Understanding Cognitive Applications: A Framework - Sue Feldman

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Sue Feldman, Synthexis and the Cognitive Computing Consortium presentation from the Cognitive Systems Institute Speaker Series on September 1, 2016.

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Understanding Cognitive Applications: A Framework - Sue Feldman

  1. 1. Understanding Cognitive Applications: A Framework Sue Feldman
  2. 2. 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 Connect Collabo- rate EducateResearch Publish Events What we do:
  3. 3. 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
  4. 4. 1 2 3 4 Cognitive Computing: A Definition Today’s Session Applications Framework A Continuum of Uses Examples
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. HCI & Cognitive Studies AI Cognitive Computing Contributing Technologies BOTS
  9. 9. 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
  10. 10. Designing Cognitive Applications
  11. 11. + + 11 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 Saved lives Engaged customers Revenue Security Productivity Reduced risks Cost savings data Cognitive Computing Applications
  12. 12. 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 Value Behaviors Required Value
  13. 13. 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
  14. 14. Cognitive Applications: Framework Generalized DomainKnowledge Individual Task/Process/ Goal Expert System 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 Modality
  15. 15. Cognitive Applications: Examples Specialized Generalized DomainKnowledge 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 Expert System Discovery/ Exploration Modality
  16. 16. Questions? Sue Feldman Synthexis sue@synthexis.com

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