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Growing Intelligence - Looking beyond year one

“ The question of whether computers can think is like the question of whether submarines can swim.” - Edsger W. Dijkstra. Growing Intelligence - Looking beyond year one. Gadi Singer VP and GM, IDGz Architecture Group GM, Israel Development Centers (IDC ) May 7th, 2013.

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Growing Intelligence - Looking beyond year one

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  1. “The question of whether computers can think is like the • question of whether submarines can swim.” • - EdsgerW. Dijkstra Growing Intelligence -Looking beyond year one Gadi Singer VP and GM, IDGz Architecture Group GM, Israel Development Centers (IDC) May 7th, 2013

  2. Why are we here today?

  3. Almost 1 year ago – ICRI InaugurationTel Aviv Museum of Arts, May 22, 2012 What were we trying to achieve ? Computational Intelligence Bringing together ML and heterogeneous architectures to deliver next generation of intelligent devices that are efficient, adaptive and always-learning

  4. Were we the first to think about it ?

  5. בן גוריון מתנבא על העתיד

  6. ICRI-CI Year One Retrospective • Impressive group of researchers, impressive set of projects • Cross-domain / Cross discipline research • High match between the grand vision and institute themes / projects • ML 2020 • Intelligent Agents • Brain Inspired Computing • Accelerators • Intel Internal - Growing interest regarding the selected ICRI themes

  7. Year Two and Beyond • Computational Intelligence - one of the greatest frontiers • Academia, Industry, Societies • Requires World-Class research • Impactful research with path to deployed solutions • Increased collaboration among researchers, and with Intel • Continuously refine program – e.g., adding projects • NN based architecture • Agent assist in discussion

  8. Characteristics of “Truly Intelligent” Computing

  9. Elements of Intelligent Systems Collects and synthesized user data to gain awareness: • Knowledge of the user • General Knowledge • Sensing of the environment • Understand the user needs and goals: • User’s general intent • Specific goals may be explicitly defined with corresponding actions • Judges a course of action: • Rules to govern decisions • System recommendation • User approval • Surface relevant options • Acts on user’s behalf: • Autonomous action • Proactive decisions • Enable users to track actions Adapts to experiences over time to improve the system: ACTION AWARENESS ALIGNED GOALS DECISIONS LEARNING

  10. Principle #1 – Brain Inspired Computing

  11. Brain Inspired Computing Theories of Perception and Cognition Approaching Viable Implementation

  12. Principle #2 – Modular and Open Platforms

  13. “Open & Horizontal” is live and kicking! Source: Bain. *Other brands and names may be claimed as the property of others. Tablets & Phones Power Other RISC (IBM) 100% 15% Data Centers Other 25% 75% 100% SPARC 50% 75% OtherCISC (IBM) 60% Architecture 25% 50% 0 25% 2011 2009 2010 2012 0 ‘90 ‘92 ‘94 ‘96 ‘98 ‘00 Platform where capabilities come from modules provided by individuals, companies, or Academia

  14. Example – Intel’s Perceptual ComputingThe Rise of Natural Intuitive Computing Now Near Future The Vision Providing Human-like Senses to Computing

  15. BETA SDK: Free for Evaluation Perceptual Modes Support: Face Analysis, Tracking Finger Tracking Close-Range Hand Gesture Recognition Voice Processing 2D, 3D Augmented Reality APIs: High-Level API: For fast, easy programming Low-Level API: For innovation and programming control Intel® Perceptual Computing SDK BetaProviding Infrastructure to build on

  16. Principle #3 – Development for a Learning (Evolving) Machine

  17. Development for a Learning (Evolving) Machine Design a machine for unforeseeable scenarios Validate a solution that will evolve and change in the field • What does “correctness” means? • Validate to ensure people’s safety, security, and privacy ? Opening a door

  18. Principle #4 – Significantly Improved Power Efficiency

  19. Efficient Architectures for Perceptual/Cognitive Computing Watson: Ninety IBM Power-750 servers (plus additional I/O, network and cluster controller nodes in 10 racks) Total of 2880 POWER7 processor cores and 16 Terabytes of RAM. Each Power-750 uses a 3.5 GHz POWER7 8-core processor, 4-threads per core. • Calculation vs. Cognitive • “Invisible” and seamless • Re-imagine power • efficient computing

  20. Principle #5 – Structuring for Ethical Choices

  21. “Ethical Computing” • Autonomous == Making Choices • Value system  weights on options • High-impact opportunities for good, also imply risks “Ethical Module[s]” needs to emerge

  22. Example - Three Laws of Robotics (Asimov, 1940) 0. A robot may not harm humanity, or, by inaction, allow humanity to come to harm • A robot may not injure a human being or, through inaction, allow a human being to come to harm. • A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law. • A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

  23. Year 2 and Beyond- The Quest

  24. Computing Evolution The Decade Cycle • 1980’s – Compute revolution • 1990’s – Network revolution • 2000’s – Sensor revolution • 2010’s – Recognition/Cognition? The Usage Evolution • Productivity and Entertainment- Von Neumann Arch has worked well • Interactive computing : • Traditional Devices struggle to fit the needs (e.g. ASR, Object Recognition etc…). • Dedicated platforms lead (Gestures, Voice…) • Relating Computing – • Currently available architectures fail to cope with the new tasks (NLU, AGI etc…) Source : IBM DARPA Synapse Project

  25. The Quest: Specialized Cognitive System • New HW/SW solutions that are optimized for: • Representation: Massively parallel, somewhat redundant, semantic rich, info storage • Inference/Reasoning: Massively parallel (>>1000), probabilistic, hierarchical, pattern matching and abstraction • Learning: Adding new info/patterns through external source (teaching) or introspective ML. • Power Efficiency: For effective local and distributed computing Will they create > 100X efficiency in Cognitive Computing uses?

  26. Closing… and opening for a future • Community (Academia, Industry, Developers) should create content for intelligence competencies • Define a framework and platform[s] for intelligence computing: • Brain inspired • Collaboration through modularity and openness • Enable and contain machine learning • Significantly improved power efficiency • Structure for ethical choices “The coming 3-5 years are about exquisite sensing; the following decade will be about making sense of the senses.” Gadi Singer

  27. Thank You !

  28. Intelligent System – Research ingredients Focus application areas Retail Automotive Home Interactive task assistance Family coordination Mobile health and wellness Apps &Services Machine understanding Human action/intent understanding Object recognition speech recognition Dynamic scene understanding Never-ending learning Imitation/reinforcement learning for manipulation Precision location Parallel ML algorithms Personalized activity models Multi-step task reco. Personalized, joint speech/gesture reco. Multi-sensor / multi-person activity inference Activity learning by demonstration Scene understanding Stress recognition Resource management Data complexity reduction Resource-constrained ML Perpetual sensing Energy eff. data collection OS & Middleware HCI Human robot interaction Goal-driven labeling Task assistanceMobile persuasion System Architecture Robotic platforms Embedded-to-cloud Crowd/embed. arch. Sensing First Person Sensing Camera-Projector Inertial localization Sensor network Infra. mediated Privacy Preservation Compressive cameras Communications Embedded interfaces V2V Communication Visible light communication Low-power GSM Burst RFID PlatformIngredients Power harvesting GSM / 802.11 / UHF Barometric / Thermal

  29. Human Brain Competencies Perception Motivation Vision, Audition, Touch, Proprioception, Cross-Modal Subgoal Creation, Affect Based Actuation Emotion Emotional Expression, Understanding Emotions, Perceiving Emotions, Control of Emotions Physical Skills, Tool Use, Navigation, Proprioception Memory Modeling Self and Other Implicit, Working, Episodic, Semantic, Procedural Self Awareness, Theory of Mind, Self Control. Other-Awareness, Empathy Learning Social Interaction Imitation, Reinforcement, Dialogical, Written, Experimental Appropriate Behavior, Social Communication, Social Inference, Cooperation Reasoning Communication Deduction, Induction, Abduction, Causal, Physical Verbal, Gestural, Pictorial, Language acquisition, Cross-Modal Planning Quantitative Tactical, Strategic, Physical, Social Counting Objects, Grounded Small Number Arithmetic, Comparison of Quantitative Properties, Measuring with tools Attention Building/Creation Visual, Social, Behavioral Physical Construction w/ Objects, Formation of Novel Concepts, Verbal Invention, Social Organization Source: Ben Goerzel, AGI 2011

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