Cognitive ComputingThe Hype, the Reality, the Hope

Sue Feldman

Synthexis

Cognitive Computing Consortium

Synthexis

Agenda

• Cognitive Computing Defined

• Why we need cognitive computing

• The hype

• The reality

• Cognitive applications: choosing and using them

• Challenges and issues

• The hope

HCI & Cognitive Studies

AI

CognitiveComputing

Evolution and Revolution

3

Cognitive Computing…

• Ambiguous, unpredictable

• Shifting situation, goals, information

• Conflicting data, voluminous, multiple sources

• Require exploration, iteration, discussion

• Need to uncover patterns, relationships and surprises

• Best answers based on context

• Problem solving: beyond information gathering

makes a new class of problems computable:

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Contextual: Filters results depending on “who, what, where, when, why”

Probabilistic: Delivers confidence scored results

Adaptive: Learns, reasons, infers, recommends

Highly integrated: Data and technology

Conversational: Language-based, Interactive, Iterative. stateful

Cognitive Computing Pillars

• 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

Why Now?• Too much information—what’s useful?

• Market demand: ROI, risk management, broader, better access, IT

complexity, new & difficult classes of problems

• User expectations

• Environment of experimentation and innovation

• Mature technologies: cloud, big data, machine learning, Internet of

Things, semantic, visual and sentiment understanding

• New tools: visualization, analytics

7

The Hype

• AI will make current technology obsolete

• Self driving cars will take over the roads in five years

• AI & cognitive systems will take all our jobs

• Decisions will be made automatically

• The singularity will govern all human life

The Technology Reality

• 99% of AI today is human effort

• Custom development is the norm.

• Bias: Training sets, ontologies, vocabularies

• No technology is magic. Combine multiple technologies for best

results—rules, simple phrases, heuristics, ML.

• Moving from the digital to the physical world may entail higher

physical risk for humans (self driving cars vs. video games)

• Augmented applications, NOT autonomous AI

Man vs. Machine

Human Machine

• Unbiased.• Consistent to a fault• Statistical reasoning and

inference. • Value judgments must be

programmed. Spectacular mistakes

• Large scale math• Finds unexpected patterns

across sources• Scalable/big data an

advantage

• Common sense

• Biased

• Sets goals/hypotheses

• Intuitive/hunches

• Inconsistent

• Gets tired/bored

• Doesn’t scale: limit to

data ingestion

• Understands human

values, ethics, culture

Cognitive ApplicationsToday

Synthexis

Uses TodayDigital Assistants• Cancer

diagnosis/treatment

• Healthcare advisor

• Customer service

• Investment advisor

Opportunities• Mergers/acquisitions

• Drug discovery

Threat Detection

• Fraud

• Terrorism

• Hacking

• Brand protection

High risk - High value - Dynamic, shifting data and situations - Multiple sources

Context is important

Data is well curated, domain or task specific

Traditional Information System

Data

Index

QueriesResults

Cognitive System

CognitiveProcessor

ContextQuestions

ExploreData

Problem

Decide

When to Use Cognitive Technologies

• Problems are complex, information and situation fluid, conflicting data

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

• No clearly right answers: context determines best answer

• Ranked (confidence scored), multiple answers are preferred (alternatives)

• Process intensive and difficult to automate because of unpredictability

• Context dependent: time, user, location, point in task

• Exploration, across silos is a priority:

• Human-computer partnership, iteration and interaction and dialog are

required

Cognitive Computing Principles

1. Because we can not predict what we will want to find…

• Extract and store elements of meaning and their relationships

• Combine at runtime

• Rank, filter and explore using context

2. Similarity matching + interaction and exploration tools

3. Feedback to system to improve understanding, terminology

changes, add/alter models, etc.

4. Repeatability of results only if nothing has changed

And When NOT… • When predictable, repeatable results are required (e.g. sales reports)—a

snapshot in time

• 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

• When existing transactional systems are adequate

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+ +tech Output Goal

Structured data

Unstructured data

Audio

Images/Video

Knowledge bases:

Ontologies

Process knowledge

Schemas…

Machine learning

Analytics

Search

Visualization

Game theory

Machine vision

Databases…

Answers

Recommendations

Patterns

Predictions

Visualizations

Voice interaction

Maps

Directions

Saved lives

Engaged customers

Revenue

Security

Productivity

Reduced risks

Cost savings

data

Cognitive Computing Applications

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Trade-offs and ChoicesWhat 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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Cognitive Applications Continuum

• Find/recommend for individual’s context

• Answers

• High accuracy

• Domain specific

• Data prep time is high, manually intensive

• Questions

• Curated, cleansed data

• Rule bases, heuristics

• Problems: over fitting, missed related information, changes in terminology, too little information

• Explore

• Patterns, trends, clusters, information spaces

• Serendipity, low accuracy

• General knowledge

• Lower prep time, automated training, predictive models

• Target or goal description

• Merged data, not curated or overly cleansed

• Grammars, vocabularies, synonym bases

• Problems: correlation Vs. causation? low accuracy, false drops, false leads, too much information

Expert System Discovery/Exploration

Example: Oncology assistant Example: Drug discovery

Social & Legal Issues• Can computers replace humans? Should they?

• Should we trust computers to make complex decisions?

• Can people accept choices instead of a simple recommendation?

• Effects of built in bias

• Who is responsible for computer errors that harm people?

• Should we trade off privacy for better medical treatment?

• No best practices or accepted practices. No standards.

The Cognitive Future

• Extract more communication clues: sentiment, voice

(intonation/tone) vision, gestures, facial expressions

• Embodied cognition: self driving cars, robots, devices, virtual

reality…

• Research becomes reality: conversational models, task and

individual interfaces.

• Digital assistants for work or personal use

• Neuroscience-based software and hardware

• More regulations for privacy, cyber civility26

Trends

Olli: Self Driving Bus & Tour Guide

THE HOPECognitive applications will:

• Decrease information overload

• Generate personalized contextual recommendations

• Respond appropriately to moods, emotions, priorities, emergencies

• Prevent medical errors

• Detect impending epidemics

• Detect patterns of fraud, criminal behavior, hacking

• Detect mental and physical illness earlier

• Personalize and improve education

• Interact naturally and contextually

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CustomerMatrix, SAS, Hewlett Packard Enterprise,

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Sue Feldman

sue@synthexis.com