Wolfram: Probably you expect me to say it's because our computers aren't fast enough, thinking is a difficult thing to get, and so on. But I really don't think so. I think it's just a historical accident. Sometime -- perhaps ten years from now, perhaps twenty-five -- we'll have machines that think. And then we'll look back on the 1990s and ask why the machines didn't get built then. And I'm essentially sure the reason won't be because the hardware was too slow, or the memories weren't large enough. It'll just be because nobody had the key idea or ideas. You see, I'm convinced that after it's understood, it really won't be difficult to make artificial intelligence. It's just that people have been studying absolutely the wrong things in trying to get it.

The history of AI is quite interesting. I think in many ways it's a microcosm of what's wrong with science and academia in general. Everyone knows that when computers were first coming out in the 1940s and 1950s many people assumed that it'd be quite easy to make artificial intelligence. The early ideas about how to do it were, in my view, pretty sensible, at least as things to try -- simple neural nets, stuff like that. But they didn't work very well. Why not? Probably mostly because the computers in those days had absolutely tiny memories. And to do anything that remotely resembles what we call thinking one has to have a fair amount of knowledge -- and that takes memory. Of course, now any serious computer can easily store an encyclopedia. So by now that problem should have gone away.

Well, anyway, after the failures of the early brute-force approaches to mimicking brains and so on, AI entered a crazy kind of cognitive engineer- ing phase, where people tried to build systems which mimicked particular elaborate features of thinking. And basically that's the approach that's still being used today. Nobody's trying more fundamental stuff. Everyone assumes it's just too difficult. Well, I don't think there's really any evidence of that. It's just that nobody has tried to do it. And it would be considered much too looney to get funded or anything like that.

Stork: So, what kind of approach do you think will work in building intelligent machines?

Wolfram: I don't know for sure. But I'm guessing that a key ingredient is going to be seeing how computations emerge from the action of very simple programs -- the kind of thing that happens in the cellular automata and other systems I've studied. I think that trying to do engineering to mimic the high-level aspects of thinking identified by cognitive scientists or psychologists is not going to go anywhere. Thinking is, I'm pretty sure, a much lower-level process. All those cognitive things are just icing on the cake -- not fundamental at all. It's like in a fluid: there are vortices that one sees. But these vortices are not fundamental. They are a complicated consequence of the microscopic motions of zillions of little molecules. And the point is that the rules for how the vortices work are fairly complicated -- and hard to find for sure. But the rules for the molecules are fairly simple. And I'm guessing that it's the same way with the underlying processes of thinking.

Stork: So, do you really think we can get a handle on profoundly hard, high-level problems of AI -- such as my favorite, scene analysis -- by looking at something as simple as cellular automata? After all, it seems like an enormous gulf between the operation of groups of "dumb" cells -- each obeying a simple rule, based on the values of neighboring cells -- and the exquisite subtlety of high-level reasoning, memory, vision, language, and so on.

Wolfram: Definitely. But it takes quite a shift in intuition to see how. In a sense it's about whether one is dealing with engineering problems or with science problems. You see, in engineering we're used to setting things up so we can explicitly foresee how everything will work. And that's a very limiting thing. In a sense, you only ever get out what you put in. But nature doesn't work that way. After all, we know that the underlying laws of physics are quite simple. But just by following these laws, nature manages to make all the complicated things we see.