agenda ibm - aub.edu.lb · 1 agenda • welcome to the cognitive era • demystify ai / ml / dl •...
TRANSCRIPT
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, [email protected]
“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
5
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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
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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+
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
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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
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
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
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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
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• 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
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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)
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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
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• 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
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• Market Leader in Data Science
• AI to do AI
• Automatic reports, feature engineering, model training
• Interpretable models
Watson Studio Local
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• 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
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
16xMORE
MEMORY
per node
“SUMMIT” on POWER
vs.“TITAN” on x86 >
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.
Chief Data Scientist, Cognitive Systems
IBM, Dubai
Thank you !