It's very much connected with the things I talk about in A New Kind of Science. It took me more than ten years to understand it. But the key point is that even though their underlying rules are really simple, systems like cellular automata can end up doing all sorts of complicated things -- things completely beyond what one can foresee by looking at their rules and things that often turn out to be very much like what we see in nature.

The big mistake that gets made over and over again is to assume that to do complicated things one has to set up systems with complicated rules. That's how things work in present-day engineering, but it's not how things work in nature -- or in the systems like cellular automata that I've studied.

It's kind of funny: one never seems to imagine how limited one's imagination is. One always seems to assume that what one can't foresee isn't possible. But I guess that's where spending fifteen years doing computer experiments on systems like cellular automata instills some humility; over and over again I've found these systems doing things that I was sure wouldn't be possible -- because I couldn't imagine how they'd do them.

It's like bugs in programs. One thinks a program will work a particular way, and one can't imagine that there'll be a bug that makes it work differently. I guess intuition about bugs is a pretty recent thing. In 2001 there's a scene where HAL talks about the fact that there's never been a computer error in the 9000 Series. The notion of unforeseen behavior that isn't due to hardware malfunction simply isn't there.

Anyway, about hard problems in AI, my own very strong guess is that these will be solved, not by direct engineering-style attacks, but by building things up from simple systems that work a bit like cellular automata. It's somewhat like the hardware-versus-software issue we discussed earlier. In the end I don't think elaborate special-purpose stuff will be needed for problems like scene recognition; I think they'll be fairly straightforward applications of general-purpose mechanisms. Of course, nobody will believe this until it's actually been done.

Stork: So, have you yourself worked much on the problem of building intelligent machines?

Wolfram: Well, since you ask, I'll tell you; the answer is yes. I don't think I've ever mentioned it in public before. But since you asked the right question: yes, I have been interested in the problem for a very long time -- probably for twenty years now -- and I've been steadily picking away at it. I've been held back by a lack of tools, both practical and conceptual. But that's finally getting sorted out. I have Mathematica from the practical side to let me do experiments easily. And I have my new science, from which I think I've figured out some of the basic intuition that's needed. And I even have my company -- headquartered in Champaign-Urbana, HAL's birthplace, as chance would have it -- that can potentially support my efforts. But I guess I'll have to disappoint you. We won't be announcing a machine that thinks in 1997. It'll just be Mathematica version X and A New Kind of Science from me. But wait for another year, though. Perhaps in 2001 ...