KIVI Innovation Drinks Twente

An AI encounter with BrainCreators

Overview

➢ Short introduction BrainCreators➢ Example use-cases➢ AI maturity model➢ The Application Gap➢ Epilogue: pretty pictures

Discover value Deploy solutions Accelerate teamsCompile a strategic roadmap of viable

business cases

Implement scalable solutions with maximum

business impact

Inherit skills & best practices with

expert coaching

BrainCreators applies 20+ years of experience in artificial intelligence to business challenges across all verticals

TRUSTED BY

Use cases

➢ Smart Radio➢ Logistics➢ Fashion and Retail➢ Steel quality control➢ Genetics ➢ Telecommunication

Smart Radio

● 10 hours original radio broadcast per day ● Additional podcast creation● Inconsistent manual tagging of content● Course segmentation of topics

24/7 news radio● Automatically curated playlists of news content● Taylored to a listener’s preferences● On demand

Smart Radio

Smart Radio

● Audio feature detection● Detection of semantic overlap among existing labels● Semi-supervised refinement of existing dataset● Topic modeling● Segment classification

AI under the hood

Smart Radio

● Detect topics in segments● Cut audio segments ● Provide relevant user content

Results (v1 in the making)

Logistics

● Manual identification of address data for 15% of total volume● 4% delivered to wrong address● Geographical location of delivery points imprecise● Delivery window too coarse

Before application of AI

Logistics

● Fuzzy logic address matching● GPS delivery point prediction● Time window estimation & optimisation● Automated location mapping (inc. po-boxes)● Trained on historic data and self learning

AI under the hood

Logistics

● Manual correction reduced to <2% of total volume● Delivery failures reduced by 50%● 2000 man hours saved per month● Improved customer service through better time windows

Results

Fashion & retail

● Manual classification of products● Complex mappings to market place taxonomies● Poor quality of properties data● Basic recommendations

Before

Fashion & retail

● Training set of 20+ Million products● Combined Image & Text classifiers● Sorting of products using complex features● Human-in-the-loop data improvement

Under the hood

Fashion & retail

● Automated categorization >95% accurate● Auto-enrichment of product data● Product family recommendation● Cross & upselling automation

Result

● A major European steel producer● Total of 7.1 million tonnes of steel

products in 2016● High quality sheet and strip steel● Automotive, packaging, and construction

sectors

General

Steel quality control

● Kilometers of steel sheet each day● Accurate quality assessment enables

more profitable trading● Defects need to be detected to prevent

machine breaks● Manual inspection supported by

automatic camera system

Initial Situation

Steel quality control

● Infrared cameras inspect moving steel sheets on conveyor belts

● Basic image processing detects regions of interest

● Manual inspection often needed● Accuracy can still be improved

Camera system

Steel quality control

● Up to 50 different defect types● 5 million (!) new images each day● Currently only 25 thousand annotated

images available in total● Severely imbalanced data sets● Manual annotation is costly

Data sets

Steel quality control

● Deep Learning for robust image classification

● Ai & Active Learning approach for efficient image annotation

● Integration in existing systems● Knowledge transfer to customer’s own

tech team

Solution

Steel quality control

Genetics for livestockthe animal protein value chain

Genetics for livestockthe animal protein value chain

● Large scale selective breeding as an industrial optimization process

● Changing targets due to commercial, political, and environmental requirements

● Integration of different data sets, including genomics data

● Evaluation is either slow or imprecise

Selective breeding

Genetics for livestock

Breeding value = Genetics + Environment

● Predict carcass properties from measurements on live animals

● Numerical input data, e.g. weights at different ages

● Ultrascan visual data● How can the ultrascans be used more

effectively ?

Challenge

Genetics for livestock

● Information can get lost in the narrow passage of human interpretation

● Deep learning helps to extract useful information from complex visual data

● Less requirements for human understanding of the images

● End-to-end learning combines visual and non-visual data into one system

Narrow passage

Genetics for livestock

Human understanding Deep Learning

● Very large telco network

● Hybrid Fibre Coax● Up to 5M modems● Modems report their status

● Thousands of relay points● Diversity of legacy systems

Initial situation

Fault detection in telecom

…..

● Manage fleet of field technicians● Network errors and maintenance

● Detect & classify problems● Find problem root causes

● Collect useful data

Challenge

Fault detection in telecom

● Dedicated data labeling software● Network Anomaly Detection

● Generalize to all fault types● AI Roadmap

Solution

Fault detection in telecom

Human understanding Deep Learning

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring Planning

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring Planning

Experimenting

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring Planning

Experimenting

Productizing

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring Planning

Experimenting

Productizing

Scaling

TRANSFORMING TO A DIGITAL ENTERPRISE

Exploring Planning

Experimenting

Productizing

Scaling

Data-Centric

TRANSFORMING TO A DIGITAL ENTERPRISE

The Application Gap

The Application Gap… between Research and Industry

The Application Gap… between Research and Industry

● When is something “solved” ?● Has it been demonstrated to work once,

under special circumstances?● Or is it ready and safe to deploy in

general, right now, for everyone ?

● Speech-to-text ? ● Self-driving cars ? ● … …

Solved? .... really?

Andrew Ng: “AI is the new electricity”

“We have enough papers. Stop publishing, and start transforming people’s lives with technology!”

The competitive landscape

Thank you!Maarten Stol maarten.stol@braincreators.com

BrainCreators Prinsengracht 697 1017JV Amsterdam+31 (0)20 369 7260

Epilogue

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image SynthesisAndrew Brock, Jeff Donahue, Karen Simonyan

GAN Progress

Source: www.eff.org/files/2018/02/20/malicious_ai_report_final.pdf

Source: PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION Tero Karras, et.al. NVIDIA

Source: PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION Tero Karras, et.al. NVIDIA