WELCOME [on-demand.gputechconf.com]on-demand.gputechconf.com/gtcdc/2018/pdf/dc8101-ai-for...KEY...
Transcript of WELCOME [on-demand.gputechconf.com]on-demand.gputechconf.com/gtcdc/2018/pdf/dc8101-ai-for...KEY...
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WELCOME
JOSH ELLIOTDirector, Artificial Intelligence & Emcee (for today)
Booz | Allen | Hamilton
@JoshElliotDC
/JoshElliotDC
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SESSION TIME PRESENTER
CHECK-IN/REGISTRATION 1:00-1:30PM N/A
KEY NOTE: INTRODUCTION TO AI 1:30-1:50PM Kirk Borne, Booz Allen
GOVERNMENT PROJECTS
Computer Language Initiative 1:50-2:05PM Capt. Michael Kanaan, Air Force
AI in Biomed, Drug Development, and Regulatory Science 2:05-2:20PM Dr. Sean Khozin, FDA
Velocity Lab: AI from Almost Nothing 2:20-2:35PM Dan Pines, Navy
Facilitated Q&A on Projects 2:35-2:50PM Josh Elliot, Booz Allen
BREAK 2:50-3:00PM
PANEL: HOW TO BUILD AN AI TEAM 3:00-3:40PMHost: Shelly Brown, Booz AllenThomas Beach, USPTO; Lee Becker, VA; David Bottom, OMB;Kenneth Clark, ICE; COL Benjamin Ring, USCYBERCOM; Anil Tilbe, VA
BREAK 3:40-3:55PM
HOW TO APPROACH AI
How to Begin with AI 3:55-4:25PM Margaret Amori, NVIDIA; Seth Clark, Booz Allen; May Casterline, NVIDIA
AI Buying Considerations 4:25-4:45PM Jennifer Arnold, Booz Allen; Josh Elliot, Booz Allen
CLOSING: WHERE SHOULD YOU GO FROM HERE 4:45-5:00PM John Larson, Booz Allen
AGENDA
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KEYNOTE: INTRODUCTION TO AI KIRK BORNEPrincipal Data Scientist
Booz | Allen | Hamilton
@KirkDBorne
/kirkdborne
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60 BILLIONVideo frames per day uploaded on YouTube
VIDEO
140 BILLIONWords per day
translated by Google
TRANSLATION
500 MILLIONDaily active users on
iFlyTek
SPEECH
2 TRILLIONMessages Per Day on
PERSONALIZATION
ARTIFICIAL INTELLIGENCE IS EXPLODING.
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HYPEandreality….
ZDNet
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Inductive
Evolutionary Probabilistic
Kernel
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
DEEP LEARNING
Quantum Annealing
ASICs
HYPERSCALE HARDWARE
GPUs
MACHINES CAN LEARN
Booz Allen analysis, Michael Copeland (NVIDIA)
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TITLE
6Booz Allen Hamilton Internal 6
TEXT
Booz Allen Hamilton Restricted
UMD Cognitive Neurosciences, Booz Allen analysis
There are other forms of learning, this is a summary for context setting
Learning from sources of knowledge happens in
two main ways:
Facts and specific details that you retain in various methods...
▪ Washington DC is the capital of the US
▪ More than half of the coastline of the entire United States is in Alaska
Experiences you must have on your own to retain...
▪ Balancing on a bicycle
▪ Pronunciation of a foreign language
Moving between deductive and inductive reasoning during the learning cycle is a learning technique used by humans and machines
DIRECT INDIRECT
HOW DO PEOPLE LEARN?
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HOW DO MACHINES LEARN?Five approaches to structuring machine learning algorithms
“TRIBE” ORIGINS MOTIVATIONTECHNICAL APPROACH
SYMBOLISTS Logic, Philosophy
Automate the scientific method
Inverse Deduction
CONNECTIONISTS NeuroscienceReverse engineer the human brain via math model of neurons
Backpropagation
EVOLUTIONARIES EvolutionaryBiology
Replicate the evolution of the human brain over generations
Genetic Programming
BAYESIANS StatisticsTest hypotheses to determine the certainty of knowledge
Probabilistic Inference
ANALOGIZERS PsychologyUse previous problems / solutions and extrapolate into new context
Kernel Machines
1. Fill in gaps in existing knowledge
2. Emulate the human brain
3. Simulate evolution over generations
4. Systematically reduce uncertainty
5. Find similarities between old and new
Pedro Domingos, Booz Allen analysis
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NON-EXHAUSTIVEInput cell Hidden cell Recurrent CellOutput cell Kernel
Perceptron(P)
Feed Forward
(FF)
RecurrentNeural Network
(RNN)
Deep ConvolutionalNetwork (DCN)
Strong predictive power when used with large amounts of sequenced
information (e.g., image classification, sentiment analysis)
Inspired by the animal visual cortex and used for wide applications in image and video recognition,
recommender systems, and natural language processing
Earliest and simplest neural networks; form the foundation
for future advances
RestrictedBoltzmann Machine
(RBM)
1958 1986 1990 1998 - TODAY1957
Ideal for making predictions based on past
behavior (e.g., Netflix recommendations)
Back-fed Input cell Probabilistic Hidden cellKEY:
NEURAL NETWORKS ARE POWERING TODAY’S AI ADVANCES
Different View? Jeff Hawkins On Intelligence
INCREASINGLY SOPHISTICATED ALGORITHMS
Booz Allen analysis, CoolInfographics Neural Networks, DeepLearning4J, Algobeans 2016
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AI CAPABILITY TECHNOLOGIES EXAMPLE USE CASES
Pattern Recognition & Response Maturing/Pilots and some scaled Deployment
Contextual ReasoningIn the lab
Machine Learning Software and Platforms
Semantic or “Cognitive” computing
Computer Vision
Natural Language Understanding
Autonomous Vehicles and Robotics
• Image/video tagging • Real-time video analysis• Sentiment analysis
• Facial recognition • Scene analysis• Biometrics
• Complex task automation• Real-time data analysis and response
• Virtual assistants • Chatbots• Machine translation• Speech recognition• Language detection
• Sentiment analysis• Text analysis • Report generation • Insight summarization
• Co-bots• Smart manufacturing • Smart logistics • Companion robots
• Partially autonomous vehicles/unmanned systems
• Execution of tasks requiring context, judgment
• Fully autonomous vehicles
A cyber security algorithm detects, classifies, and prevents a network-based attack
A video sensor on a drone identifies damage to an airfield runway
Virtual assistants engage with citizens to ask about available camp grounds on recreation.gov
A robotic surgeon performs surgery, automatically responding to changes in a patient’s condition in real time
A vehicle drives down a crowded city road, responding to bad weather, unexpected pedestrian behavior, and obstacles in traffic
EXAMPLE APPLICATION
AI OFFERS A RANGE OF OPPORTUNITIES FOR HUMAN AND MACHINE COLLABORATION
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PARTINGTHOUGHTS
Key AI Thought Pieces available for download @ www.boozallen.com/ai
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CAPT. MICHAEL KANAANEnterprise Lead for Artificial Intelligence & Machine Learning,
HQ USAF Intelligence, Surveillance, and Reconnaissance
UNITED STATES AIR FORCE
COMPUTER LANGUAGE INITIATIVE
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AI IN BIOMED, DRUG DEVELOPMENT, AND REGULATORY SCIENCE
DR. SEAN KHOZINAssociate Director, FDA Oncology Center of ExcellenceFounding Director, FDA INFORMED
FOOD AND DRUG ADMINISTRATION
@SeanKhozin
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VELOCITY LAB: AI FROM ALMOST NOTHING
DAN PINESChief Innovation Officer, Naval Surface Warfare Center Indian Head EOD Technology Division
UNITED STATES NAVY
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JOSH ELLIOTDirector, Artificial Intelligence
Booz | Allen | Hamilton
@JoshElliotDC
/JoshElliotDC
FACILITATED Q&A ON PROJECTS
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HOST: SHELLY BROWNDirector, Booz Allen’s Data & Analytics Unit
Booz | Allen | Hamilton
DAVID BOTTOMCloud Migration Program Manager
O F F I C E O F M A N A G E M E N T & B U D G E T
KENNETH CLARKDeputy Assistant Executive Director
U. S . I M M I G R AT I O N A N D C U S TO M S E N F O R C E M E N T
PANEL: HOW TO BUILD AN AI TEAM
THOMAS BEACHChief Data Strategist & Portfolio Manager
U. S . PAT E N T & T R A D E M A R K O F F I C E
COL. BENJAMIN RINGDirector of the Applied Research and Development (ARD) Division
U. S . C Y B E R C O M C A PA B I L I T I E S D E V E LO P M E N T G R O U P ( C D G )
ANIL TILBEDirector of Enterprise Measurement & Design
V E T E R A N S A F FA I RS , V E T E R A N S E X P E R I E N C E O F F I C E
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MARGARET AMORIDirector, Artificial Intelligence
NVIDIA
SETH CLARKAI, Product Manager
Booz | Allen | Hamilton
GETTING STARTED WITH AI
MAY CASTERLINESolution Architect
NVIDIA
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DNN GPU BIG DATA
THE BIG BANG IN AI
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Inductive
Evolutionary Probabilistic
Kernel
Artificial Intelligence
Machine Learning
Deep Learning
Quantum Annealing
ASICs
HYPERSCALE HARDWARE
GPUs
RECAP: MACHINES CAN LEARN!
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AI
This Photo by Unknown Author is licensed under CC BY-SA
A PROBLEM
This Photo by Unknown Author is licensed under CC BY-NCThis Photo by Unknown Author is licensed under CC BY-SA
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PROBLEM 1st
This Photo by Unknown Author is licensed under CC BY-NCThis Photo by Unknown Author is licensed under CC BY-SA
AI 2nd
This Photo by Unknown Author is licensed under CC BY-SA
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Is AIever
OVERKILL?
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It’s a Good Idea, but Too Soon It’s an Excellent Choice! It’s Probably Excessive
WHEN IS AI A GOOD IDEA?
Identifying people and objects in images or video
Translating speech or text from one language to another
Detecting fraud and other anomalous behavior
Autonomous vehicles
Generalized intelligence that’s indistinguishable from humans
Humanoid robotics
Intelligent language generation
Searching across multiple databases
Creating monthly financial dashboards
Automating that Excel spreadsheet Jennifer made before she left on TDY
Goldilocks Zone for AI
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INPUTS
Text Data Images
AudioVideo
BUSINESS QUESTION
What type of thing is “it”?
To what extent is “it” present?
What is the likely outcome?
What will satisfy the objective?
What is the speaker saying?
AI TASK
CLASSIFICATION
SEGMENTATION
PREDICTION
RECOMMENDATIONS
NATURAL LANGUAGE PROCESSING
HEALTHCARE
Image Classification
Tumor Size/Shape Analysis
Survivability Prediction
Therapy Recommendation
Expert diagnosis
GOV SERVICES
Cyber Security
Route Planning
Preventative Maintenance
Recommendation Engine
Real time Language Translation
GEOSPATIAL
Full Motion Video analysis
Building + Road Detection
Disaster Relief
Infrastructure Planning
Verbal Scene Description
COMMON APPLICATIONS OF AI
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Deep Learning Use Cases
Image VideoC Civil Services
M Manufacturing
D Defense
E Energy & Utilities
F Finance
H Healthcare
L Law Enforcement
Applicable Industries
Text
Sound
Vehicle Classification
Building Classification
Facial Detection
Synthetic Data Generation
Threat Detection
D
C
D
D
D
D
L
L
Damage Assessment
C D E
AI-Assisted Radiology
H
Disease Diagnosis
H
Defect Detection
M
Scrap Rate Reduction
M
Tracking & Targeting
Threat Detection
Behavior Classification
Facility Security
Automated ICU Monitoring
Video Captioning
Predictive Maintenance
D
Chat Bots for call centers
Document Exploitation
Web Chat Bots
Fraud Detection
Report Generation
Patient Record Mining
D
C
D
H
L
Malware Detection
H
Network Security
Asset/Inventory Optimization
M
Fraud Detection
F
Portfolio construction
Demand Forecasting
Anti-Money Laundering
Autonomous Vehicles
F
M
F
D
L
L
AI Agents in Simulations
D
Treatment Recommendations
H
Robotic Surgery
H
Disease Control & Prevention
C
Energy Distribution Mgmt
E
Structured
Multi-Modal
H
C D E F L M
HC D E F L M
F
E
L
F
E H
D LF
ME
C E H
D L
H
C D E F
D E F L
D L
D L
DEEP LEARNING USE CASES
Deep Learning Use Cases
Image VideoC Civil Services
M Manufacturing
D Defense
E Energy & Utilities
F Finance
H Healthcare
L Law Enforcement
Applicable Industries
Text
Sound
Vehicle Classification
Building Classification
Facial Detection
Synthetic Data Generation
Threat Detection
D
C
D
D
D
D
L
L
Damage Assessment
C D E
AI-Assisted Radiology
H
Disease Diagnosis
H
Defect Detection
M
Scrap Rate Reduction
M
Tracking & Targeting
Threat Detection
Behavior Classification
Facility Security
Automated ICU Monitoring
Video Captioning
Predictive Maintenance
D
Chat Bots for call centers
Document Exploitation
Web Chat Bots
Fraud Detection
Report Generation
Patient Record Mining
D
C
D
H
L
Malware Detection
H
Network Security
Asset/Inventory Optimization
M
Fraud Detection
F
Portfolio construction
Demand Forecasting
Anti-Money Laundering
Autonomous Vehicles
F
M
F
D
L
L
AI Agents in Simulations
D
Treatment Recommendations
H
Robotic Surgery
H
Disease Control & Prevention
C
Energy Distribution Mgmt
E
Structured
Multi-Modal
H
C D E F L M
HC D E F L M
F
E
L
F
E H
D LF
ME
C E H
D L
H
C D E F
D E F L
D L
D L
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TRAINING▪Blogs
▪Tutorials
▪Online courses
▪Formal training
MODELS▪Kaggle
▪Github
DATA▪Kaggle
▪Github
▪Imagenet
LIBRARIES &FRAMEWORKS
▪Academia (Caffe)
▪Google (Tensorflow)
▪Microsoft (CNTK)
▪NVIDIA (CuDNN)
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FACTOR QUESTIONS
DL CHALLENGE Supervised or unsupervised, classification or regression, # of labels?
ARCHITECTURE What is the simplest architecture I can use?
TRAINING MODEL How am I going to tune my neural net? Kinds of non-linearity, loss function and weight initialization? Best training framework?
DATA QUANTITY How much data will be sufficient to train my model? How do I go about finding that data and is it evenly balanced?
DATA QUALITY Is my data directly relevant to the problem & real world data.
DATA LABELS Is training data is labeled same as raw data sets, how do I ‘featurize’?
DATA SIMILARITY Is data same length vectors or does it require pre-processing?
DATA STORAGE &ACCESS
Where is it stored, locally and on network Data pipeline? How do I plan to extract, transform and load the data (ETL)?
INFRASTRUCTURE Cloud, On-premise, Hybrid. GPUs, CPUs or both? Single or distributed systems? Integration with languages, ent. apps/ databases.
QUESTIONS TO ASK YOURSELF
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G O V E R N M E N T -I S S U E D L A P T O P
P R O S :You’ve already got one
C O N S :Pretty slow for most deep learning applications
C O S T :Basically free
H I G H - E N D W O R K S TAT I O N
P R O S :Better performance than your laptop
C O N S :Not portable, still has limitations for large data sets
C O S T :$2k-$15k
E N T E R P R I S E G P U H A R D W A R E
P R O S :Best performance available
C O N S :Requires major infrastructure investment and IT support
C O S T :$50k-$250k
“ T H E C L O U D ”
P R O S :Most flexible for varying loads
C O N S :Slower data transfer reduces performance, limited for sensitive data
C O S T :Varies
CHOOSING THE RIGHT TOOL FOR THE JOB
I N C R E A S I N G P E R F O R M A N C E P E R F O R M A N C E V A R I E S
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Hypothesis for the business outcome you believe DL can solve
Current, needed Data – enough to train?
Current AI & DL skills
People training plan
Current IT Infrastructure(Cloud, On-premise)
ASSESS DESIGN & SELECT
Analyze data to train (e.g. text, video, images,
structure)
Plan research (Data Scientist) & deployment
models (IT Architect)
Select DNN Network, Libraries & Frameworks
TRAIN
Begin training
Feedback on outputs so the network can learn
Achieve training state that provides actionable
data for business decisions
Performance monitoring
DEPLOY
Optimization of trained DNN for deployment
performance
Move trained outcomes to inferencing platform
Begin inferencing (e.g. search, speak, translate,
classify, segment, predict, recommend)
Expand DL Training to adjacent areas
Performance monitoring
GETTING PREPPED FOR A DL PROJECT
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FACTOR NIMBIX/AWS/AZURE OEM SYSTEMS WITH GPUS DGX
WHY Get started quickly, pre-trained models
Meets your IT standards Full DL software stack required
HOW Work with Nvidia, Booz Allen and cloud provider
Get POC requirements from OEM Work with Nvidia & Booz Allen
DATA Where does data already reside & how much to move
Stays on premise Stays on premise
BUSINESS CASE Get started quickly at any scale without capital investment
Fastest path to get started with existing ITFastest way for data science team to do their work
DIY PROOF OF CONCEPTS
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FA Q : H O W D O I B U I L D T H E S K I L L S N E E D E D F O R A I ?
TRAIN YOUR PEOPLE FIRST,then hire or contract to fill the remaining gaps
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START SMALL AND PLAN FOR GROWTH
H O W S M A L L C A N Y O U S TA R T ?
▪ 1 Problem
▪ 1 Person
▪ 1 Laptop
▪ An Internet Connection
▪ Some Coffee*
*Optional, but highly recommended. Add free pizza for peak performance.
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“Put the thought of hitting right out of your mind! You can be a Master even if every shot does not hit.“
- Z E N I N T H E A R T O F A R C H E R Y
MEASURING THE SUCCESS OF NEW AI PROJECTS
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DON’T GIVE UPand
DON’T GETDISCOURAGED!
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THANKS YOU FOR YOUR TIME!Questions?
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AI BUYING CONSIDERATIONS
JENNIFER ARNOLDPrincipal
Booz | Allen | Hamilton JOSH ELLIOTDirector, Artificial Intelligence
Booz | Allen | Hamilton
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AI BUYING CONSIDERATIONSPROCUREMENT WILL LOOK DIFFERENT than before, because you need the solutions and the labor1
AI is a team sport, make sure you’re thinking about the RIGHT MIX OF STAFF2
Think through the right EVALUATION CRITERIA to get what you really need, not just what’s acceptable3
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THANKS YOU FOR YOUR TIME!Questions?
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CLOSING: WHERE SHOULD YOU GO FROM HERE
JOHN LARSONSenior Vice President
Booz | Allen | Hamilton
/johnwlarson
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COME SEE US LATER IN THE WEEKTUESDAY WEDNESDAY
PANEL: BETTER SERVING AMERICANS WITH AI1:30pm – 2:20pm, Atrium HallJ O S H S U L L I VA N , S E N I O R V I C E P R E S I D E N T
THE ROLE OF ARTIFICIAL INTELLIGENCE IN VIRTUAL WORLDS2:30pm – 3:20pm, Atrium Ballroom AD R E W FA R R I S , C H I E F T E C H N O L O G I S TN I R M A L M E H TA , C H I E F T E C H N O L O G I S TC A M E R O N K R U S E , L E A D T E C H N O L O G I S T
REVOLUTIONIZING CYBER WITH AI AND RAPIDS3:30pm – 4:20pm, Atrium Ballroom AJ O S H E L L I O T, D I R E C T O R O F A R T I F I C I A L I N T E L L I G E N C EA A R O N S A N T - M I L L E R , L E A D D ATA S C I E N T I S T RESISTING ADVERSARIAL ATTACKS ON MACHINE
LEARNING MALWARE DETECTORS1:30pm – 2:20pm, Hemisphere AJ A R E D S Y LV E S T E R , L E A D D ATA S C I E N T I S T
PANEL: HOW TO BUILD A DEEP LEARNING WORKFLOW @ THE WOMEN IN AI BREAKFAST 8:00am – 9:30am, Oceanic RoomC AT H E R I N E O R D U N , C H I E F D ATA S C I E N T I S T
ACCELERATING DETECTION AND ALERTING OF CREDENTIAL MISUSE NEAR THE EDGE11:30am – 12:20pm, Hemisphere AR A C H E L A L L E N , L E A D D ATA S C I E N T I S T
PANEL: ARTIFICIAL INTELLIGENCE FOR VIRTUAL AND AUGMENTED REALITY 11:30am – 12:20pm, Atrium HallN I R M A L M E H TA , C H I E F T E C H N O L O G I S T