Cognitive ComputingSue Feldman, Synthexis

Managing Director,

Cognitive Computing Consortium

• Introductions

• What is cognitive computing and why do we need it?

• Market and technology trends

• Cognitive system processes: what’s different?

• Examples

• Issues and impacts

• Future directions for cognitive computing

• The Cognitive Computing Consortium

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Agenda

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IBM Watson Debut

…makes a new class of problem computable:

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Cognitive Computing…

• Ambiguous, unpredictable

• Conflicting data

• Require exploration, not searching

• Need to uncover patterns and surprises

• Shifting situation, goals, information

• Best answers based on context

• Problem solving: beyond information gathering

• Meaning-based

• Probabilistic

• Iterative and conversational

• Interactive

• Contextual

• Learns and adapts based on interactions, new information, users

• Big data knowledge base—multiple sources, formats

• Analytics

• Highly integrated: Search, BI, analytics, visualization, voting algorithms,

categorization, statistics, machine learning, NLP, inferencing, content management,

voice recognition, etc.

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Cognitive Computing is…

Act as an intelligent partner:

• Analyze BIG data

• Understand human language on multiple levels

• Analyze and merge all formats and sources of information

• Uncover relationships across sources

• Understand and filter by context

• Find patterns in the data that are both expected and unexpected

• Learn from new information, new interactions

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What Cognitive Systems Do

• Are there new drugs that might be MORE effective for controlling diabetes?

• Who is funding this terrorist organization and how are the funds delivered? IS THIS ORGANIZATION A THREAT?

• Can I identify the MOST RISKY product or customer problems before they blindside our company?

• Which company would be the MOST PROMISING M&A target?

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Examples

• More than big data or AI

• Not robotics.

• Not drones.

• Not humanoids

• Not entirely autonomous

• Not the singularity

• Not a human replacement

A cognitive system is an aid, not a replacement for humans

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What Cognitive Computing is NOT

• Too much information

• Buyer Demand: ROI, risk management, more access for more users

• User Expectations

• Different problems to tackle

• IT complexity

• New, robust technologies integrated with information:

• Probabilistic computing

• Adaptive learning systems

• Pervasive and expanded analytics

• Language understanding

• Aggregation and integration beyond MDM

• Cloud and InfoApps

• Vendors driving demand

• Environment of experimentation and innovation

Why Now?

Information Growth 2010-2020

2010

• Access anywhere on any device

• Collaboration

• Ease of use

• Task-specific design

• Security and Privacy

• Know the individual: App or service provider knows who I am: the user’s

device, history and context

Answers questions appropriately for the context

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User Expectations

IBMSAS

Cogni veScale

Intel-Saffron

SAP-BusinessObjects

HP-AutonomyMicroso

CustomerMatrix

Welltok

LifeLearnSophie

9WSearch

NaraLogicsCambridgeSeman cs

CrimsonHexagon

Palan r

WolframAlpha

OpenPediatrics

AppleGoogle

SkytreeContextRelevant

Oracle

Numenta

MetaMind

Metaphacts

MegaTechVendors

IBMWatsonPartners

StartupApplica ons

Analy csSpecialists

LegacyTechVendors

&manymore…

genieMDsparkcogni on

wayblazer

Market Landscape

Cognitoys

A9

Entering the Era of the Individual

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MICRO-SEGMENTATION: TARGET THE INDIVIDUAL

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Mark: A Cognitive Computing Story

• 6 years old

• Lithuanian-American

• Tumor

1. Dialog

2. Match individual to appropriate population segment

3. Mine all literature, clinical trials, guidelines, genetic profiles for best

matches

4. Diagnosis

5. Find top treatment recommendations

6. Discuss with patient

7. Add patient’s questions and iterate

8. Choose preferred treatment

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The New Healthcare

Cognitive Systems Overview

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The Best Cognitive System

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Traditional Information System

Index and

Matching EngineQuestions Documents

Decide

Technology Map

Cognitive System

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Cognitive System

• Task or process

• Location

• Domain

• Time and current step in process

• History

• Personal characteristics

• Role

• Sentiment

• Preferred approach depending on personality

• Emotions: positive/negative, joy,surprise, satisfaction, trust, forgiveness, excitement, amusement,

hope, pride, sympathy, sadness, confusion, regret, anger, frustration, disappointment, envy, horror,

etc.

• Usefulness ranking23

What is Context?

Emphasis on the individual at that time in that task

Filter

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Context: Jeopardy!

• Rules

• Other players

• What everyone has son

• What confidence do I have in my answer?

• How much do I need to win?

• Individual profile (context):- Genetic makeup- Age- Sex- Medical history: allergies,

other conditions, etc.• Location• Health services available• Possible treatments and

confidence scores

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Context: A Patient

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Context: An Investor

• Portfolio

• Personality:

• Conservative? Adventurous?

Depends on friends’ advice

• Wants a lot/little data?

• Influencer?

• Age

• Previous investment history

• Market trends

• Investment strategy

Recommends best actions based on

customer context:

• Profile of successful sales • Industry sector, finances• Buying behavior

• Profile of best contacts• Position in company• Relationship to sales person

• Current company news

• Confidence scores

• Action recommendations: strategic usefulness/importance score

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Context: SalesFinding the BEST Prospect

• Health plan options available• Preferences for competitive activities,

group activities? Solitary activities?• History• Medical profile• Best way to deliver advice?

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A Wellness Program Client

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Context: An Event(Expert System)

Context: An Event(Expert System Cognito API)

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Watson Personality Insights

• Diverse data sources, including unstructured (text, images)

• No clearly right answers:• Data is complex and ambiguous

• Conflicting evidence

• Ranked (confidence scored) answers are acceptable

• Process intensive and difficult to automate because of unpredictability

• Time dependent: need put to the minute information (evidence) to support decisions

• Big data

• Exploration is a priority

• Interaction on human terms32

When to Use Cognitive Technologies

• When predictable, repeatable results are required (e.g. sales reports)

• When all data is structured, numeric and predictable

• e.g. Internet of Things

• When shifting views and answers are not appropriate or are

indefensible due to industry regulations

• When interaction, especially in natural language, is not necessary

• When a probabilistic approach is not desirable

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And When NOT…

What is good enough? It depends on the use:

• Serendipity vs. high confidence level

• Preprocessing and ingestion: depth vs. speed

• Speed of response: real time vs. a few seconds, days, or weeks

• Impact of outcome: life and death vs. trend detection in social media

• Thoroughness and type of data

• Thoroughness of analysis

• Type of use: question answering/monitoring/trend analysis/risk alerts/customer interaction…

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Trade-offs and Choices

Issues, Impacts and

Future Directions

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Redefines the human-computer relationship

• Computers to sift through massive information

• Computers to detect unknown patterns

• Computers to avoid bias

• People to identify and define problems

• People to make decisions based on evidence

• Individualized results: no more one size fits all

The Impact

The Impact

Upends existing industries: technology, software, healthcare, publishing, finance, government, security…

Raises issues of ethics, privacy, security, law:

• Can computers replace humans? Should they?

• Can a cognitive system think or feel emotions? How do they differ from humans?

• What are the implications for liability in using a cognitive system?

• What right does a vendor/retailer have to decide how to handle me and my account based on my actions?

• What rights do I have to contest how others analyze and use my data?

The Impact

• InfoApps

• Cognitive computing platforms and their ecosystems

• Security—for internal data and for personal information

• Recommendation systems

• Innovation and research support applications

• Better UI design for information interaction:

• Visualization

• Guided questioning

• Speech and gestural (haptic) interfaces

Opportunities

• Collaboration with large vendors like IBM, SAS, HP

• ROI metrics: new, more verifiable measures; baseline measures

• New uses for an analytics suite.

• What does a probabilistic interface look like?

• Can you normalize semi-automatically and quickly across multiple diverse sources and formats to improve serendipity for finding patterns, innovation? How? In what types of uses?

• Who is using cognitive computing (adoption)? To solve which problems?

• Maturity models

• Market dynamics

Research Opportunities

• Improved, individualized healthcare

• A cognitive-assisted stockbroker

• Improved, individualized sales and customer

support

• Computer-orchestrated political campaigns

• Executive Advisor: tells you the top 3 things to

pay attention to

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The Cognitive Future

Questions?

Sue Feldman

sue@synthexis.com

sfeldman@cognitivecomputingconsortium.com

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The Cognitive Computing Consortium

• Purpose and goals• Organization and Leadership• Members• Services in 2015• Future plans• Questions

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Our Vision

• Forum: An online discussion forum limited to high level practitioners, vendors and researchers. We

believe that the current approach to indiscriminate use of social media (anyone can join in) has

been counter productive for top level thinkers. It wastes their time answering questions that are

repetitive and naive. Our goal is to ensure that industry leaders and thinkers will find discussions

valuable and that pre-vetting of forum members will help guarantee they are talking to their peers.

• Reporting: A trusted information source for

– Events

– Research and publications

• Ideas: Thought leadership through Web site (blog), speeches, KM World column

• Research: sponsors research at respected institutions or by respected thought leaders.

• Events: Annual conference. Invited speakers.

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Mission

The Cognitive Computing Consortium provides a forum for researchers, developers

and practitioners of cognitive computing and its allied technologies. Its goals are to:

Discussion Forum. Develop a community of experts and practitioners for the exchange of

ideas and information;

Research and Education. Promote and support research in cognitive computing by

creating alliances between the research, academic and industry communities

Publications: research reports and practical guides on cognitive computing;

Events on cognitive computing and participate in conferences and workshops on this topic.

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Members

The Consortium is a cross-industry group of organizations and individuals from

the IT, academic and analyst communities.

Members have expertise in:

• Machine learning and Artificial intelligence

• Analytics

• Search

• Big data

• User interaction design

• Data science and master data management

• Information source evaluation and selection

• Technology market research.