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With recent advances in cognitive computing, computers may soon be able to help experts make better decisions by making sense of unstructured data. Systems are being trained today to sense, predict, infer and, in some ways, think. Learn about recent advances in cognitive computing and ways it can help you improve business decision making.
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The Dbriefs Technology Executives series presents:
Cognitive Computing: Can Computers Learn from Experience?Rajeev Ronanki, Principal, Deloitte Consulting LLPDavid Steier, Director, Deloitte Consulting LLP
November 7, 2013
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Cognitive computing introduction
How is it different from traditional computing?
Structured vs. unstructured data
Cognitive analytics
Case studies
Convergent solutions
Agenda
Follow @MarkAtDeloitte or #cognitivecomputing during the webcast
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Are you familiar with any of these new cognitive computing approaches like IBM’s Watson, Numenta and Google Now?
• Had not heard of them prior to this webcast• Have heard of some of these in the news• Have followed them but have not thought of them as
representing a new class of computing• Have used some of these tools and look forward to more• Don’t know/Not applicable
Poll question #1
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Historical timelineThe evolution of Cognitive Computing
• Turing Test published: a computer that exhibits intelligent behavior equivalent to, or indistinguishable from, that of a human.
• Scientific community focuses on machine translation
• Scientific community focuses on AI• First successful NLP systems• MYCIN diagnosed infectious blood
diseases
• Semantic classification & probabilistic parsing are combined in machine systems. Can derive rules and their probabilities
• First commercial database management system tracks huge amount of structured data for Apollo Moon Mission
• Machine learning algorithms for language processing introduced
• Judea Pearl brings probability and decision theory into AI
• Watson. Question –answering system capable of answering questions posed in natural language
• TAKMI (Text Analysis & Knowledge Mining) developed to capture and utilize knowledge embedded in text files – applied to call centers
• TAKMI provides insights on patient groups to help doctors treat groups of patients at a time
• Watson: IBM, WellPoint, Memorial Sloan Kettering use Watson to give doctors treatment options in seconds
• World’s first single molecule computer circuit
• The High Performance Computing Act of 2004 was enacted
• IBM Content, Predictive and Streaming analytics
• Streaming analytics process 5 million messages of market data per second to speed up financial trading decisions
1950’s
1960’s
1970’s
1980’s
1990’s
2000’s
1997
2001
2004
2007
2009-2010
2013
Current
Over the decades…The last 10 years…
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Cognitive computing
• Emulates strengths of the human brain, including parallel processing & associative memory
• Enables natural language processing of structuredand unstructured data.
• Understand/leverage big data in real time• Use machine learning to develop context-based
hypotheses
Basics Current Investments
Cognitive Computing can push past the limitations of human cognition, and connect the dots between big data, enabling more informed decisions. A couple of industry examples include:
Academic Commercial Consumer
The development of computer systems inspired by the human brain
Financial Services Implications:Advise in trading, and help identify financial fraud cases
Healthcare Implications:Incorporate all new medical evidence, individual patient histories, and eliminate geographic constraints.
Potential applications of cognitive computing
Boltzmann Machine
Never-Ending Language Learning (NELL)
Saffron Natural
Intelligence Platform
Facebook AI Group
Kngine
IBM Watson
Numenta
Google Now
Apple Siri
Copyright © 2013 Deloitte Development LLC. All rights reserved.
How is it different?
• Sequential processing
• Driven by programming language
• Not real-time
• Predefined logic
• Static business rules
• Passive
• Defined input parameter
• Event driven
• Machine learning and natural language based
• Parallel processing different sources at the same time
• Context driven
• Dynamic learning algorithm
• Sensory & mobile based
• Continuous collection and feedback
Traditional Computing Cognitive Computing
User Interface
Application Layer
Processing Platform
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Cognitive computing architecture
Analytic Solutions
Data & Analytics Platform
Content Lifecycle Services
Data Corpus Core Engine
Con
tent
Sou
rces
Extract Ingest Discover
Curation Services
Ingestion Services
Enrichment Services
Search Indices
Semantic Models
Derived Knowledge
Question Analysis
Hypothesis Generation
Evidence Scoring
Final Merge and Rank
Cognitive Analytics Applications
Natural Language Processing (NLP) Stack
Machine Learning Modules
Computing Resources (Cloud/On-Premise)
Modeling & Processing Engine
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Do you see a role for adopting cognitive computing technologies into your business?
• Still unclear as to how these fit into our strategy• We are tracking these developments but no plans yet• Beginning to develop a roadmap in some areas• Have active plans or projects beginning now• Don’t know/Not applicable
Poll question #2
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Not all data is created equal
Structured data is being rapidly augmented by unstructured data
Structured Data
Email & blog contentVideo & social contentPatient records10-Ks & public filingsIndustry reports & research journals
Unstructured Data
Transaction & CRM dataResearch & market dataMainframe dataPoint-of-sale data
AnalysisAnalysisHypothesis
Generation & Scoring
Hypothesis Generation &
Scoring
Final Evidence & Scoring
Final Evidence & Scoring
Initial Question
Initial Question
Final Insights
Final Insights
Advanced processing capabilities such as Natural Language Processing analyze disparate data to yield valuable insights
40-50% annual growth
in digital data volume1
~8xof unstructured data vs. structured data by 20203
1https://www-950.ibm.com/events/wwe/nedc/scesfall12.nsf/RodAdkins.pdf, page 52HP Autonomy Whitepaper: Transitioning to a New Era of Human Information, page 33https://www-950.ibm.com/events/wwe/nedc/scesfall12.nsf/RodAdkins.pdf, page 2
62%annual growth
in unstructured data2
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Cognitive analytics
Cognitive Computing
Industry Application
Healthcare: A global cognitive analytics provider and major cancer medical center creating a cognitive system that uses cancer patient treatment data to assist oncologists to diagnose and treat patients based on the most current available data.
Retail: Cognitive computers serving as customer service lines, in-store kiosks, or digital store clerks providing answers to customers’ questions around products, trends, recommendations, etc. pulled from millions of data points and structured / unstructured data.
Financial Services: Narrative Science arming investment managers and financial advisors with customized portfolio intelligence, and clients with regular, mobile-friendly account performance summaries, updates, imbalance alerts, changes in risk, etc.
Basic Application
• Drive insights with rich context/unstructured data• Form hypotheses and predictions based on
machine learning to aid real time decision making
• Self-correcting and evolving algorithm that emulates human cognition
• Big data processing
• Platform for machine based learning
• Processing of unstructured data
• Natural language processing
• Processing power and flexibility
Real time decision making Context rich insights/data
Multiple Industries
Cognitive Analytics
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Case study: Health Plan improves patient care
ü Combine claims and provider data to gain insight into the true health of the patient
ü Use Cognitive Analytics to identify patients most at risk for hospital re-admissions and high-cost events
ü Refine and enrich the solution as the dataset evolves
ü Augments scarce skillset, and leverages subject matter experts to review/refine instead of performing the primary analysis
Ø Proactively identify at-risk patients and prevent disease before it occurs or gets worse
Ø Over 100 terabytes of claims data, with 200+ points of correlation
Ø Petabytes of medical notes, physical exams, test results, etc. from providers
Ø Ability to process a billion new claims each year is constrained by clinical subject matter expert
High-Level Process
Combine with related
population data
Combine with related
population data
Identify key at-risk patients
Identify key at-risk patients
Refine based on human input
Refine based on human input
Ingest large volumes of claims &
provider data
Ingest large volumes of claims &
provider data
Final candidate list of at-risk
patients
Final candidate list of at-risk
patients
The problem The solution
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Case study: Financial Services firm improves customer service
ü Use Cognitive Analytics to detect customer micro segments
ü Track customers with high-value and high attrition risk, and predict future high-value customers
ü Develop personalized marketing strategies to maximize responsiveness and create promotions appropriate for each client
Ø Firm has vast amounts of transactional data, but is light on data scientists and missing the opportunity to see what drives customer behavior
Ø Over 500 terabytes of transactional data and multiple 3rd party data sources
High-Level Process
Integrate with 3rd
party sourcesIntegrate with 3rd
party sources
Create simulations /
generate models
Create simulations /
generate models
Ingest large volumes of
transactional data
Ingest large volumes of
transactional data
Targeted marketing for
high value customers
Targeted marketing for
high value customers
Identify target candidates / promotions
Identify target candidates / promotions
The problem The solution
Copyright © 2013 Deloitte Development LLC. All rights reserved.
What barriers to adoption of these technologies do you see in business?
• Too new, we do not have a clear enough understanding of them
• There remain doubts about their effectiveness• We do not have a clear vision of how to adapt them to our
needs given the small number of examples that exist now• It may just take time to build the business case• Building the business case and measuring value from the
solution• Don’t know/Not applicable
Poll question #3
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Case study: Unlocking the full picture of health
• Patient has type 2 diabetes• Regular checkup that included a blood sugar test
• Reasons for visit: Hypertension; medication and treatment plan non-compliance
• Recommendations for medication and lifestyle changes to manage stress, alcohol use. and begin smoking cessation
• Perspective into the patient’s lifestyle and reason for medication non-adherence (e.g., does the patient have underlying issues with depression?)
• How long has the patient been having symptoms of hypertension and type 2 diabetes?
• What triggered this occurrence? Does patient have underlying depression?
• What were the final diagnoses?• What were all the costs of the outpatient visit?
Structured Data | Claims• Visit Type: Outpatient• Primary Diagnosis: Type 2 diabetes, hypertension• Lab Result: Elevated random blood sugar levels of
240mg/dl
Unstructured Data | Medical Records & Notes• Reason for visit: Dizziness and blurred vision for 10
days (B/P 170/96)• Patient Background: o 6 month history of drinking a six pack & smoking a
pack dailyo Sleeps over 10 hours a dayo Recent weight gain of 30 lbs.o Has not refilled prescriptions for 5 months
Ove
rvie
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usio
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Hyp
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Una
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Q
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Through the convergence of structured and unstructured data, we can get the full view of the patient, and make recommendations to improve his or her well-being
Copyright © 2013 Deloitte Development LLC. All rights reserved.
WatsonPaths explores complex scenarios and draws conclusions much like people do in real life. When presented with a medical case, it extracts statements based on the knowledge it has learned from medical doctors and medical literature. As medical experts interact with WatsonPaths, the system will use machine-learning to improve and scale the ingestion of medical information. Through this collaboration, WatsonPaths compares its actions with that of the medical expert so the system can get “smarter”.
Case study: IBM WatsonPaths
• A result of a year-long research collaboration with faculty, physicians and students at Cleveland Clinic Lerner College of Medicine of Case Western Reserve University
• Expected to help physicians make more informed and accurate decisions faster and to cull new insights from electronic medical records (EMR)
• Planned to be used by the Cleveland Clinic faculty and students as part of their problem-based learning curriculum and in clinical lab simulations
Source: http://www.research.ibm.com/cognitive-computing/watson/watsonpaths.shtml
Copyright © 2013 Deloitte Development LLC. All rights reserved.
How do these technologies need to develop to become useful tools for your organization?
• Provide clarity on business perspective purpose and effectiveness
• Showcase more wins in areas that matter to us – stress results not science
• Develop packaged offerings for industries that make them easier to adopt
• It will take time, we are not early adopters• Don’t know/Not applicable
Poll question #4
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Cognitive Analytics: In summary
How convergence will impact solutions
Making relevant context-based
suggestions and recommendations
Ability to make quicker, more
informed decisions
Access to the right data at the
right time
Ability to capture and process
larger amounts of data
Machine Learning
Artificial Intelligence Statistics &
Decision Science
Natural Language Processing
Distributed ComputingCloud
Computing
Database Technology
Analytics and
Business Intelligence
Visualization
Various Data
Collection Channels
Structured and
Unstructured Data
Real Time Decision Making
Cognitive Analytics
Question and answer
Join us December 5 at 2 PM ET as our Technology Executives series presents:
Cloudy With a Chance of Core: Managing Integration in an Increasingly Complex World
Copyright © 2013 Deloitte Development LLC. All rights reserved.
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Click the CPE icon in the dock at the bottom of your screen.
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Rajeev RonankiPrincipal, Deloitte Consulting [email protected]
David SteierDirector, Deloitte Consulting [email protected]
Contact info
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Jim Zhu, Deloitte Consulting LLPJeff DeLisio, Deloitte Consulting LLPRui He, Deloitte Consulting LLPFatema Samiwala, Deloitte Consulting LLPRich Carelli, Deloitte Consulting LLPSteven Truong, Deloitte Consulting LLPRiddhi Roy, Deloitte Consulting LLP
Research Team:Ashish Kumar, Deloitte Consulting LLPMarjorie Galban, Deloitte Consulting LLPWilliam Shepherdson, Deloitte Consulting LLPLindsey Tsuya, Deloitte Consulting LLPEugene Chou, Deloitte Consulting LLP
Thanks to our Dbriefs team
Copyright © 2013 Deloitte Development LLC. All rights reserved.
• Application Programming Interface (API)• Customer Relationship Management (CRM)• Enterprise Resource Planning (ERP)• Natural Language Processing (NLP)
Acronyms used in presentation
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This presentation contains general information only and Deloitte is not, by means of this presentation, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This presentation is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this presentation.
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