Artificial Intelligence: Prospects for the 21st Century

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Artificial IntelligenceProspects for the 21st
Century

• Henry Kautz
• Department of Computer Science
• University of Rochester

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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

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Cant 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

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Outline

• Approaches to AI
• Task based (Classical AI)
• Neural networks
• Which Way Will Achieve AI?
• Criticisms
• Ray Kurzweils Perspective
• A Middle Ground

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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

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Success Story Medical Expert Systems

• 1980 First expert level performance
• diagnosis of blood infections
• Today 1,000s of systems
• Often outperform doctors

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Success StoryChess

• 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

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Success Story Robotics (1)
Rendezvoused with an asteroid, 1998-2000 Capable
of autonomous diagnosis repair
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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!

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Success Story Text to Speech

• Kurzweil Reading Machines, 1978-2006

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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

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Success Story Face Recognition

• Programming a neural net that learns to recognize
  faces can now be done as homework problem!

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Success Story Brain-Computer Interfaces
Miguel Nicolelis (2003), Duke University
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Success Story MRI Imaging of Specific Thoughts
Tools
Buildings
Food

• Tom Mitchell (CMU) 2006

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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

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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!

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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!

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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

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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