From Cognitive Computing to Artificial Intelligence Dr Peter Waggett Director Emerging Technology September 2016
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The document outlines advancements in cognitive computing and artificial intelligence led by IBM, particularly focusing on the capabilities of the Watson system. It emphasizes the challenges and potential of machines understanding human language and thought, alongside exploring neuromorphic computing and its efficiencies compared to traditional systems. Additionally, it discusses future trends in computing, including brain-inspired technologies and their applications across various domains.
From Cognitive Computing to Artificial Intelligence Dr Peter Waggett Director Emerging Technology September 2016
- 1. © 2016 International Business Machines Corporation From Cognitive Computing to Artificial Intelligence Dr Peter Waggett Director Emerging Technology September 2016 Follow us @IBMWatson
- 2. © 2016 International Business Machines Corporation About Me Dr Peter Waggett PhD in Rocket Science Failed Astronaut Over 20 years IT Research Experience
- 3. © 2016 International Business Machines Corporation Smarter Planet Vision Instrumented Interconnected Intelligent Interactive
- 4. © 2016 International Business Machines Corporation Automating the World Understanding the World Graph Analytics Genomics Social Networks Security Machine Learning Healthcare Education Internet of Things Video, Speech Analytics Object recognition Complex video analytics Multi-model analytics Moore’s Law
- 5. © 2016 International Business Machines Corporation Cognitive Computing Grand Challenge
- 6. © 2016 International Business Machines Corporation IBM Watson and Jeopardy
- 7. © 2016 International Business Machines Corporation Person Organization L. Gerstner IBM J. Welch GE W. Gates Microsoft “If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE.” Welch ran this? Noses that run and feet that smell? How can a house burn up as it burns down? Does CPD represent a complex comorbidity of lung cancer? What mix of zero-coupon, non-callable, A+ munis fit my risk tolerance? Why is it so hard for computers to understand us and Vice Versa?
- 8. © 2016 International Business Machines Corporation Watson is a massively parallel system Answer Scoring Models Responses with Confidence Inquiry Evidence Sources Models Models Models Models ModelsPrimary Search Candidate Answer Generation Hypothesis Generation Hypothesis and Evidence Scoring Final Confidence Merging & Ranking Synthesis Answer Sources Inquiry/Topic Analysis Evidence Retrieval Deep Evidence Scoring Learned Models help combine and weigh the Evidence Hypothesis Generation Hypothesis and Evidence Scoring Inquiry Decomposition
- 9. © 2016 International Business Machines Corporation Attribute extraction Medications SymptomsDiseases Modifiers
- 10. © 2016 International Business Machines Corporation Imagine if you could read, understand and recall all relevant data… Answer Scoring Models Responses with Confidence Inquiry Evidence Sources Models Models Models Models ModelsPrimary Search Candidate Answer Generation Hypothesis Generation Hypothesis and Evidence Scoring Final Confidence Merging & Ranking Synthesis Answer Sources Inquiry/Topic Analysis Evidence Retrieval Deep Evidence Scoring Learned Models help combine and weigh the Evidence Hypothesis Generation Hypothesis and Evidence Scoring Inquiry Decomposition “Ingest” relevant data across a broad domain to create a Repository Understand complex English language “inquiries” Indentify potential responses Rank each option with confidence and supporting evidence Learn and Adapt
- 11. © 2016 International Business Machines Corporation Artificial Intelligence ??
- 12. © 2012 International Business Machines Corporation Artificial Intelligence the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
- 13. © 2015 International Business Machines Corporation Neuromorphic Computing 60% of valuable sensory data loses value in milliseconds Today’s computing systems are challenged to extract real-time actionable information from complex and cluttered sensor data with very low power. Biological systems on the other hand, have evolved to be extremely efficient at making sense of raw sensory information in real time at power levels orders of magnitude smaller than conventional compute. IBM’s SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) Chip – with brain-inspired computer architecture is powered by an unprecedented one million neurons and 256 million synapses. Such differentiated architecture – both in hardware and software – can enable neural computation to be deployed where the sensory data is at the edge, or at massive scale within the cloud.
- 14. © 2016 International Business Machines Corporation 14 Emerging Compute Fabrics 2D Grid of Qubits Cryptography Physics and Quantum Chemistry Material and Drug Design Challenge Exactness: Binary Synapse, 8-bit weight Challenge Synchrony: Asynchronous, event driven design Challenge Error Free Computing: Training in probability space Push the limits of energy efficient implementations (customized) Result: 100X power reduction IBM TrueNorth 1M Neurons 256M Synapses 5.4B Transistors Realtime 73 mW
- 15. © 2016 International Business Machines Corporation Brain-inspired systems will allow better analysis of sensory data
- 16. © 2016 International Business Machines Corporation 16 Neuromorphic Image Matching
- 17. © 2016 International Business Machines Corporation Recognition
- 18. © 2016 International Business Machines Corporation Brain-inspired devices will play diverse, critical roles
- 19. © 2016 International Business Machines Corporation Leading in the next era of computing Advanced Image Recognition Personality Analytics Symbiotic Cognitive Platforms Neurosynaptic Brain-inspired Computing Advanced Dialogue & Reasoning Exascale Data-centric Systems Quantum Computing Hybrid Cloud Physical Analytics Curation at Scale Personalized Learning 0-18 months 18 months - 3 years 3-10+ years “Ad Hoc” Systems
- 20. © 2016 International Business Machines Corporation ”Wetware”
- 21. © 2016 International Business Machines Corporation Prediction is Difficult …....... Especially About the Future (Niels Bohr)
- 22. © 2016 International Business Machines Corporation