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In their proposal for the 1956 Dartmouth summer workshop, John McCarthy et al. summarized their plan for a “2 month, 10 man study of artificial intelligence”: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” While it turned out that the two months they had allocated wasn’t sufficient, in the seven decades since the workshop, enormous progress has been made on machine language processing, problem-solving, and learning. Much less progress has been seen on forming concepts and abstractions.

Consider, for example, the “simple” concept on top of. In its most concrete definition, an object or location being “on top of” another object or location refers to a spatial configuration (“the cat is on top of the TV”) but the concept can be abstracted in any number of ways: being on top of a social hierarchy, on top of one’s game, on top of the world (i.e., extremely happy), staying on top of one’s work, singing at the top of one’s voice, being born at the top of a decade, and so on.

Why the Abstraction and Reasoning Corpus is interesting and important for AI

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