Artificial Intelligence: Prospects for the 21 st Century Henry Kautz Department 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, Capable of autonomous diagnosis & repair

Success Story: Robotics (2) DARPA Grand Challenges, –Races in desert and urban environments by fully autonomous vehicles –Succeeded with “off the shelf” AI technology!

Success Story: Text to Speech Kurzweil Reading Machines,

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!

Success Story: Brain-Computer Interfaces Miguel Nicolelis (2003), Duke University

Success Story: MRI Imaging of Specific Thoughts Tom Mitchell (CMU) 2006 ToolsBuildingsFood

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