What is intelligence, in the context of machine learning and AI? A classic from 1979, Hofstadter’s GEB, gives eight essential abilities for intelligence:
- to respond to situations very flexibly
- to take advantage of fortuitous circumstances
- to make sense out of ambiguous or contradictory messages
- to recognize the relative importance of different elements of a situation
- to find similarities between situations despite differences which may separate them
- to draw distinctions between situations despite similarities which may link them
- to synthesize new concept by taking old concepts and putting them together in new ways
- to come up with ideas which are novel
It seems to me that the keyword is “flexibility“. Our world is complex, and a creature must be able to act in an infinite variety of circumstances. Sometimes a simple rule is enough, sometimes you need a combination of rules, and sometimes a totally new rule is required.
Hofstadter’s usage of the term stereotyped response got me thinking about Kahneman’s System 1 and 2. In those terms, it seems that System 1 covers all eight abilities. System 1 is fast thinking, applying a stereotypic solution to a situation. No actual reasoning or logical “thinking” is required to fulfil the requirements. However, the stereotypic solutions or rules must be flexible.