WELCOME

JOSH ELLIOTDirector, Artificial Intelligence & Emcee (for today)

Booz | Allen | Hamilton

@JoshElliotDC

/JoshElliotDC

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

KEYNOTE: INTRODUCTION TO AI KIRK BORNEPrincipal Data Scientist

Booz | Allen | Hamilton

@KirkDBorne

/kirkdborne

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

LinkedIn

PERSONALIZATION

ARTIFICIAL INTELLIGENCE IS EXPLODING.

HYPEandreality….

ZDNet

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)

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?

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

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

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

PARTINGTHOUGHTS

Key AI Thought Pieces available for download @ www.boozallen.com/ai

CAPT. MICHAEL KANAANEnterprise Lead for Artificial Intelligence & Machine Learning,

HQ USAF Intelligence, Surveillance, and Reconnaissance

UNITED STATES AIR FORCE

COMPUTER LANGUAGE INITIATIVE

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

VELOCITY LAB: AI FROM ALMOST NOTHING

DAN PINESChief Innovation Officer, Naval Surface Warfare Center Indian Head EOD Technology Division

UNITED STATES NAVY

JOSH ELLIOTDirector, Artificial Intelligence

Booz | Allen | Hamilton

@JoshElliotDC

/JoshElliotDC

FACILITATED Q&A ON PROJECTS

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

MARGARET AMORIDirector, Artificial Intelligence

NVIDIA

SETH CLARKAI, Product Manager

Booz | Allen | Hamilton

GETTING STARTED WITH AI

MAY CASTERLINESolution Architect

NVIDIA

DNN GPU BIG DATA

THE BIG BANG IN AI

Inductive

Evolutionary Probabilistic

Kernel

Artificial Intelligence

Machine Learning

Deep Learning

Quantum Annealing

ASICs

HYPERSCALE HARDWARE

GPUs

RECAP: MACHINES CAN LEARN!

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

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

Is AIever

OVERKILL?

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

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

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

TRAINING▪Blogs

▪Tutorials

▪Online courses

▪Formal training

MODELS▪Kaggle

▪Github

DATA▪Kaggle

▪Github

▪Imagenet

LIBRARIES &FRAMEWORKS

▪Academia (Caffe)

▪Google (Tensorflow)

▪Microsoft (CNTK)

▪NVIDIA (CuDNN)

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

NO DATA, NO AI

https://youtu.be/Pj-qBUWOYfE

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

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

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

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

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.

“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

DON’T GIVE UPand

DON’T GETDISCOURAGED!

THANKS YOU FOR YOUR TIME!Questions?

AI BUYING CONSIDERATIONS

JENNIFER ARNOLDPrincipal

Booz | Allen | Hamilton JOSH ELLIOTDirector, Artificial Intelligence

Booz | Allen | Hamilton

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

THANKS YOU FOR YOUR TIME!Questions?

CLOSING: WHERE SHOULD YOU GO FROM HERE

JOHN LARSONSenior Vice President

Booz | Allen | Hamilton

/johnwlarson

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