1

Agenda

• Welcome to the cognitive era

• Demystify AI / ML / DL

• Selected AI Industry Use Cases

• How IBM made AI ready for Enterprises ?

• Why infrastructure matters ?

• The future of AI

IBM

The Journey TowardsEnterprise AI

Ahmad El Sayed, Ph.D.Chief Data ScientistCognitive Systems, IBM, Dubaiahmad.el.sayed@ibm.com

Welcome to the Cognitive Era

“AI is the fastest growing workload on the planet”, Forrester

190,000

shortage of people with analytical expertise

300%

Increase in AI Spend year over year

$ 320 Billions

The potential impact of AI in the Middle East by 2030

50%

Of CIOs have started or planning to deploy AI solutions

Da

ta

Time

Available

Data

Understood Data

Enterprise

Amnesia

5

010101010101010111100010011001010111

0000000000010101010100000000000 111101011

11000 000000000000 111111 010101 101010 10101010100

PrescriptiveBest Outcomes?

DescriptiveWhat Has Happened?

CognitiveLearn Dynamically

PredictiveWhat Could Happen?

ACTIONDATA

HUMAN INPUTS

<< >

< >

>cc

c

c

When sci-fi becomes reality

6

6

https://arxiv.org/pdf/1612.03242.pdf https://arxiv.org/pdf/1702.00783.pdf

Creating images from textual descriptions Enhance images from low-res versions

Welcome to the dawn of the Cognitive Era.

+ Large-Scale Data

+Hardware Capabilities

+ Scalable Algorithms

Compassion

Intuition

Design

Value judgments

Common sense

Deep Learning

Discovery

Large-scale math

Fact checking

Human Machine+

Demistify AI / ML / DL

11

Teaching a Computer to Recognize a Bicycle

Questions

Rules

AnswersProgramming

Questions

Answers

RulesMachine Learning

Marketing Campaigns - Next Best Offer

13

Customer Credit Debit Tickets Gold Card

Jana 5 100 0 1

Asim 10 90 1 1

Elie 20 50 3 0

Mike 30 20 2 0

ML Algorithm

ML ModelOutput = Function (Input)

OUTPUT INPUTS

OUTPUT INPUTS

Training

Inference

Customer Credit Debit Tickets Gold Card

Mike 6 120 1 0.9

Jad 12 85 0 0.8

Layla 18 55 3 0.3

Samar 25 30 4 0.1

Machine Learning

14

Customer Age

Nb Credit Transac

Nb Debit Transac

Nbr Tickets

INPUT OUTPUT

Forward Propagation

Error

Backward Propagation

w1

w2

w3

w4

1/0

Deep Learning

15

INPUT OUTPUT

Input Layer

Output Layer

Hidden Layer

Hidden Layer

Forward Propagation

Error

Backward Propagation

Customer Age

Nb Credit Transac

Nb Debit Transac

Nbr Tickets

INPUT OUTPUT

Earlier LayersDetect Edges

Later Layers Detect Features Eyes, Nose, Mouth

Demystify AI / ML / DL

17

Deep Learning allows higher accuracy on bigger data

Small NN

Data

TraditionalML

Medium NN

Large NN

Acc

ura

cy

2011Machine Learning

26% Error

Human5% Error

2016Deep Learning

3% Error

Selected AI Use Cases

ProblemEnsure the safety of citizens by detecting parking violators; enforce traffic regulations, etc.

SolutionBuild AI models to identify, classify ant count vehicles and raise alerts in case of any violation, and recommend optimal routing/

BenefitsDecrease incident response timeOptimize traffic routing to avoid trafficDetect traffic violations

Smart Cities - Traffic Management

ProblemLow campaign response rates as they are typically executed on mass or segment-based customers.

SolutionBuild predictive model that takes customer 360 view as input and previous campaign responses as output.

Benefits- Increase in campaign response rate- Increase nb of products per customer- Increase in Share of Wallet

Marketing - Next Best Offer

ProblemAs opposed to online channels, brick and mortar retailers lack visibility of customer behavior inside their stores.

SolutionBuild AI models to classify visual features from in-store/mall video, to collect data on consumers profiles, shopping journey, product placement, product touches, and other KPIs

BenefitsOptimize in-store product assortment by analysing profiles and behaviour.Increase of Sales / Margins

Marketing - Footfall Analysis

ProblemHelp doctors to more accurately and more effectively identify the suspected illness areas in the medial images.

SolutionBuild deep learning model to detect, localize and classify suspected disease areas on

medical images, e.g. X-ray, CT, MRI

BenefitsMore accurate disease detectionEfficiency gain to analyse images fasterEarly detection of diseases

Healthcare – Medical Image Analysis

ProblemChallenges of out-of-stock or over-stock result in lost sales and an increase of inventory carrying costs.

SolutionPredict demand based on multimodal data such as historical demand (sales, consumption), marketing data (campaigns), external data (events, weather, demographics, social media)

BenefitsMaximize the service level as well as minimize the inventory cost,increase sales / margins, decrease days of inventory, improve product availability

Operations - Demand Forecasting

Operations - Predictive Maintenance

ProblemReactive and preventive maintenance implies that considerable time/effort is spend on inspecting the wrong asset

SolutionBuild machine learning models to predict failure based on sensor data, asset and maintenance data and then recommend actions to fix part failures.

BenefitsImprove assets healthReduce assets downtimeReduce maintenance costs

ProblemManual quality inspection of assets is time-intensive, exhausting, hazardous, inaccurate and sometimes risky.

SolutionBuild AI models to detect, localize and classify defects in batch or real-time on images or videos.

BenefitsImprove inspection accuracyReduce defects and inspection costsEnsure 24/7 operability

Quality Control - Visual Inspection

Security - Worker Safety

ProblemNot enough staff to monitor all cameras placed at dangerous zones,

SolutionBuild AI models to detect workers not respecting security instructions (e.g. no helmet, no vest)

BenefitsReduce risk of incidents at dangerous zones without having to hire more staff to monitor live CCTV cameras.

ProblemNot enough security staff to monitor all cameras at all time. VMS aren’t flexible to detect new patterns, large number of false alerts, static patterns.

SolutionBuild AI modes to detect suspicious objects or activities and generate alerts in real-time to prevent crimes.

BenefitsDecrease in false and missed alertsDecrease in average search timeDecrease in number of crimes

Security - Video Surveillance

ProblemFraudsters are constantly finding new schemes to fraud the system, so it’s important to have multi-channel monitoring

SolutionBuild AI models that can constantly learn to detect evolving thefts techniques from structured and unstructured data

BenefitsReduce fraud lossesPrevent reputational damagePrevent fraud in real-time

Security - Fraud Detection

ProblemUsers spend 2 weeks in average to watch a program and extract the top scenes in 2 minutes trailer, which is very time-consuming

SolutionAI models are built to analyze the video and audio to rank scenes by the level of excitement and then select the top ones

BenefitsProducing a trailer with our models take now 2 hours as opposed to 2 weeks which free stafffor more quality tasks.

Entertainment - Trailer Production

How IBM made AI ready for the Enterprise ?

32

Enable non-Data Scientists to use AI(PowerAI Vision & Others)

Higher Productivity for Data Scientists(Faster Training with Larger Models)

Integrated & Supported AI Platform

Caffe

IBM PowerAI – the Enterprise Offering for Deep Learning

GPU-Accelerated

Power Servers

Storage

PowerAI Base on IBM Power AC922

33

• Co-Optimized Software + Hardware

• Enterprise Software Distribution

• Best Server for Enterprise AI with super accelerated highways between CPU-GPU and GPU-GPU

• Performance optimized for large model support and distributed deep learning

• Enterprise Support L1-L3

Caffe

34

34

34

3.1 Hours

49 Mins

0

2000

4000

6000

8000

10000

12000

Xeon x86 2640v4 w/ 4xV100 GPUs

Power AC922 w/ 4x V100GPUs

Tim

e (s

ecs)

Caffe with LMS (Large Model Support)Runtime of 1000 Iterations

3.8x Faster

GoogleNet model on Enlarged ImageNet Dataset (2240x2240)

34

1 System 64 Systems

16 Days Down to 7 Hours58x Faster

16 Days

7 Hours

Near Ideal Scaling to 256 GPUs

ResNet-101, ImageNet-22K

1

2

4

8

16

32

64

128

256

4 16 64 256

Spee

du

p

Number of GPUs

Ideal Scaling

DDL Actual Scaling

95%Scaling

with 256 GPUS

Caffe with PowerAI DDL, Running on Minsky (S822Lc) Power System

ResNet-50, ImageNet-1K

4x faster on 1 node / 58x faster on 64 nodes

IBM PowerAI Vision

35Think 2018 / DOC ID / Month XX, 2018 / © 2018 IBM Corporation

• End-to-End Deep Learning

• Computer Vision Applications

• AI for All – User-Friendly

• Fast & Accurate

Intelligent Video Analytics

36

• Pre-trained models for video surveillance

• Complex Event Monitoring with GUI-based Configuration

• Facial Recognition & People Search

• Detect Changes to Patterns

• Search events and get alerted

H2O Driverless AI

37

• Market Leader in Data Science

• AI to do AI

• Automatic reports, feature engineering, model training

• Interpretable models

Watson Studio Local

38

• Environment for Data Scientists

• Project Management & User Collaboration

• Model lifecycle management

• Adapted for different skillsets

• Vision, Speech, NLP APIs

• Sentiment Analysis

• Knowledge Studio

• Natural Language Understanding

• Primarily Targeted at Application Developers

• Available as Cloud APIs (SaaS)

Watson APIs

▪ Easy to use: “drag and drop” visual workflow

▪ Automated data modeling

▪ Automatic data preparation

▪ Advanced capabilities: text analytics, entity analytics, scripting

▪ Extended support for open source

▪ Code-Free Deployment at Scale: Activating Analytics

▪ On Cloud or On-Premises

Predictive Analytics - SPSSSPSS Modeler

Why infrastructure matters ?

Start your AI Journey with the right Infrastructure

AI Platform AI InfrastructureBusiness

ApplicationAI Models

Business

Workflow

Moore’s-Law doubling time for processors is no longer 1.5 or even 3.5 years. It’s now twenty years.

John Hennessy and David Patterson, Computer Architecture: A Quantitative Approach, 6/e. 2018

5x Faster Data Communication with Unique CPU-GPU NVLink High-Speed Connection

1 TB

Memory

Power 9

CPU

V100

GPU

V100

GPU

170GB/s

NVLink150 GB/s

1 TB

Memory

Power 9

CPU

V100

GPU

V100

GPU

170GB/s

NVLink150 GB/s

IBM AC922 Power SystemDeep Learning Server (4-GPU Config)

Store Large Models in System Memory

Operate on One Layer at a Time

Fast Transfer via NVLink

Say “Hello” to POWER9

1.8xmore memory

bandwidth

vs x86

2xfaster core

performance

vs. x86

2.6xmore RAM

supported

vs x86

9.5xmax I/O bandwidth

vs. x86

46

5-10xFASTER

vs. previous

x86 system

75%LESS NODES

for superior

density

~29xPER NODE

PERFORMANCE

(>40TF)

~8xMORE

STORAGE

(250PB@2.5TB/s)

16xMORE

MEMORY

per node

“SUMMIT” on POWER

vs.“TITAN” on x86 >

Future of AI

The evolution of AI

© 2018 IBM Corporation 48

Narrow AIInitial

Value Creation

Broad AIDisruptive and Pervasive

General AIRevolutionary

We are here 2050 and beyond2010 and earlier 2015

▼

Learn from less data

Adapt learning to new domains without forgetting old ones

Embed security & ethics

Prevent human biases from propagating to AI systems

Substantiate decisions, build trust, and comply with regulations

Making AI robust for enterprises

© 2018 IBM Corporation 49

“AI programs exhibit racial and gender biases, research reveals”The Guardian, April 2018

“AI is quickly becoming as biasedas we are”

The Verge, April 2018

“Facial Recognition Is Accurate, if You’re a White Guy”The New York Times, February 2018

Can AI be biased?

50© 2018 IBM Corporation

IBM AI

Explain a transaction

Deployment: Claim Approval Model name: Claim Model

AI Fairness 360 toolkit

Trust and transparency integral to AI on the IBM Cloud

Explainability, fairness, lineageare critical principles of trusted AI

Open source toolkit to check for unwanted bias in datasets and machine learning models

© 2018 IBM Corporation 51

DENIED APPROVEDCONFIDENCE

90% 10%

POLICY HOLDER AGE: 18 RESPONSIBLE PARTY: Self

CAR BRAND: Oldsmobile Cutlass POLICE REPORT: Yes

CAR VALUE: $20,000 POLICY AGE: 5 Years

65% 17%

23% 13%

13% 5%

Factors contributing to a DENIED confidence level Factors contributing to an APPROVED confidence level

IBM’s global research capability

HealthcareGovernment

Financial Services

HealthcareIndustry CloudIoTBlockchain

Cognitive RoboticsFinancial ServicesAccessibility

Green Horizon EnergyOpenPOWER Cloud

Cognitive Health

BlockchainCognitive FashionEducation & SkillingCognitive Financial Services

CognitiveHealthcareIoT & MobileSecurity

SecurityAnalytics

NanotechnologyExascale

Cognitive IoTAI for HealthcareEdge Computing Big Data & Cognitive

CloudHealthcare / Life Sciences

Quantum Computing

POWERMobileAging

Cognitive Oil & GasInsurance AnalyticsIndustry Cloud

Big DataNanomaterials

Neurosynaptics

3,000+ researchers

Australia

Tokyo

China

Almaden

Haifa

Zurich

Africa

Ireland

Brazil

Watson

Austin

India

© 2018 IBM Corporation 52

Foundational breakthroughs have made us famous

6NobelLaureates

10NationalMedals of Technology

6TuringAwards

5NationalMedals ofScience

© 2018 IBM Corporation 53

Ahmad El Sayed, Ph.D.

ahmad.el.sayed@ibm.com

Chief Data Scientist, Cognitive Systems

IBM, Dubai

Thank you !