Usama silicon-slopes-summit-20190131

Deploying AI Technology that Works
AI Hype vs. Reality: Lessons Learned for
Pragmatic AI in the Enterprise
Usama M. Fayyad, Ph.D.
Co-Founder & CTO, OODA Health, Inc.

Copyright Usama Fayyad © 2019
The use of computers to “simulate” human intelligence
• Very old concept
• Dates back to the 1940’s and 50’s (Alan Turing)
• Defining “intelligence” is an open problem
• “Common Sense Reasoning” is still an open problem
What is Artificial Intelligence?

First generation Artificial Intelligence
1950’s to 1970’s – AI Winter I: mid-1970’s
Major underestimations of the
complexity of:
• Common sense reasoning
• Natural language understanding
• Mathematical logic was only useful in
theory but not practical for computers
• Extremely narrow problems, with
limited or no need for common sense
reasoning
• Many practical applications did work in
very narrow verticals:
• Expert systems, scheduling in
manufacturing, travel bookings, etc.
• Game players – chess, checkers, etc.
Where did the first wave fail? Where did the technology work?

Second generation Artificial Intelligence
1980’s to early 1990’s AI Winter II: early-1990’s
• Common sense reasoning still proved to be
too challenging
• “Natural Language Understanding”
continued to disappoint
• Severe under-estimation of the complexity
of “Machine Vision”
• Probabilistic reasoning did not scale
• The problem was worse: it seems like
we needed a ”complete understanding”
of the world to do any of these tasks
• Spatial Reasoning turns out to be a very
hard, and essential, problem to solve
What did we learn?Where did the second wave fail?

• Games are an example of this
• Business and engineering tasks are examples of this
• Many practical applications did work in very narrow verticals
• Fuzzy logic controllers
• Automation in manufacturing – factory robotics
Lesson 1 for Pragmatic AI in Enterprise
Reduce the problem domain to one where “complete knowledge” is possible

How about “big problems” like Machine Vision in Commerce
• Look for simplifications - recognizing objects is too hard?
• Avoid image analysis completely
too hard to figure out items
in a basket?
Any relation to how
humans see?
Lesson 1 for Pragmatic AI in Enterprise
Reduce the problem domain to one where “complete knowledge” is possible

What about the hype around AI in the 1980’s?
None of the initial hype
delivered – resulting in the
AI winter in the 1990s and
2000s
But predictions were off
and instead of jobs being
replaced, high value jobs
were created
• Major hype in the 1980’s – AI
was going to solve all
problems and change the
world
• U.S. was afraid of Japan AI
program – 5th Generation
Systems
• High-value jobs in
automation/digitization
• Digital transformation of
business started
• Productivity tools created
whole new classes of jobs:
• Excel jockeys
• Accounting systems

Sound like familiar hype?
• We are all going to be useless
• Jobless
• Brainless
• China 2030 AI is the new Japanese 5th Gen
• Major hype in the 1980’s – AI was going
to solve all problems and change the
world
• U.S. was afraid of Japan AI program –
5th Generation Systems

Machine Learning survived
both AI winters

A subset of AI concerned with machines learning/modifying behaviors based on experience (inputs)
Early true success: Arthur Samuel’s Checkers player at IBM (1955-1959)
Lots of hype on neural networks in 1980’s
• Turns out to be “just non-linear regression”
• Major insights on learning as an optimization problem
Significance of Samuel’s ML Proof?
• Samuels’s Checker player learned to play by playing games
• It “learned” by adjusting a scoring function that allowed it
to prefer some moves over others – started beating
Samuel himself!
• Fed it all championship games in simulation – the machine
started playing at championship levels
• This type of success was repeated in many “data analysis”
tasks
What is Machine Learning?

The only field of AI that gained serious traction during the AI Winters – because data became available,
and was growing exponentially
Bypassed the issues of emulating human thinking or common sense reasoning, by emulating patterns in
data recording desired outcomes (supervised learning)
Classification learning became a huge application
• Became critical in search engines (MLR – Relevance Feedback)
• Many problems solved: face recognition, controls, science data analysis, predictive maintenance, etc.
Neural Networks of early 1980’s coming back under the name DEEP LEARNING
“We don’t have better algorithms. We just have more data.”
-Peter Norvig, Google
Machine Learning Survived both AI Winters

Neural Networks and Deep Learning

Mostly Dead
ML
ML
ML
Mature
Slow
[Peter Norvig, Google]
AI Redux

Data and AI Strategy
1. The Data is the main asset and driver
2. AI/ML cannot work effectively without Data
3. AI/ML can enhance Data assets if you use the right AI platform
4. Your Digital Transformation must be linked to
• Your Data Strategy
• Your AI Strategy
5. Get clarity on the Cloud strategy for Data and for Compute
• Logical cloud computing is a must
• Hybrid cloud is pragmatic solution
• Think through the Edge to the Cloud
6. Remember to always focus on narrow domains when using AI

Serious presence and opportunity
• Recommender systems in retail, search completion, Chatbots for completing
text, simple question answering…
• Interesting: Erika, Mathematica - Embarrassing: Microsoft Tay
• Promising/Overhyped: IBM Watson, Powerful: IBM Deep Blue
• Useful: Map routing/traffic models, Search Technology, Simple home
controllers, radiology readers, mobile apps with situational awareness
Many Practical & Useful Applications (1)

Serious presence and opportunity
• Promising: Facial recognition, Alexa with 40K skills and growing, but don’t try to have a
real conversation…
• Annoying: Ad tech that bombards you with ads after you search or shop
• Lots of promise and big bets:
• Autonomous driving
• Financial advisory
• Drug discovery, scientific knowledge compilation, automation of tedious skills in
manual ops
Many Practical & Useful Applications (2)

So what do we do about getting the Data we need?
How do we deal with the volume, velocity & Variety of all this Data
New big data technology comes to the rescue
• Mostly open source
• Makes it easy to deal with many data types, especially unstructured Data
-Peter Norvig, Google
We said successful AI is about ML + DATA
big data: is a mix of structured, semi-structured, and unstructured data
• Typically breaks barriers for traditional RDB storage
• Typically breaks limits of indexing by “rows”
• Typically requires intensive pre-processing before each query to extract “some structure” – usually using
Map-Reduce type operations

Why was this technology developed?
• We needed to count keywords in documents… billions of docs?
• Why?
• Because that’s what Search Engines needed to rank relevance
of a doc to a “search phrase”?
• Everything is bag of words with counts of occurrences…
What was the earliest “big data” machine?
big data How did it all start?

Herman Hollerith
Won the 1888 Census Bureau competition for fast
counting
Formed a company, called:
The Tabulating Machine Company
Merged with 3 other companies into:
Computing-Tabulating-Recording Company
After rebranding? The name changed to:
What was the first big data Machine?

Lesson 2 for Enterprise “Practical AI”
Data is a huge enabler of Practical Applied AI -- so make sure the data is
captured and managed as an asset
• In most companies, Data is a liability and not an asset
• Need to keep Data under control (well-governed), else:
big data will rapidly create a Big Mess
• Need to make sure you have a story for the variety of data
• Social media
• Documents
• Customer service recordings (audio)
• Video assets
20

We said it is all about the Data
Without the right Data, forget about AI/ML
“We don’t have better algorithms. We just have more
data.”
[Peter Norvig, Google] So what are you doing with all this data?

What has really changed things in commerce?
The Data collected is going beyond the details of Transaction
o Basics: The “Classical” World:
o What? When? Where?
o How much?
o What payment method?
o Context: what is the context of this transaction?
o Where are you?
o What is around you?
o Are you moving? How fast? Acceleration?
o Temperature? Ambient sounds?
o What is the exhaust? What is the spectrum around you?
o Graph: understanding location and surround… and intent
o What is Near you?
o Who is around you?
o Where are you heading to? What’s on your calendar? What’s on the way?
o What are you doing? Biometrics? Health? Emotional responses?

Data: Key to restoring the lost customer intimacy in the digital
banking era
100 years ago
Front office was intimate
Personal knowledge
Direct interactions
Staff knows all that is happening with client and family
Personalized service automatic
Now
Back office was simple
Easy to understand risk
Easy to score and set limits by intuition
KYC trivially easy and natural
Controls straightforward
Front office has no knowledge or intimacy
More complex product line
Data silos and high latencies
No unified view of the customer
Personalization a challenge
Poor understanding of customer
Back office is complex
Difficult to scale because tech did not evolve
No culture of automation
Risk and Finance expensive hard to manage because the
data is a mess and hard to access
Controls a challenge
Front
Office
Back
Office

Why data is important: Every customer interaction in an
opportunity to capture data, learn & act
An instant car loan
product offering is
displayed in the app
Connie logs into BMB to
check her balance. From
browsing behaviour we know
she seeks a car loan
CRM data is used to
pre-calculate Connie’s
borrowing limit for a
car loan
Using internal/external data
sources, predictive models,
identify cross-sell opportunities
Connie is offered a
competitively priced
‘bespoke’ offer for car
servicing / MOT
Connie’s journey is
enhanced based on
previous multivariate
testing results
User experience is continuously,
iteratively improved by capturing
user interaction in real-time every
session
Connie has a personalised
journey based on
calculated limit for the car
loan amount
Data Capture/
Opportunity to
learn
Actions
Customer Interaction Event (branch, telephony, digital, mobile, sales)
Millions of customer
interactions per day
Customer
Interaction
CRM Predictive
Analytics
Multivariate
Testing
Targeted offers
during browsing
Product Discovery Cross-sell
Measure feedback
per session
Every
interaction is an
opportunity to
capture data, to
learn and to act
Big Data
platform
Imagine we could move back to this model 100 years ago – on demand, consumable, understandable
information to build intimate relationships & an understanding of the customer

AI/ML expects data is certain formats, so the problem is making sure it is
convenient to leverage the data quickly and experimentally
• In most Digital Transformations, Data is still an after-thought
• Organizations thus digitize the operations, but leave the data in a mess
• Makes it impossible to use the data for AI/ML
• Recall, the tricks for making AI work are:
• Very narrow application domain
• Within that domain you have complete ”knowledge” – i.e. ”Complete Data” for that domain
• How do you leverage this data effectively?

Big Data Platform (Data-as-a-Service)
Central Data Fusion
Engine
Ingesting, persisting,
processing
and servicing in Real-
time.
Analysis
Transactions
Trade
Network Traffic
Financial CrimeCyber Security
big data
Stack
Trade
Customer
Interactions
Application Logs
Social Data

Big Data Platform (Data-as-a-Service)
Central Data Fusion
Engine
Ingesting, persisting,
processing
and servicing in Real-
time.
Analysis
Transactions
Trade
Network Traffic
Financial CrimeCyber Security
Trade
Customer
Interactions
Fraud Marketing RiskFinancial Crime
Application Logs
Social Data
Big Data
Stack

Copyright OODA Health, Inc. © 2019
Why use EHR information?

The ability to identify, leverage, analyze and use information from a
huge data lake via AI and deliver actionable input to enhance
intelligence, trends, patterns, controls and procedures for overall
improvement of the cyber posture.
Data Fusion

AI operations should leverage understanding intent → understand
who the actors are and what they want achieved
• This is especially true for Chat bots
• A ”conversation” without “understanding” is a futile exercise
• If your AI platform builds a “model of Actors” and a model of “Actor intent”, then this can
feed into your data assets and enrich your ability to leverage the data correctly
• Makes the difference between a digital transformation and “Smart Digital
Transformation”
• Enriching the data, helps enriching other applications
• Starts a valuable virtuous cycle
• Enables ““Intelligent Decisions & Actions” - e.g. Achieves “Customer Intimacy”

THREAT CENTRIC
The Traditional Approach To Cybersecurity
DIGITAL
ACTIVITY
EASY TO CLASSIFY EASY TO CLASSIFYHARD TO CLASSIFY
“BAD”“GOOD” ALERT
DETERMINATION
A LACK OF
CONTEXT
• Trusting static policies in a
dynamic environment
• Decide what is good or bad at a
single
point in time
• Configure your defenses to stop
the bad from entering and allow
the good to pass through

‣ Detect individuals interacting with system
that post the greatest potential user risk
‣ Rapidly and anonymously understand
potential risky behavior and context
around it
‣ Decide what is good or bad based on how
users interact with your most valuable
data
‣ Continuously revisit your decisions as you
and our machines learn
BEHAVIOR CENTRIC
A New Paradigm: Human-centric Cybersecurity
“BAD”“GOOD”
PROVIDE CONTEXT
TO MAKE OPTIMAL
SECURITY DECISIONS
DIGITAL
ACTIVITY
ALERT DETERMINATION

Pragmatic enterprise
Artificial Intelligence
Lesson [5]
There is no autonomous AI, there is no general AI → It is about Hybrid AI where AI
systems help humans perform much of the low-level work quickly and accurately

SystemLayer
Big Data Platform (Hadoop - Scoop, Scala, Hive, Hue), SAS, Tableau, Solr, etc
Single Sanctions
Payment View
Centralised Watch
List Depository
Single Customer
View Centralised
SAR Depository
Enabling a holistic view
of Financial Crime Risk
Enhanced
Financial Crime
Intelligence capabilities
Data&Analytics
Layer
Customers
(Due Diligence, ID&V,
Accounts, Risk Rating)
Transactions
(Wire Transfers,
Checks, Trades)
Payments
(Cross-Border,
Domestic, IBAN,
Internet)
Data
Detection/In
telligence
Court Orders
Fraud Data
Use Case 2: Financial Crime Intelligence - Big Data Capturing Data Exhaust
• Statistical & Threshold-based
alerting
• Early warning & notification
• Near repeat pattern analysis
• Load forecasting and cyclical
patterns
• Behavioral targeting
• Ability to handle billions of records
• Trending and intelligence
dashboards
• Real time access to data
• “information fabric” integrates
heterogeneous data and content
repositories
• Terrorism Activity
• Human & Drug Trafficking
• Standard Financial Crime Reports
(SAR, STR, CTR)
• Sanctions Circumvention
• FinCEN & other Regulatory Reports
• Law Enforcement, Industry Forums
• Fraudulent
transactions
• Hawala
• Money Laundering
• E-Commerce Fraud
• Online Bank Theft
• Inside TradingSanctions, ABC & AML
Policies and Standard
Regulatory Agencies
and Law Enforcement

Welcome to the IoT
Billions of devices that are able to communicate
• Graphic credit: Accenture 2016
What will these devices say to each other?
✓What will they say to you?
✓What will they report to their owner?
✓What will they “understand”?
What is identity in this world?
✓How will they recognize “you”?
✓How do they identify each other?
✓How will they know who “controls” them?
The New Frontier of Cybersecurity
✓Where is the perimeter?
✓Who controls what?
Graphic courtesy Accenture

Pragmatic enterprise Artificial Intelligence
Lesson [3]
AI/ML expects data in certain formats,
so the problem is making sure it is
convenient to leverage the data quickly
and experimentally
Lesson [4]
AI operations should model the processes and
aim at understanding intent → understand the
actors and what they want achieved
Lesson [5]
There is no autonomous AI, there is no general AI → It is about Hybrid AI:
systems help humans preform much of the low-level work quickly and accurately
36
Lesson [1]
Reduce the problem domain to one where
“complete knowledge” is possible
Lesson [2]
Data is a huge enabler of Practical Applied AI –
so make sure the data is captured and
managed as an asset

A PROVEN TEAM
• Experienced healthcare entrepreneurs: Castlight Health (NYSE: CSLT) , RelayHealth
(acquired by McKesson), athenahealth (NASDAQ: ATHN)
• Big data / AI leadership: Microsoft, Yahoo!, NASA / JPL, Barclays
• Board members and advisers include Scott Serota (BCBSA), Ken Goulet (ex-Anthem)
• Investors include Oak HC / FT , DFJ
Who is OODA Health?

Runaway administrative costs waste
hundreds of billions of dollars and
result in a poor experience for all
involved.
Billing and insurance-related
complexity in the US healthcare
system is responsible for $400 billion of
waste
1975 2010
Physician
growth
(+150%)
Admin
growth
(+3,200%)
The problem

Administrative Costs (Per Claim) for Physician Billing and Insurance-Related Activities
Cost of reimbursement Admin time for reimbursement
Source: JAMA, February 2018 Confidential – not for distribution
Billing and insurance-related administration takes
time and money away from care
40

Improving the administrative experience is not simply a provider or payer problem, but requires
a collaborative approach on both sides
A vicious cycle of expense and delays
Confidential - not for distribution

42
Our vision

One provider
+
One episode
+
Two services
Two confusing bills
One angry patient
43Confidential – not for distribution
Current billing practices hurt patient NPS

44
Real-time payment of
payer liability
Immediate, guaranteed
payment of patient liability
The OODA vision involves two key
components
Providers
drastically reduce or eliminate admin,
billing, and collections follow-up after a
patient visit
Members
receive immediate, streamlined
bills and can easily access credit,
payment plans, and other perks
Payers
save money on admin costs, better
forecast reserves, and offer a
differentiated experience to members
and providers

45
Real-time “claim”
estimation/
generation
Immediate,
payment of
patient liability
Patient
liability
management
Complete
real-time
payment
Real-time
payment of
payer
liability
Real-time auto-
adjudication
Payment gateway, payment rails,
financial and billing systems,
collections management, financing
options, member experience
Will likely involve both technology (EMR integrations,
adjudication capability) as well as contract, policy, and plan
design changes, and process flow reengineering
A retail experience requires real-
time payments
Retail payments
experience

Quality feedback loop requires going beyond the claim
Providing high-quality care requires a
comprehensive set of data...
•Full patient detail
•Full provider detail (billing, rendering, ordering)
•Date and time of service
•Reason for visit
•Visit type
•Vitals
•Procedures performed (code, description, date/time, result/observation)
•Conditions diagnosed (or confirmed)
•Drugs administered
•Full medication list (Rx, etc.)
•Orders (procedures, labs, imaging, other)
•Treatment plan
•Future appointments
•Prior medical history
•Family history
•Prior lab or imaging results (actual result values)
•Prior treatments (medications, physical therapy, specialist visits, etc)
...but most of these fields are missing
from a standard claims set

A final thought...
“Like many other observers, I look at the U.S. health care system
and see an administrative monstrosity, a truly bizarre mélange of
thousands of payers with payment systems that differ for no
socially beneficial reason, as well as staggeringly complex public
system with mind-boggling administered prices and other rules
expressing distinctions that can only be regarded as weird.”
– Henry Aaron, Economist

Usama Fayyad
www.linkedin.com/in/ufayyad
Email: Usama@ooda-health.com
Assistant: Geraldine.Anson@ooda-health.com
Thank You! & Questions
Twitter – @Usamaf

Interested in
joining OODA?
We’re hiring!
Apply: ooda-health.com
Email: info@ooda-health.com
: @oodahealth
: @oodahealth
: OODA Health

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