Top-Down Engineering of AI

The philosophers’ fascination with propositions was mirrored in good old-fashioned AI, the AI of John McCarthy, early Marvin Minsky, and Allen Newell, Herbert Simon, and Cliff Shaw. It was the idea that the way to make an intelligent agent was from the top down. You have a set of propositions in some proprietary formulation. It’s not going to be English—well, maybe LISP or something like that, where you define all the predicates and the operators. Then, you have this huge database that is beautifully articulated and broken up into atoms of meaning, which have the meaning they have by being part of the system they’re part of. You stipulate their meanings, and then you have a resolution theorem prover that sits on top of that, and this is how we’re going to generate a thoughtful, creative mind. It was a very attractive dream to many people, and it’s not quite dead yet.

This early attempt in AI, which went through well into the ‘80s, gave us many wonderful things. It’s important to recognize how much we now take for granted in AI and in computers that grew out of the explorations of the good old-fashioned AI pioneers. But it hit a brick wall. McCarthy and Hayes discovered the frame problem. There were other intractable issues, and then along came connectionism, and then, more recently, reinforcement learning and deep learning. People have moved away from this ideal of a canonical expression of specific propositions as in an axiom system.

I’ve used the term good old-fashioned AI or GOFAI, which was coined by my late dear friend John Haugeland in his book, Artificial Intelligence: The Very Idea. He was an early influential and knowledgeable critic of the field. He deserved the right to come up with the amusing name “good old-fashioned AI” when it was still regarded as very promising by many people. He was already foreseeing its demise correctly in many regards, but the fact is that the fruits of good old-fashioned AI are all around us.

In fact, the Internet is largely based on good old-fashioned AI. When people talk today about semantic search as opposed to string search, they’re talking about going beyond what you can do with the methods of good old-fashioned AI, which is string search till the cows come home, go deeper and get at the semantic meanings of what’s out there.


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 "A Difference That Makes a Difference"
Electronic/World Wide Web>Internet Article:  Dennett , Daniel C. (11.22.17), "A Difference That Makes a Difference", Retrieved on 2017-11-22
  • Source Material []
  • Folksonomies: information thought ai