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TR: Why do people shy away from the common-sense problem?

MM: I think people look around to see what field is currently popular, and then waste their lives on that. If it’s popular, then to my mind you don’t want to work on it. Now, physics is different. There, people say “This popular theory works pretty well, but it doesn’t explain this or that – so I should look at that.” But when people write AI papers, they only tell what their program did, and not how it failed or what kinds of problems it couldn’t solve. People don’t consider the important problem to be the one their system hasn’t solved. People have gotten neural networks to recognize that if you are looking for a taxi, for example, you should look for a yellow moving object. But they don’t ask how come these networks can’t answer other kinds of questions.

TR: But understanding common sense is a much harder problem, isn’t it? Couldn’t that explain why so many AI researchers go into other areas?

MM: That’s true. Back when I was writing The Society of Mind, we worked for a couple of years on making a computer understand a simple children’s story: “Mary was invited to Jack’s party. She wondered if he would like a kite.” If you ask the question “Why did Mary wonder about a kite?” everybody knows the answer – it’s probably a birthday party, and if she’s going that means she has been invited, and everybody who is invited has to bring a present, and it has to be a present for a young boy, so it has to be something boys like, and boys like certain kinds of toys like bats and balls and kites. You have to know all of that to answer the question. We managed to make a little database and got the program to understand some simple questions. But we tried it on another story and it didn’t know what to do. Some of us concluded that you’d have to know a couple million things before you could make a machine do some common-sense thinking.

TR: As people have realized how difficult it is to get a computer to understand even simple common-sense situations, would you say that some of the optimism around the possibilities for AI in the 1950s and 1960s has dissipated?

MM: I don’t think optimism is the right word. I think we were asking good questions, but somehow most of the people working on what they called AI started looking for one of these universal solutions. In physics, that worked; there were Newton’s equations and then Maxwell’s and then relativity and quantum theory. Most AI people are trying to imitate that and find a general theory. But humans have 100 different brain centers that all work in slightly different ways. You shouldn’t be working on a single solution; you should be working on a host of gadgets.

TR: A lot of the funding for AI has come from the Defense Advanced Research Projects Agency (DARPA), where there’s a pretty clear demand for practical results. In fact, they’re one of the sponsors of the Dartmouth AI conference. How has DARPA shaped the direction of AI research?

MM: In the early days, DARPA supported people rather than proposals. There was a lot of progress from starting in 1963; for about ten years the kinds of things I am talking about did flourish. And then in the early 1970s there was a kind of funny accident. Senator Mike Mansfield, quite a liberal, decided that the Department of Defense shouldn’t be supporting civilian research. So he was responsible for ARPA becoming DARPA, and straining not to compete with industrial and civilian research. So it became much harder for them to support visionary researchers.

At the same time, the American corporate research community started to disappear in the early 1970s. Bell Labs and RCA and the others essentially disappeared from this sort of activity. And another thing happened: the entrepreneur bug hit. By the 1980s, many people were starting to try to patent things and start startups and make products, and that coincided with the general disappearance of young scientists. People who could have become productive scientists are now going into law and business.

So there’s no way to support this research. If you have a good idea, it’s hard to get it published because people say “Where’s your experiment?” But the trouble with common-sense thinking is that you can’t experiment until you have a big common-sense database. There is one called Cyc, started by Doug Lenat in 1985. And we have the Open Mind database, which is publicly available but not very well structured yet. But it’s a whole research project just to figure out how to open up the Open Mind database.

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