at Berkeley was excited about getting me. There I taught two things: organization theory à la March and Simon, and the new discipline called Artificial Intelligence.

There were no books on the subject of AI, but there were some excellent papers that Julian Feldman and I pho-tocopied. We decided that we needed to do an edited collection, so we took the papers we had collected, plus a few more that we asked people to write, and put together an anthology called Computers and Thought that was published in 1963.

The two sections mirrored two groups of researchers. There were people who were behaving like psychologists and thinking of their work as computer models of cognitive processes, using simulation as a technique. And there were other people who were interested in the problem of making smart machines, whether or not the processes were like what people were doing.

How did choosing one of those
lead you to Stanford?

The choice was: do I want to be a psychologist for the rest of my life, or do I want to be a computer scientist? I looked inside myself, and I knew that I was a techno-geek. I loved computers, I loved gadgets, and I loved programming. The dominant thread for me was not going to be what humans do, it was going to be what can I make computers do.

I had tenure at Berkeley, but the business school faculty couldn’t figure out what to make of a guy who is publishing papers in computer journals, artificial intelligence, and psychology. That was the push away from Berkeley. The pull to Stanford was John McCarthy.

How did you decide on your
research program?

Looking back in time, for reasons that are not totally clear to me, I really, really wanted smart machines. Or I should put the “really” in another place: I really wanted really smart machines.

I wasn’t going to get there by walking down the EPAM road, which models verbal learning, or working on puz-zle-solving deductive tasks. I wanted to model the thinking processes of scientists. I was interested in problems of induction. Not problems of puzzle

ai is not much of a
theoretical discipline.
it needs to work
in specific task
environments.

solving or theorem proving, but inductive hypothesis formation and theory formation.

I had written some paragraphs at the end of the introduction to Computers and Thought about induction and why I thought that was the way forward into the future. That’s a good strategic plan, but it wasn’t a tactical plan. I needed a “task environment”—a sandbox in which to specifically work out ideas in detail.

I think it’s very important to emphasize, to this generation and every generation of AI researchers, how important experimental AI is. AI is not much of a theoretical discipline. It needs to work in specific task environments. I’m much better at discovering than inventing. If you’re in an experimental environment, you put yourself in the situation where you can discover things about AI, and you don’t have to create them.

Talk about DENDRAL.

One of the people at Stanford interested in computer-based models of mind was Joshua Lederberg, the 1958 Nobel Prize winner in genetics. When I told him I wanted an induction “sandbox”, he said, “I have just the one for you.” His lab was doing mass spectrometry of amino acids. The question was: how do you go from looking at a spectrum of an amino acid to the chemical structure of the amino acid? That’s how we started the DENDRAL Project: I was good at heuristic search methods, and he had an algorithm which was good at generating the chemical problem space.

We did not have a grandiose vision.
We worked bottom up. Our chem-
ist was Carl Djerassi, inventor of the
chemical behind the birth control
pill, and also one of the world’s most
respected mass spectrometrists. Carl
and his postdocs were world-class ex-
perts in mass spectrometry. We began
to add in their knowledge, inventing
knowledge engineering as we were go-
ing along. These experiments amount-
ed to titrating into DENDRAL more and
more knowledge. The more you did
that, the smarter the program became.
We had very good results.

What then?

We needed to play in other playpens. I believe that AI is mostly a qualitative science, not a quantitative science. You are looking for places where heuristics and inexact knowledge can come into play. The term I coined for my lab was “ Heuristic Programming Project” because heuristic programming is what we did.

For example, MYCIN was the Ph.D. thesis project of Ted Shortliffe, which turned out to be a very powerful knowledge-based system for diagnosing blood infections and recommending their antibiotic therapies. Lab mem-