Steve Omohundro talk on “Learning and Recognition by Model Merging”

A talk given by Steve Omohundro on “Learning and Recognition by Model Merging” on 11/20/1992 at the Sante Fe Institute, Sante Fe, New Mexico. It describes the very general technique of “model merging” and applies it to a variety of learning and recognition tasks including visual learning and recognition and grammar learning. It also contains a general description of techniques to avoid overfitting and the relationship to Bayesian methods. Papers about these techniques and more advanced variants can be found at:http://steveomohundro.com/scientific-contributions/

What is intelligence?

There is a large literature on human intelligence. John Carroll’s classic “Human Cognitive Abilities: A Survey of Factor-Analytic Studies” identifies 69 distinct narrow abilities but finds that 55% of the variance in mental tests is due to a common “general intelligence” factor “g”. The leading AI textbook, Artificial Intelligence: A Modern Approach, considers 8 different definitions of intelligence and Legg and Hutter lists over 70. For our purposes, we use the simple definition:

“The ability to solve problems using limited resources.”

It’s important to allow only limited resources because many intelligence tasks become easy with unlimited computation.  We focus on precisely specified problems such as proving theorems, writing programs, or designing faster computer hardware. Many less precise tasks, such as creating humor, poetry, or art, can be fit into this framework by specifying their desired effects, eg. “Tell a story that makes Fred laugh.” Philosophical aspects of mind like qualia or consciousness are fascinating but will not play a role in the discussion.