knowledge engines and ai – applications beyond gaming
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Knowledge Engines and AIAadhar Garg ([email protected])Product ManagerWatson Developer Cloud
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Imagine if…
… every patient had a full-time, dedicated staff
of medical specialists.
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Imagine if…
… the time to discover new sources of energy went
from 2 years to 3 months.
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Imagine if…
… every American had affordable access to
personalized investment advice.
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Imagine if…
… legal documents could be automatically be reviewed for consistency and
accuracy with precedent.
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Imagine if…
… every student had a personal, full-time, world class tutor?
From Early AI to Cognitive Systems
AARON - The First Artificial Intelligence Creative Artist
(Harold Cohen, UCSD) 1973–present
The Aaron system composes and physically
paints novel art work. It is a rule-based expert system
using a declarative language. 7
AIBM Researcher Gerald Tesauro (1994) developed a self-teaching backgammon
program called TD-Gammon. Starting from a random initial
strategy, and learning its strategy almost entirely from
self-play, TD-Gammon achieved a human world-
champion level of performance.
On May 11, 1997, IBM’s Deep Blue (manned by co-creator Murray Campbell
above) beat the world chess champion Garry
Kasparov after a six-game match: two wins for IBM, one for the champion and
three draws.
Watson competed against Jeopardy’s two all-time
greatest champions. This match appeared on
television in February of 2011. Watson won the match, outscoring both opponents combined.
Knowledge & Interaction
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Where was Einstein born?
Knowledge & Interaction
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Welch ran this…
Big Data: This is just the beginning
You are here
Sensors & Devices
Social Media
VoIP
Enterprise Data
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Knowledge & Interaction
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Where was Einstein born?
One day, from among his city views of Ulm, Otto chose a watercolor to send to Albert Einstein as a
remembrance of Einstein´s birthplace.
Knowledge & Interaction
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Welch ran this…
If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE.
A new era of computing…Cognitive Systems learn and interact naturally with people to amplify what either humans or machines could do on their own. They help us solve problems by penetrating the complexity of Big Data. Programmable Systems
» Structured data (local)» Deterministic Applications» Search Oriented» Small Data» Machine Language» Systems of records
Cognitive Systems» Structured & unstructured (global)» Probabilistic Applications» Discovery Oriented» Big Data» Natural Language» Systems of engagementTabulating Systems Era
Programmable System Era
Cognitive Systems Era
Knowledge & Interaction
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Where was Einstein born?
One day, from among his city views of Ulm, Otto chose a watercolor to send to Albert Einstein as a
remembrance of Einstein´s birthplace.
Person Born inA. Einstein Ulm
Knowledge & Interaction
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Welch ran this…
If leadership is an art then surely Jack Welch has proved himself a master painter during his tenure at GE.
Person OrganizationJ. Welsh GE
Recent advances in AI are enabling a new generation of scenarios
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Earlier AI systems stalled due to…» Reliance on a large number of manually
designed rules for specific purposes» Lack of sufficient computational power» Trouble scaling to complexities of real
applications
Recent trends are driving change…» Probability and statistics a fundamental
formalism for AI – probabilistic reasoning, graphical models, and HMMs
» More powerful and sophisticated machine learning algorithms - DBN
» The availability of huge computing power and vast amounts of data
» Individuals overwhelmed by information overload in private and professional lives
Computers and Brain: Different & Complementary
~5 GHz, sequential, linear, clocked
Separates memory, computation, communication
100 W/cm2
~1 year / rapidly evolving architecture
10 Hz, parallel, high fanout, event-driven
Integrates memory, computation, communication
10 mW/cm2
~106 years / pace of architecture change
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The Watson Developer Cloud — A set of purpose-built, REST APIs with AI built in
http://www.ibmwatson.com/developercloud
Cognitive SystemsBuilding Cognitive Systems requires
» A deep understanding of users’ problems “in the wild”» Soft sciences: cognitive, psychology, neuroscience» Engineering: machine learning technologies, sensory components, analytics,
interaction» Application Development & Deployment: Easy to build and deliver
How can we build a Cognitive System» We know something about each discipline» We are faced with sort of an “inverse problem”
Two observations:» A small number of perspectives is not enough» Deeper relationship between these perspectives needed 19
An Engineering Approach» Don’t ignore Symbolic knowledge – it plays a vital role» Use an “atomic” structure that can learn at the highest
possible level – Deep Neural Networks» Be task oriented and use humans and automated
techniques to build the largest training data» Use Machine Learning to optimize the components of the
system» Perform accuracy tests regularly and iterate on all above
steps» System of Systems still a major challenge
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Neuro-science
Nano-technology
Super-computing
Cognitive Computing
Cognitive Science
MachineLearning
Quantum Computing
© 2015 International Business Machines Corporation 3
Thank you
Aadhar Garg ([email protected])Product ManagerWatson Developer Cloud