On the thresholds of knowledge

摘要

Three major findings in the domain of artificial intelligence are articulated. The first is the knowledge principle, which states that if a program is to perform a complex task well, it must know a great deal about the world in which it operates. The second is a plausible extension of that principle, called the breadth hypothesis, which states that there are two additional abilities necessary for intelligent behavior in unexpected situations: falling back on increasingly general knowledge, and analogizing to specific but far-flung knowledge. The third finding is a concept of AI as an empirical inquiry system requiring the experimental testing of ideas on large problems. It is concluded that together these concepts can determine a direction for future AI research.