Earley Executive Roundtable on Data Analytics - Session 1 - The Business Potential of Machine Learning & Cognitive Computing

Copyright © 2015 Earley Information Science1 Copyright © 2015 Earley Information Science
Earley Executive Roundtable
Series on Data Analytics
Session 1: Business Potential of Machine
Learning and Cognitive Computing
May 27, 2015
Presented by
Seth Earley
CEO
Click to watch the
recording of this session

Copyright © 2015 Earley Information Science2
Today’s Agenda
• Welcome & Housekeeping
– Session duration & questions
– Session recording & materials
– Take the survey!
• Introduction – Seth Earley
• Panelist Introductions
– Bruce Daley Principal Analyst, Tractica (@brucedaley)
– Olly Downs, Chief Scientist/CTO, Globys (@globysinc )
– Mitchell Shuster, Data Scientist, Knowledgent (@Knowledgent)
– Patrick Heffernan, Practice Manager, TBR (@TBR_PatrickH)
• Panel Discussion
• Questions & Answers

Seth Earley, Founder & CEO, Earley
Information Science
seth@earley.com
@sethearley
• Over 20 years experience in data science and technology, content and knowledge
management systems, background in sciences (chemistry)
• Current work in cognitive computing, knowledge and data management systems,
taxonomy, ontology and metadata governance strategies
• Co-author of Practical Knowledge Management from IBM Press
• Editor of Data Analytics Department IEEE IT Professional Magazine
• Member of Editorial Board Journal of Applied Marketing Analytics
• Former Co-Chair, Academy of Motion Picture Arts and Sciences, Science and
Technology Council Metadata Project Committee
• Founder of the Boston Knowledge Management Forum
• Former adjunct professor at Northeastern University
• Guest speaker for US Strategic Command briefing on knowledge networks
• AIIM Master Trainer – Information Organization and Access
• Course Developer and Master Instructor for Enterprise IA and Semantic Search
• Long history of industry education and research in emerging fields

Machine Learning and Cognitive Computing
Core Concepts

Machine Learning - The detection of patterns and surfacing of information through a
variety of approaches based on statistics and mathematics
A search index is a derivation of
structure from unstructured
information (clustering, classification,
entity extraction and various text
analytics approaches use machine
learning approaches)
Advanced search algorithms detect
“signals” from users’ intent and past
search patterns to increase the
relevance of search results

Machine Learning - The detection of patterns and surfacing of information through a
variety of approaches based on statistics and mathematics
“More like this” and “users who liked
this also liked that” types of results
leverage machine learning algorithms
Systems that classify documents
based on “training sets” use analytical
methods to create mathematical
representations of content and
documents
Personalization – content, search
results or product recommendations
are all based on a system for
“predicting” what you are looking for.

Machine Learning and Cognitive
Computing
Siri answers your questions about movie
times, sports scores, restaurants nearby
Cognitive Computing - A way for computers to be more user friendly and “understand”
what humans want
Watson answered tricky and ambiguous
trivia questions with obscure references,
puns, metaphors, time references, slang,
idiomatic expressions and other
challenging types of ambiguous queries
Interpreting signals - what a user is
looking for in a query, interpreting
questions asked in plain English (natural
language), engaging in a dialog,
“understanding” the meaning of an
ambiguous question, anticipating the next
step in a process
Pattern recognition,
pattern matching and
rules for predicting
outcomes

Machine Learning and Cognitive
Computing
Artificial intelligence
encompasses all of these tools
and techniques to solve various
types of problems – from
writing articles to driving cars to
detecting fraud, diagnosing
disease, making decisions that
have previously been in the
realm of human judgement

Today’s Panel of Experts
Bruce Daley, Olly Downs, Mitchell Shuster, Patrick Heffernan

Bruce Daley
• Contributor to Tractica’s Automation & Robotics practice with
focus on artificial intelligence and machine learning for enterprise
applications
• Previously, vice president and principal analyst with Constellation
Research covering business research themes related to
customer relationship management, mobility, and infrastructure
• Also, founder of Great Divide, co-founder of Rabbit Ears Capital
Advisors, founder of Test Common Inc., founder of the Enterprise
Software Summit, and founder of The Siebel Observer, the
largest publication devoted to Siebel Systems
• Additionally, held consulting and management roles at Oracle
and Bain & Company
• Widely quoted industry expert in major publications including The
Wall Street Journal, The New York Times, The Financial Times,
The International Herald Tribune, IEEE Spectrum, The San Jose
Mercury News, and many more.
• Author of a soon-to-be-published book on data storage, Where
Knowledge is Power, Data is Wealth
• Holds a BA from Tufts University
Principal Analyst
Tractica
@brucedaley

Ad Services Automotive Agriculture Finance Data Storage
Education Investment Health Care Legal Manufacturing
Media Medical Oil and Gas Philanthropy Retail
• Self driving cars
• Self parking cars
• Diagnostics
• Control plane
• Watson
• Credit scoring
• Fraud detection
• Personalized
geo location
• Intelligent
agents
• Forecasting
• Fraud detection
• Rotor position
estimation
• Predicting
electricity prices
• Optimizing milling
parameters
• Testing
mathematical
theorems
• Grading exams
• AI tutors
• Gamification
• Writing
sports stories
• Storytelling
analysis
• Analyzing seismic
data
• Estimating size
oil reservoirs
• Determine min
gas miscibility
• Fundraising
• Optimized giving
• Smart charity
• Moral AI
• Fraud detection
• Malignant
pleural
mesothelioma
• Orthodontic
diagnosis
• Lung CT classify
• Clinical trial
compliance
• Adverse drug
reaction
prediction
• Crop planting
optimization
• Develop drought
tolerant crops
• Self driving tractors
• Electronic
discovery
• Patent
infringement
analysis
• Contract
• Program trading
• High frequency
trading
• Algorithm trading
• Index arbitrage
• Personalized ad
serving
• Ad portfolio
optimization
POV – Bruce Daley
My point of view – after years of false starts, the amalgamation of statistics, GPU chips, deep
learning algorithms, and big data has made narrow applications of AI practical. The only limit to
the problems they are being asked to solve to seems to be the human imagination
New Algorithms
GPUData
DEEP
LEARNING
Probability and Statistics

Dr. Olly Downs
• Responsible for the analytics strategy, technical approach and
algorithm design and development for Globys’ marketing
personalization technology platform (Amplero).
• A machine learning scientist and serial technology entrepreneur,
credited with bringing advanced analytics and machine learning
methods to bear as the creative spark behind numerous early-stage
technology companies.
• Specializes in applying abstract analytical ideas from mathematical,
physical and statistical science to problems in the real world and
commercializing them into significant businesses.
• Recently served as Chief Scientist at Atigeo, Chief Scientist at
Mindset Media (sold to Meebo, February 2011) and Director of
Research at Pelago (sold to GroupOn, April 2011).
• As Principal Scientist at INRIX, the first technology spin-out from
Microsoft Research, delivered a world-first in the provision of real-
time traffic information using a nationwide network of GPS-enabled
probe vehicles.
• Holds Ph.D. and MA degrees in Applied & Computational
Mathematics from Princeton University, and BA, MA and MSci
degrees in Experimental & Theoretical Physics from the University
of Cambridge, UK.
Chief Scientist/CTO
Globys
@globysinc

Olly Downs – POV
• Successful adoption of Machine Learning (ML) and Cognitive Science to
drive business value is split across 3 segments
– Large businesses for which these capabilities are core to their business
– Businesses for which these capabilities are strategic and that can invest in team and
tools
– Businesses for which these capabilities are valuable but inaccessible
• “Chasm” between 1 & {2,3}
– Getting business-impacting results and operationalizing is difficult
– Processes are unwieldy, and even best practices with teams and tools move slowly
i.e., weeks, months
– Hiring and retaining people with the right skills is not easy (as #1 consumes them)
• Proposition for ML and Cognitive Computing to “Cross the Chasm”
– Add value without hands on intervention – discover and act without human experts
– Inform and educate on what is discovered
– Reduce upfront investment hurdle
[ 13 ]

Example – Applying Machine Learning to
Marketing
[ 14 ]
Plan Development Launch
Traditional
Campaign
Process
Test
Optimize Design
Test
Analyze
Analyst/
Data Scientist
Plan Development
Launch
Campaign
Process
With Amplero
Discover
“Team & Tools” Approach:
Weeks of Design and Configuration
10’s of Marketing Contexts
Weekly Analysis
One-time Optimization
BI Team Researching Results
Machine Learning Approach:
Configuration in Minutes
1,000’s of Marketing Contexts
Daily Discovery
Continuous Optimization
BI Team Researching New Revenue Opportunities

Mitchell Shuster
• Award-winning data scientist, physicist, and technology
entrepreneur who seamlessly blends analytic and research
knowledge honed in the academic realm with real-world technical
and industry expertise.
• As Informationist and Data Scientist at Knowledgent, the data and
analytics firm, specializes in applying advanced analytics and data
science concepts and techniques, including machine learning
(Regression, Neural Nets, SVMs, Clustering, PCA, Anomaly
Detection, etc.), to help client organizations gain actionable insights
and competitive advantage.
• Currently leveraging predictive analytics expertise to deliver data-
driven models that improve patient outcomes, decrease costs, and
increase operational efficiency for healthcare and life sciences
organizations.
• Previously in Research & Development at Intel Corporation,
designed and developed basis for Intel's worldwide high-volume
manufacturing at the newest technology node and was recognized
for computational modeling and process implementation.
• Earned Ph.D. degree in Physics and multiple research fellowships
from Penn State University, where he authored research published
in multiple prominent peer-reviewed scientific journals, and BA
degree in Physics from Cornell University.
Informationist and
Data Scientist
Knowledgent
@Knowledgent

POV – Mitchell Shuster
• Machine learning is a powerful tool for extracting
information from data
– It is non-trivial to frame the questions, prepare the data, and interpret
the result in context
– The right data is required to answer a given question
– “Machine learning” is not a magic wand to solve all problems
• Cognitive computing is an extension of compute
capabilities into more human-like interactions
– The primary distinguishing characteristics are context awareness
and tolerance of ambiguity
– At present, limited to specific tasks and contexts
Beware the hype! What is possible is not always practical.
What is practical is not always desirable.

Patrick Heffernan
• Coverage and Focus Areas include IT Services, management
consulting, global delivery, strategy and operations, cloud,
intelligence cycle, project management, and client engagement
• Directs the practice’s syndicated portfolio and cultivates and
manages projects on topics ranging from management consulting
to firms’ financial advisory services to emerging technologies.
• Expertise in competitive intelligence, strategy, and global political-
economic impacts on business cycles and consulting vendors.
• Prior to joining TBR, was part of a Big Four firm’s competitive
intelligence team, conducting field work and analysis.
• Professional career started in diplomacy, with Middle East postings
as a foreign service officer with the State Department and
counterterrorism assignments with the National Security Council
and the U.S. Department of the Treasury.
• Received a B.A. from Washington and Lee University and an M.A.
in foreign affairs from the University of Virginia.
Practice Manager and
Principal Analyst,
Professional Services
Practice
Technology Business
Research
@TBR_PatrickH

POV – Patrick Heffernan
• Cognitive computing and machine learning will
increasingly have business impacts on ---
– IT services vendors, including Accenture, Infosys, Wipro, IBM, and
Cognizant, as those vendors must invest in people and capabilities
to keep pace with competition and grow in new areas -- and these
vendors are afraid of being too slow, too late, or too “me-too” for the
market;
– clients who appreciate the potential of what the vendors listed above
can deliver, but don’t know how disruptive those changes will be –
these companies are afraid of being too aggressive in adopting
emerging technologies and paying a premium for what will soon be a
commodity; and
– employees at IT services vendors and at their clients who fear losing
their jobs to “robots” – this is a recurring fear when emerging
technologies take root, but just because it keeps coming up doesn’t
mean it isn’t real

Discussion
• OK, interesting stuff - where do I get started?
• How do I tell what is possible from what is practical and achievable for
my organization?
• What kinds of problems can I solve?
• What is the difference between “deep learning” and “machine learning”?
• What kinds of education does my team need? Where do I get it?
• What are the industries and applications that are most mature?

Thank you to our sponsors/producers
www.computer.org/itpro
http://www.henrystewartpublications.com/ama
www.informationdevelopmentworld.com
www.thecontentwrangler.com
http://www.tbri.com

For more information
• IT Professional Magazine - www.computer.org/itpro Next issue focuses on Analytics
• Computing Edge http://www.computer.org/web/computingedge (highlights of IEEE
publications)
• Cognitive Computing and Big Data Analytics by Judith Hurwitz, et al
http://www.amazon.com/Cognitive-Computing-Big-Data-Analytics/dp/1118896629
• Artificial Intelligence for Enterprise Applications https://www.tractica.com/research/artificial-
intelligence-for-enterprise-applications/ (contact sales@tractica.com mention roundtable to
get 10% discount)
• Microsoft- Machine learning blog:
http://blogs.technet.com/b/machinelearning/archive/2015/05.aspx
• McKinsey- AI for the C Suite
http://www.mckinsey.com/insights/strategy/artificial_intelligence_meets_the_c-suite
• Stanford course in machine learning https://www.coursera.org/course/ml
• Data science and machine learning resources: http://conductrics.com/data-science-
resources/
• Video lectures: http://videolectures.net/Top/Computer_Science/Machine_Learning/

Mining Business Insights with Big Data Analytics and the
Internet of Things
Joanna Schloss
Business Intelligence and Analytics
Evangelist, Dell Software
John Spooner
Vice President, Platforms,
Technology Business Research, Inc.
Ram Sangireddy
Dir of Product Management, Predictive
& Analytics, Vitria Technology
Bruce Daley
Principal Analyst,
Tractica
Next Session: June 3rd 1pm EDT

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Twitter: @sethearley
LinkedIn: www.linkedin.com/in/sethearley

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Earley Executive Roundtable on Data Analytics - Session 1 - The Business Potential of Machine Learning & Cognitive Computing

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