboration with MIT for autonomous vehicle technologies

Autonomous Vehicles

§ AI-based Designs§ Simulated Testing

Baidu invested in ZestFinance, a start-up that uses machine learning to develop a credit scoring platform

Credit Scoring

§ ADAS§ Connected Car§ Speech Recognition

§ Intelligent Production Line

§ Integrated Systems

§ Ride Sharing§ On-Demand Transport

§ Credit Scoring§ Fraud Detection§ Predictive Modelling

Tesla’s Autopilot can, in real-time, learn the daily routes taken by its users

Ride-Sharing

$721 MnTotal Funding

2011Average Founding year

192Disruptors

Automotive

And adding value across the industry value chain : Case Study- Automotive

Note: The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Source : DRAUP

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AI innovations are dominated by Tech Mafias and Start-ups (1/2)

Average AI Headcount ~5,600

AI Talent as a percentage of

R&D talent

~177 ~330

16% 70% 0.6%

25$

Bn

Tech Mafia

R&D Spend

Google, Facebook, Microsoft, Apple and Amazon

DriversAI Start-ups

15$

Bn

Start-ups: Global AI start-ups have received financing from corporates, VCs and other angels

Funding

Leaders

Top 500 Global R&D Spenders (Not including Tech Mafias)

3$

Bn

G500

R&D Spend

Laggards


Source : DRAUP

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11

Hardware

APPLICATIONS

PLATFORMS

INFRASTRUCTURE

MACHINE INTELLIGENCE NLP COMPUTER VISION

DEEP LEARNING

ADAS

GESTURE CONTROL

Enterprise Software Assistants

Productivity

FinTech

AdTech

Data Platforms

HealthTech

Auto

Dominated by the start-ups who build verticalized applications for various use cases.

Applications – The Start-up Zone

Focused efforts on building platforms that can then be leveraged by the ecosystem.

Platforms – Tech Mafia Playground

The AI focused companies can be found providing the infrastructure that enables the rest of the landscape.

Infrastructure – G500 Domination

Intensity

Start-ups G500Tech Mafia

Tech Mafias are building a robust platform infrastructure to accelerate the application ecosystem which is dominated by the AI start-ups


Source : DRAUP

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Tech Mafias: Each of the Tech Mafias have developed strong platform capability in the fields of Chatbots, Deep Learning and Computer Vision; AI applications is their emerging focus segment

Note : The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Automotive Healthcare Consumer AI Platforms Infrastructure

Google WaymoVerily –for Diagnosis

DeepMind for HealthcareGoogle X nanoparticle research Google Home

Android Wear Smartwatch

Google Now

Project Jacquard AI Robot-GoogleXGoogle Prediction API

TensorflowDeepMind Google for Work

Project Titan

Wearables for Health monitoring

Siri controlled home kitApple Smartwatch

SiriiPhone iOS 10 image recognition

Spotlight for images & text

Microsoft –Volvo Self driving Cortana for Healthcare

Kinect

CortanaHololens SwiftKey

Microsoft Graph –Sales lead scoring

DSSTNEAzure ML

Alexa AWS MLRecommender systems

CNTK

OculusFacebook M

FAIRFacial recognitionFacebook Deep TextWit.ai

Total AI Spend $25 Bn

AI Talent 30K

Acquisition $10 Bn

Patents 300+

Source : DRAUP

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Tech Mafias: Tech mafias own ~45% of global AI talent leveraged from hotspots beyondtheir HQ locations

9,300

19,600

Seattle Area

Bay Area

2,100

3,200

Boston

New York

9502,700

Bangalore

1,600

• Traditional Hubs for Engineering for the Tech Mafia - Machinelearning to NLP & Computer vision.

• Driverless Cars, Drones, Data Science, Cyber Security are the hot areas

West Coast of USA East Coast of USA Western Europe & Israel Indian Hi-Tech Cities

460Singapore

660BeijingIsrael

Hyderabad

• Top universities like CMU & MIT are deeply focused on Artificial Intelligence research;

• EU’s Human Brain Project is spending close to 1 Bn euros on AI over the next decade.

• OEMs like Renault, Volkswagen are partnering with Autonomous start-ups like Mobileye

• IBM set up its Watsonunit in India in 2012 to work for Healthcare and BFSI clients in US.

• Baidu is investing in deep speech for voice-based searches that leverage speech recognition;

910

Spain

4,100

UK

2,000

France

3,300

Germany

Hong Kong & Singapore

X ER&D Workforce in AI

950Netherlands

TECH MAFIA HOTSPOT UNIVERSITY RESEARCH AUTO OEMs AI FOCUS OVERSEAS FOCUS CHINESE INTERNET AI DRIVERS

45%

55%

Tech Mafias

Rest

Tech Mafias own 45%of global AI talent

Source: GEIPNote : The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Source : DRAUP

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14

XUser Base on GITHUB

Computer Vision

KINDRED Robotics

Project Malmo

Tensorflow

DSSTNE

35K

Facebook for Torch Swift-AI

CNTK

DSSTNE is designed to support problems with sparse data. 3KAI research built on top of the

game Minecraft.2K

Significantly faster than the default Torch and allow users to train larger neural nets

A unified deep-learning toolkit that describes neural networks as a series of computational steps

6K

Swift AI is a high-performance AI and machine learning library

Open sourcing their APIs allows the Tech Mafia to

democratize innovation

1K 1K

Computer Vision for refrigerators

Most popular Open Source AI Library. User base has grown tenfold since its release in Nov. 2015.

Makoto Koike uses TensorFlow to sort Cucumbers

Cornel University project on Cyber-Security

Projects based on Platforms

Tech Mafias: And opening their innovations for others to build on

Note: The list above may not be exhaustive . We shortlisted major open source initiatives as of Sept, 2017 which have been accelerating growth of the AI ecosystemThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Source : DRAUP

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“Success in creating AI would be the biggest event in human history. It might also be the last, unless we learn how to avoid the risks.” - Stephen Hawking

Trending fake news articles

Facebook

7 reported accidents (1 fatal) since April 2016

Tesla

Google

DeepMind failed at describing dumbbells

Microsoft’s Tay became a racist bot

Microsoft

Note: The graphics above indicate major AI failure events for large technology giantsThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017

Tech Mafias: Public failures notwithstanding

Source : DRAUP

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Start-ups are catching up, capitalising on the AI application segment across diverse industries

AI Talent 45K Patents 300+Total Funding $15 BnTotal

Start-ups 2,232

Consumer

Infrastructure

AI Platforms

Automotive

Healthcare

ZooxDrive.ai Nutonomy MobilEye ZenDrive

iCarbonX Lumiata Butterfly Zymergen Imagen Technologies

$ 721 Mn in investments

Total Funding

Api.ai x.aiAnki Jibo MagistoUgobe LukaGluru Emotech Sherpa

Attivio DiffbotTrifacta SentenAI SigOpt

AffectivaH20.ai Sentient.aiVicarious Systems Ayasdi


$ 1,032 Mn in investments


$ 1,217 Mn in investments

Top Start-ups

Note: The start-up list above is non exhaustiveThe above analysis is based on the DRAUP’s proprietary start-ups, updated as on Sept, 2017

Source : DRAUP

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Start-ups: Ten fold increase in AI start-up funding in the last five years

Quarterly funding trend (2013-16 YTD)

Q1, 2012

Q1, 2013

Q1, 2014

Q1, 2015

Q1, 2016

$94 Mn$137 Mn

$253 Mn

$121 Mn

$302 Mn

$552 Mn

$926 Mn $901 Mn

$602 Mn

$1,049 Mn

Raises $100Mn for Deep learning based ultrasound

Google acquires DeepMind for$500Mn

Raises $65Mn for ML-based threat detection

Q1, 2011

Focused on reverse engineering the neocortex; raised series A

AI based unicorns have emerged since July, 20165

Billion Dollar Valuation Line

Zoox

Valuation - $1.85 BnHealthcare

Valuation - $1.55 BnAutomotive

Valuation - $1.5 BnEnterprise

Valuation - $1 BnConsumer

Valuation - $1 BnHealthcare

Valu

atio

n

Age of Start-up

Zymergen

Note: No inflation assumed (all values in 2017 USD). Funding details are updated with nearly 70% accuracyThe above analysis is based on the DRAUP’s proprietary start-ups, updated as on Sept, 2017

Source : DRAUP

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Start-ups: DRAUP’s six lenses were used to examine the AI start-up landscape

Global Ecosystem Maturity

Congregation of AI start-ups in enabling ecosystems across the world for diverse

application areas

Use Case Adoption

Patterns on the top use cases adopted by highly scalable general purpose AI platforms

Value Chain Aggregation

Understand collaboration points with AI start-ups across the industry value chain

Absorption Pulse

Examine the focus areas and key drivers for M&A and investment strategies of prominent

incumbents

Deadpool Intensity

Analyse constraints for AI start-ups to scale and gain perspective on market conditions

Skillset Transformation

Adapt to the changing human capital needs to drive an AI-first business strategy

Source : DRAUP

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Start-ups: Autonomous cars and infotainment solutions are gaining investment traction in the Auto value chain

ADAS Driver Safety Autonomous carsConnected CarInfotainment

$24 Mn $10 Mn

$80 Mn

$18 Mn

$253 Mn

Fund

ing

in e

ach

of a

utom

otiv

e Va

lue

Cha

in S

egm

ent

GE acquired Cruise Automation and piloted a fleet of Chevrolet Bolt EVs in San FranciscoAutonomous Vehicles

Autonomous Vehicles

Acquired self driving technology provider Ottomatika following joint projects for CES

ADAS

Acquired the ML & Deep Learning solutions for image and video processing built by Israeli company SAIPS

Toyota acqui-hired the 16 member Jaybridge Robotics to be a part of its research institute focused on AI & Autonomous vehiclesAutonomous Vehicles

OEMs and Tier 1s are acquiring AI start-ups to bolster their value chain

Impact Assessment of AI start-ups in the automotive value chain

HighLowAI Tech Adoption

Machine Learning

NLP

Computer Vision

DeepLeaning

AI T

ECH

NO

LOG

Y

Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;SWARM Disruption Framework for Start-up Analysis

Source : DRAUP

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20

Start-ups: AI start-ups are bolstering customer engagement and marketing in the retail value chain, applying different business models

Security & Surveillance

Supply Chain Management

In-Store Analytics

Customer Engagement

Multi-Channel Marketing

$ 24 Mn

$ 90 Mn

$159 Mn

$100 Mn

$105 Mn

Tota

l Fun

ding

of V

alue

Cha

in

Segm

ent

Predictive Intelligence Platform to reduce fraud and improve customer targeting

Customer Engagement

Supply Chain Management

A Machine learning platform that solves out-of-stock and overstocking problems

Top retailers are acquiring AI start-ups to bolster their value chain

Impact Assessment of AI start-ups in the retail value chain

HighLowAI Tech Adoption

Subscription Model

Licensing Model

One Time Payment

Affiliate Fees

Bus

ines

s M

odel

s


Source : DRAUP

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Start-ups: Financial Services, Marketing and Healthcare are the first avenues for core AI start-ups that can be applied to diverse use cases

Total Funding

Patents

Vicarious Systems DataRobot

$135.8 Mn $97.9Mn $75.6 Mn $67Mn $57.4 Mn

8 16 9 - 3

Total Funding

Patents

$48Mn $37.4 Mn $34.2 Mn $30.6 Mn $30Mn

4 29 42 - 2

Sentiment

Analysis

Personal-

ization

Customer

Engagement

Digital

Marketing

Marketing

Fraud

Detection

Insurance

claims

Algorithmic

Trading

Risk

Modelling

Finance

Patient

Monitoring

Drug

Delivery

Population

Health

Precision

Medicine

Clinical

Variance

Healthcare

Use Case Adoption Index

LowHigh

Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;

SWARM Disruption Framework for Start-up Analysis

Source : DRAUP

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Start-ups: US is the dominant innovation hotbed for AI start-ups led by the Bay Area Ecosystem

2,322Start-ups $14.74 Bn

Global AI start-up distribution

86

CanadaNetherlands

25

26

Australia

Brazil

18

France

43

25 Singapore

Hong Kong12

Total FundingNumber of Start-ups

$11.5 Bn

$0.6Bn

$0.03 Bn$0.5 Bn

$0.6 Bn

$0.1 Bn$0.1 Bn

$0.1 Bn

APPLICATIONS-FINTECH, HEALTHCARE

APPLICATIONS – HRTECH, HEALTHCAREAPPLICATIONS – AUTO, FINTECH,

RETAIL

PLATFORMS- DEEP LEARNING, VISION

ENABLERS –BIG DATA PLATFORMS

Vision based advanced

assistance system

AI based consumer

robotics start-up

Massively scaled deep

learning

ML based threat

detection

ML based

recruitment solution

ML for retail

ML for personalised

healthcare

NLP API

Data cataloging

and cleaning

PLATFORMS- DEEP LEARNING, NLP

Note : Coverage may be limited in China and other South East Asian counties

The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;


Source : DRAUP

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Start-ups: Corporate acquisitions and investment strategies indicate the direction the industry is moving in

6

dealsBuild New

Products

Bolster Technology

Stack

23

deals

$625Mn

Google

DeepMind

Enter New

Markets

10

deals$3,200Mn

Google

Nest

Facebook

Wit.ai

Not

Disclosed

Acquihire

Talent

6

deals

Intel

Indisys

Not

Disclosed

Technology and market expansion are primary drivers for M&A

Amazon Apple Facebook MicrosoftGoogle

Acquisition Year

Acq

uire

eM

atur

ity

Dot Com Era Smartphone Era Cognitive Era

2004 2010 2016

1

2

3

4

5Bulk of the acquisitions by Microsoft

and Google to boost their Search Tech.

MS and Apple begin

work on Gesture Control

devices.

The Tech Mafia investing

heavily in AI enablement

platforms Googlebegin work

on Maps

NLP VisionML NLP Vision ML NLP Vision Robotics

Indicates Average

Salesforce Intel Oracle IBM GE

Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;


Source : DRAUP

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24

0 2 4 6 8 10 12Age of Start-up (In years)

Dead pool

Scal

e (B

ased

on

Inve

stm

ents

, Hea

dcou

nt G

row

th a

nd C

usto

mer

Tra

ctio

n)Start-ups: Examining the dead pool of AI start-ups reveals key challenges to scale

LegendApplication Companies

Platform Companies

Infrastructure Companies

Consumer Enterprise Industry

ML NLP Vision

Data Platform Hardware

Major factors that have prevented AI start-ups from crossing the value chasm

RegulatoryRestrictions

Autonomous car start-ups have faced problems with regulatory authorities to proactively demonstrate

Business Model

Consumer Application start-ups managed to gain user traction and customer growth but struggled to find a scalable revenue model

Market Definition

Core AI based start-ups struggled to define the right use case for their technology and proof of concept

Tech Roadmap

The development of the AI platform plateaued after a few years in operation

Series A

Seed. $3.1M

Series C

Series C

Comma.ai

*700 Start-ups plotted


Source : DRAUP

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25

Start-ups: AI start-ups are impacting roles in the engineering organizations

R&D

IT

Sales & BD

Product Management

Others

29.7% 33%

21.5% 7%

30% 38%

5.8% 4%

13% 17%

HC% of 10+ yrs exp

Hardware

Software

Architect

Analytics

UI/UX

24%

23%

5%

6%

14%

73%

50%

92%

55%

52%

HC% of 10+ yrs exp

12%

18%

4%

12%

10%

80%

30%

90%

36%

40%

ML/NLP

Release

QA

11%

8%

11%

42%

27%

73%

43%

-

2%

26%

-

-

Engineering Talent Hired From


(Head-count split across business functions)

Source : DRAUP

26

26

Google

MicrosoftAmazon

Facebook

Apple

IBM

BoschVolkswagen

Intel

Oracle

Cisco

Foxconn

SAP

Airbus

MobileyeSentient

ZooxDatarobot

X.ai

Top R&D spenders are lagging behind

Plan to release Xeon Phi processor line for AI applications-~$400Mn investments

Leveraging ML for network threat products – Cognitive Threat Analytics

Invested $5Bn in building an AI powered Giga factory.

$1Bn investment to establish the Toyota Research Institute for AI

$500Mn investment in a 200 member AI R&D lab in Silicon Valley

Note : 1 : Investment in AI in terms of talent & acquisition or Funding raised ( for start-up);2 : Focus on emerging technologies vs Older algorithms, Focus on Ecosystem creation and Platform adoption/maturity;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017

AI Focus1

Futu

re R

eadi

ness

2

Start-ups G500Tech Mafia

G500

Start-ups

Tech Mafia

Size of the bubble indicates R&D spend

Source : DRAUP

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27

AGENDA

ü How to build AI capability in the core business value chain?

ü How to build AI platform and data strategy to own key AI capabilities?

ü How to leverage collaborative AI innovation with the Ecosystem?

ü How to leverage global emerging AI hotspots?




Source : DRAUP

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28

Organisations can accelerate AI innovations through four simple steps

Identify Business Case1Build a Data Ecosystem2Collaborate with the Ecosystem

Leverage newer talent hotspots43

4-Talent Hotspots

2-Data Ecosystem

3-EcosystemCollaboration

1-Use Cases

AI Puzzle

Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017

Source : DRAUP

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29

Identify and prioritize AI’s role

DATA COMPLEXITY

APPLICATION COMPLEXITY

Wide range of interconnected activities

Well-defined rules, procedures and criteria

Complete Autonomy

Augment Humans

Reliant on individual expertise and experience

Original, innovative work

Surgical RobotsChatbots

Echo, the home control device

Automated meetings scheduler

Image Search

Auto- Recommendations

Automated Factories

Robo- Advisory

Enterprise security through AI

Autonomous Car

Dee

p Le

arni

ngR

ule

Base

d En

gine

AI bot designed car

AI-based website design platform

1


Source : DRAUP

30

30

Build a Data ecosystem and own it

ConceptualizationDesign & Development Product Usage Serviceability Manufacturing

ERP

Geo-locationdata

Social data Compete Data

Usage data Bug Reports

ProductData

Customer Data

Open Data

Web Data

Partner Data

Market Data

Design Data

Libraries

Enterprise Data

Customer Map Usage data

Govt. Data Content

Logs

SCADA

Sensor

Market Data

Machine Data

Energy Pricing MRO data

PLM Product Cloud

Supplier APIs

Data as a Product External Data APIs Internal Data APIs

2


Source : DRAUP

31

31

Balance internal and external innovation

Toyota leverages multiple facets of the innovation fabric to drive innovations in aligned technology areas

Headquarters in Japan is supplemented by 5 engineering hubs

India, China, Thailand ,Mexico & Brazil

Palo Alto research labs for AI & Robotics research

Acqui-Hired IT born robotics start-up

The SAIL-Toyota Center for AI Research

Partnered to develop autonomous car technology.

New Age Innovation Fabric

Porous innovation permeating beyond the walls of the organization

3


Source : DRAUP

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32

Partner with start-ups

Venture Fund

Accelerators

Evangelize

Ecosystem Collaboration

• Extensive hands on support & infrastructural support

• Connecting with clients and investors

• Limited platform & soft infrastructure support • Connect the startup teams with VCs & partners

• Partnership with accelerators/Universities• Mentorship support & events participation

Capital Investment Non-Capital Investment Examples

• Product GTM support• Senior level team hiring /restructuring

3rd Party Accelerators

• Partner with other accelerators, innovation workshops with stakeholders

Arena 120Microsoft Start-up accelerator

3


Source : DRAUP

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33

Build the right platform partnerships: CASE STUDY- Medtronic

GPU Hardware Platforms

Big Sur Tensor Processing Unit

Github – Public

Datasets

Infrastructure Platforms

Data Sets

Applications

ORCHESTRATION OF SPECIALISTS

Leverages Watson’s open API to

build MyCareLink Smart App that

predicts low blood sugar

Apache 2.0 open

source libraries

EHR, Clinical data

through pharmacies

and universities

H20.ai python-based ML

libraries

AWS ML optimized

infrastructure

Fraud Detection Diabetes Detection Cart checkouts Drones

AI Platforms

Driver Safety

Medtronic leverages open source infrastructure in

multiple areas of its product stack

3


Source : DRAUP

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34

Hire new talent and rebuild team structures4

Centralized AI Teams

Product Management

Sales & Marketing

Customer Support

AI ResearchScientists

Engineering

Product Management

Sales & Marketing

Customer Support

AI Research Scientists

Engineering

CXOs

ML EngineerAI Research Scientists

Data Scientists

De - Centralized AI Teams

CXOs

ML EngineerData Scientists


ProductManagement


Data Scientists

ProductEngineering

Teams

Sales & Marketing

Customer Support

ML Engineer

ML EngineerML Engineer

AI ResearchScientists

Data Scientist

Machine Learning Engineer

Education: MS or PhD in CS & MathematicsNeural networks, NLP, machine learning, statistical modelling, pattern recognition

Education: Bachelors/Master Degree in CS Problem solving, and programmingPython, Java / C++, as well as ML toolkits such as Theano, Tensorflow, Keras or similar

Education: Masters, PhDsAnalytics: SAS/RCS: Python, Hadoop, SQL, Data Derivation from Unstructured data

New Team Structure

New Talent


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Leverage AI talent from emerging hotspots such as India and China

USA

Canada

65 110 UK

35

China70

85

Available AI & related Talent in Country (in thousands)

2015 2025

20Israel

India

A global satellite that is part of the global CoE for ML, reporting to Berlin

Global engineering hub that has small team working on ML

Global engineering satellite that drives activities in Computer vision space

Global CoE for IoT and Advanced ML space.

18

615 155

Beijing is a global engineering satellite that works on NLP and Computer vision tech

Partnered with Didi in crowdsourcing challenge for optimising route algorithm

Note : Coverage may be limited in China and other south east Asian counties;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017

4

10

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ü Which are the AI talent hotspots in India ?

ü What are the key AI programmes from global R&D organisations based in India?

ü What is the maturity of AI start-up Ecosystem in India?

ü What are the top AI innovations incubated by Indian TSPs?

AGENDA




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Case Study (India) - Almost half of the total installed AI talent in India is concentrated in Bangalore

AI Talent Split Across Locations

14,500 – 15,500

Experience Split for AI Talent

Note: Others include the following cities: Chandigarh, Jaipur, Ahmedabad, Baroda, Kolkata, Vishakhapatnam, Mysore, Coimbatore, Kochi, Madurai, Trivandrum; IT GIC + R&D GIC includes Computer Vision, Machine Learning, NLP and Robotics only;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017

COMPUTER VISION

MACHINE LEARNING NLP ROBOTICS

~ 4,000 ~ 6,000 ~ 1,500 ~ 3,500

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Case Study (India) - Various global companies are looking to develop AI-based solutions in India

Enterprise Software

The Bangalore center is focused on data analytics products to ensure that customers globally have a seamless experience across multiple channels -physical stores, the web, mobile etc.

Customer Experience

Microsoft India is launching a research group todeliver large-scale eye care in collaborationwith Hyderabad-based L V Prasad EyeInstitute.

Healthcare

Retail

WalmartLab's Bangalore center is focused onintegrating various data objects (e.g. customerbehaviour) to create solutions that can beimplemented across their stores

Cognitive Computing , industryspecific applicationsThe research lab in India is working closelywith financial institutions through India and AsiaPacific.

Machine Learning, Big Data &Analytics

Data Analytics Image processing


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Case Study (India) - The start-ups in India have been quick to tap the AI potential

BangaloreDelhiHyd

40%20%10%

APPLICATIONS

PLATFORMS

INFRASTRUCTUREDATA PLATFORM -$2Mn

COMPUTER VISION -$16Mn

NLP - $1Mn

GESTURE CONTROL

AUTO - $1Mn RETAIL -$0.5Mn

VIRTUAL ASSISTANT -$2M

HR - $5Mn

E-COMMERCE MARKETING - $1Mn

MACHINE LEARNING -$1Mn

8Acquisitions

Undis.Investment

Applications

Deep Learning

Virtual Assistants

Machine Learning

tuplejump

ZoyoAI

Cruxbot

~170Start-ups

$0.03 Bn

Investment

Note: No inflation assumed (all values in 2017 USD). Funding details are updated with nearly 70% accuracy;

The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017

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Case Study (India) - Indian IT-BPM companies are already responding with their proprietary AI platforms

Recent AI-driven Headlines in the Indian IT-BPM

space


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Case Study (India) - AI Platforms of top Indian IT service providers

TCS Wipro Infosys

• Reposition to serve ‘Heart of Business’

• Technology / AI Advantage

HCL

• Broad based (BPM Focus) • Broad based (including engineering, ADM & BPM)

• Broad based (Infrastructure services)

• Plug and play deployment requiring customization and learning

• Stand alone platform for core infrastructure services

• Plug and play deployment requiring customization and learning

• Stand alone platform offering a menu of multiple cognitive services

• Bespoke deployment

• AI capabilities bolt-on to existing automation architecture (IIP, IKP, IAP framework)

• Bespoke deployment

• AI modules bolt-on to existing automation platform; collaboration with Watson, S-Now, Dynatrace, Splunk

DEPLOYMENT & PLATFORM

OVERVIEW

• End to end infra services such as

• Infra blueprint• Self healing • Deployment• Predictive

maintenance

• Digital Virtual Assistants

• Prediction systems

• Robotics & Drones

• Engineering (aircraft floor beam development)

• Forecasting as a service

• Detect and correct Infra and App issues

• Watson power chat agent

STATED DOMINANT USE CASES

KEY FOCUS AREAS


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SUMMARY

• Artificial intelligence has finally gotten out of university research labs and Hollywood studios to impacting our daily lives. Technology innovations across fourdimensions is resulting in the faster evolution of AI: 1. Computational power is faster and cheaper 2. Data availability has exploded through the use of smartphone and IOT devices 3. AI algorithms developed painstakingly across research labs and universities over the last five decades have come to the forefront due tothe access to low cost computing power 4. Access to training data and real-time platforms

• The challenge is on how AI will advance in the real world where the constraints are not known and there is a lot of unpredictability. The battle now will movefrom games to the real world. AI is just not enabling some new product features but is playing a role across the value chain of the industries. Take the example ofthe Automotive industry - Over 700 million dollars of funding has gone into start-ups focused on AI-led disruptions

• AI is becoming one of the fastest technologies to be deployed across industry verticals. Companies such as Facebook, Google who are really advertisingplatforms, are using AI to better match the advertisements to the user preference. Companies such as Microsoft and Google are using AI in creating self-healingnetworks and even in reducing the cost of cooling at their data centers

• There are three kinds of companies that are investing into AI. First is the disruptors – start-ups who are building AI platforms and applications, second is TechMafia – Apple, Google, Facebook, Microsoft and Amazon- which are dominating the AI platform space and third - G500 companies that are still figuring out thespace but will eventually play a key part in industry applications

• Tech Mafias, led by Google has invested over USD 10 billion dollars in acquiring AI start-ups and collectively employing over 30,000 engineers working on AIplatforms and applications. Google’s CEO describes Google as an AI-first company. These companies are opening up a lot of their innovations in AI by makingthem open source and are making them accessible through APIs

• Venture capitalists across the world are seeing the potential in AI and have increased their investments five-fold in the last 10 years. The funding is higher forapplication layer than other areas. Companies such as Sentient have already got over USD 100 million in investments and are building massive scale AIplatforms using Deep learning

• US start-ups have dominated VC investments. The rest of the investments are spread across Europe, China, India, Israel, Singapore and Australia

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SUMMARY

• DRAUP has defined 4 easy steps for large companies to adapt AI. First is to identify the key business use cases that can help reinvent existing products and

services or create new opportunities for growth. The second is to create and access a large set of data sources that will help train the AI engines. Third is to ensure

the presence of a platform that takes advantage of all the AI-led innovation happening in the ecosystem between the Tech Mafias, start-ups and universities. Fourthis to ensure the engineering talent capability internally is transformed to include talent pool in locations that will drive the next generation of AI platforms and

applications.

• Jobs can be categorized based on the application complexity and data complexity of the task. The jobs where the data complexity and application

complexity are low, are ideal candidates for full automation – use cases could be personalization, image recognition etc.

• The second step is to create and access all the data that is required to train the AI platform. The data should be across the product value chain and come

from internal sources, customers and partners. It is also critical to access external context data that is relevant from other data sources. It is critical to own as much

data as possible as it might be a key differentiator

• The third step is to develop an open innovation fabric to engage with the ecosystem. Organizations need to create systems that seamlessly integrate internal

innovations with external innovations. A strong process to understand the key disruptions in the industry by keeping tab of the innovations happening across the tech

Mafias and start-up ecosystem is critical

• Lastly the organisations need to structure the global engineering organization into hub-satellites to ensure they can tap into the right level of talent and

competency across the globe. The availability of affordable talent with AI skills is going to rapidly increase in countries such as India and China. Create a hub or a

center of excellence for AI in locations that have the potential to quickly scale AI talent. Amazon, for example, has a center of excellence in Bangalore for Machine

learning

• Organisations need to orchestrate partnerships across the AI stack – GPU and Hardware, Infrastructure, AI Platforms, Data sets and Applications. In order to build

their applications, organisations should use the best of breed data sets and partners across the AI stack. Indian Service Providers such as Wipro, TCS andInfosys have already taken a step ahead to develop the capability across the AI stack and use them to integrate the various layers.

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