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Cognitive Computing the Hype, the Reality, the Hope Synthexis Cognitive Computing The Hype, the Reality, the Hope Sue Feldman Synthexis Cognitive Computing Consortium Agenda • Cognitive Computing Defined • Why we need cognitive computing • The hype • The reality • Cognitive applications: choosing and using them • Challenges and issues • The hope Evolution and Revolution AI Cognitive Computing HCI & Cognitive Studies 3 Cognitive Computing… makes a new class of problems computable: • 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 4 Cognitive Computing Pillars Adaptive: Learns, reasons, infers, recommends Probabilistic: Delivers confidence scored results Contextual: Filters results depending on “who, what, where, when, why” Conversational: Language-based, Interactive, Iterative. stateful Highly integrated: Data and technology Context: A Patient • Individual profile (context): - Genetic makeup - Age - Sex - Medical history: allergies, other conditions, etc. • Location • Health services available • Possible treatments and confidence scores 6 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 • Common sense • Unbiased. • Consistent to a fault • Biased • Statistical reasoning and • Sets goals/hypotheses inference. • Intuitive/hunches • Value judgments must be • Inconsistent programmed. Spectacular • Gets tired/bored mistakes • Doesn’t scale: limit to • Large scale math • Finds unexpected patterns data ingestion across sources • Understands human • Scalable/big data an values, ethics, culture advantage Synthexis Cognitive Applications Today Uses Today Digital Assistants Opportunities Threat Detection • Cancer • Mergers/acquisitions • Fraud diagnosis/treatment • Drug discovery • Terrorism • Healthcare advisor • Hacking • Customer service • Brand protection • Investment advisor 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 Queries Results Index Data Cognitive System Context Problem Questions Cognitive Processor Data Explore 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 20 Cognitive Computing Applications + + data tech Output Goal Structured data Machine learning Answers Saved lives Unstructured data Analytics Recommendations Engaged customers Audio Search Patterns Revenue Images/Video Visualization Predictions Security Knowledge bases: Game theory Visualizations Productivity Ontologies Machine vision Voice interaction Reduced risks Process knowledge Databases… Maps Cost savings Schemas… Directions 21 Trade-offs and Choices 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… 22 Cognitive Applications Continuum Expert System Discovery/Exploration • Find/recommend for individual’s context • Explore • Answers • Patterns, trends, clusters, information spaces • High accuracy • Serendipity, low accuracy • Domain specific • General knowledge • Data prep time is high, manually intensive • Lower prep time, automated training, • Questions predictive models • Curated, cleansed data • Target or goal description • Rule bases, heuristics • Merged data, not curated or overly cleansed • Problems: over fitting, missed related • Grammars, vocabularies, synonym bases information, changes in terminology, too • Problems: correlation Vs. causation? low little information accuracy, false drops, false leads, too much information 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 Trends • 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 civility 26 Olli: Self Driving Bus & Tour Guide THE HOPE Cognitive 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 Cognitive Computing Consortium Who we are: A consortium of private and public organizations and individuals What we do: Research Educate Publish Collaborate Events Connect Collabo- Research Educate Publish Events Connect rate Our Sponsors CustomerMatrix, SAS, Hewlett Packard Enterprise, Sinequa, Naralogics, Babson College, Quid Synthexis Questions? Sue Feldman [email protected].
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