cognitive computing - sas...cognitive computing… • ambiguous, unpredictable • shifting...
TRANSCRIPT
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
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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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