From Cognitive Computing to Artificial Intelligence Dr Peter WaggettDirectorEmerging TechnologySeptember 2016 IBM TrueNorth neuromorphic computing

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From Cognitive
Computing to Artificial
Intelligence
Dr Peter Waggett
Director
Emerging Technology
September 2016
Follow us @IBMWatson

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About Me
Dr Peter Waggett
PhD in Rocket Science
Failed Astronaut
Over 20 years IT Research Experience

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Smarter Planet Vision
Instrumented
Interconnected
Intelligent
Interactive

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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

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Cognitive Computing
Grand Challenge

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IBM Watson and Jeopardy

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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?

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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

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Attribute extraction
Medications
SymptomsDiseases
Modifiers

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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

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Artificial Intelligence ??

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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.

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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.

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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

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Brain-inspired systems will allow better analysis of sensory data

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16
Neuromorphic Image Matching

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Recognition

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Brain-inspired devices will play diverse, critical roles

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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

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”Wetware”

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Prediction is Difficult ….......
Especially About the Future
(Niels Bohr)

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