Gadi SingerVP and GM, IDGz Architecture Group

GM, Israel Development Centers (IDC)

May 7th, 2013

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

Why are we here today?

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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

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Were we the first to think about it ?

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בן גוריון מתנבא על העתיד

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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 

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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

Characteristics of “Truly Intelligent” Computing

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Elements of Intelligent Systems

Judges a course of action:

• Rules to govern decisions

• System recommendation

• User approval• Surface relevant

options

Understand the user needs and goals:

• User’s general intent

• Specific goals may be explicitly defined with corresponding actions

Acts on user’s behalf:

• Autonomous action

• Proactive decisions

• Enable users to track actions

Adapts to experiences over time to improve the system:

Collects and synthesized userdata to gainawareness:

• Knowledge of the user

• General Knowledge

• Sensing of the environment

AWARENESS ALIGNED GOALS ACTION LEARNINGDECISIONS

Principle #1 – Brain Inspired Computing

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Theories of Perception and Cognition Approaching Viable Implementation

Brain Inspired Computing

Principle #2 – Modular and Open Platforms

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“Open & Horizontal” is live and kicking!Source: Bain. *Other brands and names may be claimed as the property of others .

Architecture

Other RISC(IBM)

SPARC

OtherCISC(IBM)

Power

‘90 ‘92 ‘94 ‘96 ‘98 ‘00

75%

Data Centers100%

50%

25%

0

Tablets & PhonesOther

75%

100%

50%

25%

02009 2010 20122011

25%

15%

60%

Platform where capabilities come from modules

provided by individuals, companies, or Academia

Example – Intel’s Perceptual Computing

The Rise of Natural Intuitive ComputingNow Near Future The Vision

Providing Human-like Senses to Computing

Intel® Perceptual Computing SDK Beta

Providing Infrastructure to build on• 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

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

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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

Development for a Learning (Evolving) Machine

?

Open

ing

a do

or

Principle #4 – Significantly Improved Power Efficiency

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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

Principle #5 – Structuring for Ethical Choices

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“Ethical Computing”• Autonomous == Making Choices• Value system weights on options• High-impact opportunities for good, also imply

risks

“Ethical Module[s]” needs to emerge

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Example - Three Laws of Robotics (Asimov, 1940)

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2. A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

0. A robot may not harm humanity, or, by inaction, allow humanity to come to

harm

Year 2 and Beyond - The Quest

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Computing EvolutionThe 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

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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?

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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:1. Brain inspired2. Collaboration through modularity and openness3. Enable and contain machine learning4. Significantly improved power efficiency5. 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

Thank You !

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System ArchitectureRobotic platformsEmbedded-to-cloudCrowd/embed. arch.

CommunicationsEmbedded interfacesV2V CommunicationVisible light communicationLow-power GSMBurst RFID

Power harvestingGSM / 802.11 / UHFBarometric / Thermal

SensingFirst Person SensingCamera-ProjectorInertial localizationSensor network

Infra. mediatedPrivacy PreservationCompressive cameras

Resource managementData complexity reductionResource-constrained MLPerpetual sensingEnergy eff. data collection

Machine understandingHuman action/intent understandingObject recognitionspeech recognitionDynamic scene understanding Never-ending learningImitation/reinforcement learning for manipulationPrecision locationParallel ML algorithms

Personalized activity modelsMulti-step task reco.Personalized, joint speech/gesture reco.Multi-sensor / multi-person activity inferenceActivity learning by demonstrationScene understandingStress recognition

HCIHuman robot interactionGoal-driven labelingTask assistanceMobile persuasion

Focus application areasRetailAutomotiveHome

Interactive task assistanceFamily coordinationMobile health and wellness

Intelligent System – Research ingredients

Human Brain CompetenciesVision, Audition, Touch, Proprioception, Cross-Modal

Perception

Physical Skills, Tool Use, Navigation, ProprioceptionActuation

Implicit, Working, Episodic, Semantic, ProceduralMemory

Imitation, Reinforcement, Dialogical, Written, Experimental

Learning

Deduction, Induction, Abduction, Causal, PhysicalReasoning

Tactical, Strategic, Physical, SocialPlanning

Visual, Social, BehavioralAttention

Subgoal Creation, Affect BasedMotivation

Emotional Expression, Understanding Emotions, Perceiving Emotions, Control of Emotions

Emotion

Self Awareness, Theory of Mind, Self Control. Other-Awareness, Empathy

Modeling Self and Other

Appropriate Behavior, Social Communication, Social Inference, Cooperation

Social Interaction

Verbal, Gestural, Pictorial, Language acquisition, Cross-Modal

Communication

Counting Objects, Grounded Small Number Arithmetic, Comparison of Quantitative Properties, Measuring with tools

Quantitative

Physical Construction w/ Objects, Formation of Novel Concepts, Verbal Invention, Social Organization

Building/Creation

Source: Ben Goerzel, AGI 2011