understanding cognitive applications: a framework - sue feldman
TRANSCRIPT
Understanding Cognitive Applications:
A Framework
Sue Feldman
Educate Publish Collaborate Events ConnectResearch
Cognitive Computing Consortium
Who we are: A consortium of private and public organizations and individuals
Our Sponsors
CustomerMatrix, SAS, Hewlett Packard Enterprise,
Sinequa, Naralogics, Babson College, Quid
ConnectCollabo-
rateEducateResearch Publish Events
What we do:
Research Directions
• Define cognitive computing (2014 working group)
• Develop a framework for understanding and using cognitive computing:• Identify problems amenable to cognitive computing approach
• Identify types of cognitive applications
• Compare cognitive approaches to other computing systems
• Develop trust index to track market acceptance
• Publish guides for practitioners, common frameworks for discussion
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Cognitive Computing: A Definition
Today’s Session
Applications Framework
A Continuum of Uses
Examples
Contextual: Filters results depending on “who, what, where, when, why”
Probabilistic: Delivers confidence scored results
Learning/Adaptive: Reacts and changes based on new information, interactions
Highly integrated: Data and technology
Conversational: Meaning-based, Interactive, Iterative. stateful
Cognitive Computing Pillars
When to Use Cognitive Technologies
Diverse, changing data sources, including unstructured (text, images)
Ranked (confidence scored), multiple answers are preferred (alternatives)
Context dependent: time, user, location, point in task
Process intensive and difficult to automate because of unpredictability
No clearly right answers: Data is complex and ambiguous, conflicting evidence
Exploration is a priority: across silos
Human-computer partnership and dialog are required
When problems are complex, information and situation are
shifting, and outcome depends on context
And When NOT
When predictable, repeatable results are required (e.g. sales reports)
When shifting views and answers are not appropriate or are indefensible
due to industry regulations
When a probabilistic approach is not desirable
When interaction, especially in natural language, is not necessary
When all data is structured, numeric and predictable
When existing transactional systems are adequate
HCI & Cognitive Studies
AI
CognitiveComputing
Contributing Technologies
BOTS
Contributing Technologies
BI and Data Analytics: Databases, rule bases, schemas, analytics, visualization, reporting, repeatable results, analytical & modeling tools, predictions
Search & Text Analytics: Probabilistic, confidence scored results, meaning-
based, recommendations, similarity matching,, relationships, sentiment
AI: Autonomous, learning/adaptive, machine learning, game theory, genetic algorithms, etc.
Internet of Things: Big data, streaming, Hadoop, etc.
Conversational Systems: Meaning-based, contextual, interactive, Iterative. Stateful, domain based. Bots
HCI & Cognitive Science: User interaction studies, brain science
Designing Cognitive Applications
+ +
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techOutput 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
Saved lives
Engaged customers
Revenue
Security
Productivity
Reduced risks
Cost savings
data
Cognitive Computing Applications
Medical journals
Curated oncology KB
Clinical databases
Pharma DB
Genetic profile
Patient’s medical records
Media: X-rays, CAT scans, etc.
Health insurance
Regulations
Match individual to recommendations
Access by non-IT staff
Conversational, stateful, dynamic
High accuracy (life and death)
Probabilistic recommendations
Exploration and pattern finding
Drill down to original document
NLP: text analytics,
tagging, code extraction
Machine learning
Visualization
Game theory
Domain knowledge
Analytics
Better decisions
Lives saved
What kind of tumor does this patient have and how should
we treat it? He is 80 years old and in good health, but a
heavy smoker.
Oncology Treatment Advisor
Data Technologies
ValueBehaviors
Required Value
Cognitive Systems Continuum
• Find/recommend for individual’s context
• Answers
• High accuracy
• Domain specific
• Data prep time is high (ontologies, normalization, etc.), manually intensive
• Questions
• Curated, cleansed data
• Rule bases, heuristics
• Problems with 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 with confusion of correlation and causation, low accuracy, more false drops, false leads, too much information
Expert System Discovery/Exploration Application
Example: Oncology assistant Example: Drug discovery
Cognitive Applications: Framework
Generalized
Do
ma
inK
no
wle
dge
Individual Task/Process/ Goal
ExpertSystem
Discovery/Exploration
Low confidence, high serendipity• Explore data and filter by individual
context• Find similar examples using individual
as model
High confidence, low serendipity• Answer questions• Find similar examples using individual
as query• Recommendations within context of
individual
Mid level confidence and serendipity• Find indirect connections• Find similarity to a model or problem
statement• Extract models from data, given
examples
Low confidence, high serendipity• Find unknowns. Fishing expedition• Find anomalies, abnormal behavior• Discover unknown relationships/patterns
based on minimal problem specification
Context
Mo
dal
ity
Cognitive Applications: Examples
Specialized Generalized
Do
ma
inK
no
wle
dge
Mid confidence and serendipity• Cognitive assistant for the blind• Staffing recommendations based on
social graph, interests, past projects, profiles of individuals
• Detect individuals engaged in fraud
High confidence, low serendipity• Oncology advisor• Investment advisor• Shopping recommendations• Land lease management
Mid level confidence and serendipity• M&A Advisor based on models of
previous business successes and failures, business profiles, social graphs, news, predictions of market
Low confidence, high serendipity• Drug discovery• Detect terrorism patterns among unrelated
entities
Individual Task/Process/ Goal
Context
ExpertSystem
Discovery/Exploration
Mo
dal
ity
