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Artificial Intelligence:Prospects for the 21st Century
Henry KautzDepartment of Computer Science
University of Rochester
What is Artificial Intelligence?
• Study of principles for understanding and building intelligent agents– Human, animal, or mechanical – How to perceive the world– How to reason and make decisions– How to learn – How to act (motion, speech)– How to cooperate with other agents
Can’t Win Definition of AI
• AI = making a computer solve a problem that requires human intelligence– By definition, any problem solved by
AI no longer requires human intelligence
– So, AI never succeeds!• Useful idea: study tasks people
perform in order to understand intelligence
Outline
• Approaches to AI– Task based (“Classical AI”)– Neural networks
• Which Way Will Achieve AI?– Criticisms– Ray Kurzweil’s Perspective– A Middle Ground
Classical AI
• The principles of intelligence are separate from the hardware (or “wetware”)
• Look for these principles by studying how to perform individual tasks that require intelligence
Success Story: Medical Expert Systems
• 1980: First expert level performance– diagnosis of blood infections
• Today: 1,000’s of systems – Often outperform doctors
Success Story:Chess
I could feel – I could smell – a new kind
of intelligence across the table- Garry Kasparov
(1997)
•Examines 5 billion positions / second
•Intelligent behavior emerges from brute-force search
Success Story: Robotics (1)
Rendezvoused with an asteroid, 1998-2000 Capable of autonomous diagnosis & repair
Success Story: Robotics (2)
• DARPA Grand Challenges, 2004-2007– Races in desert and urban environments
by fully autonomous vehicles– Succeeded with “off the shelf” AI
technology!
Neural Networks
• Develop computational models of the brain at the neural level– McCulloch & Pitts model (1943): very
simple, but a pretty good approximation of most real neurons
Success Story: Face Recognition
• Programming a neural net that learns to recognize faces can now be done as homework problem!
Which Approach Will Achieve AI?
• Criticism of Classical AI:– Successes so far are in all narrow
domains– We can never explicitly program enough
“commonsense” into a AI system to make it a true general intelligence
– The human brain has a completely different architecture than a modern computer
Which Approach Will Achieve AI?
• Criticism of Neural Networks:– Successes so far are in all narrow
domains– Building an AI by studying neural
processes is like trying to reverse-engineer Windows Vista by watching bits
– “Summation and threshold” is just another kind of logic gate!
Ray Kurzweil
• Kurzweil believes that in a few years we will have a complete wiring diagram of the brain
• So, the neural net approach wins…• But we still may not understand
why the brain works!
A Middle Ground
• Most AI researchers (including me) believe that AI will be accomplished by a combination of ideas from both camps– Studying tasks tells us what needs to be
computed– Studying brains tells us what classes of
algorithms are possible– We can implement those algorithms in
many ways
A Middle Ground
• Neural nets are not necessary the best way to implement all the thing the brain does!– Evolution rarely produces optimal solutions!
• Machine learning is compatible with both the classical and neural net approaches– Learning from text on the Internet will solve
the problem of getting enough “commonsense” information