Voices in AI – Episode 45: A Conversation with Stephen Wolfram

As people often say, the weather has a mind of its own. The question is, can we distinguish the kind of intelligence, the kind of mind, in effect, that is associated with the computations that go on in fluid mechanics, from the kind of intelligence that we have in our brains. I think the answer is, ultimately, there really isn’t a bright line distinction between those. The only thing that is special about the intelligence that we have, is that it’s connected to our kind of thread of history and our kind of biological evolution, and the evolution of our civilization and things like this. I don’t think we can say that there’s something we can’t distinguish at some sort of scientific level. What’s that essential feature that means that brain is intelligent, and the weather doesn’t really have a mind, so to speak. I think the thing that’s interesting about modern computation in AI is that we’re seeing our first examples of some kind of alien intelligence. We’re seeing our first examples of things that clearly have attributes very reminiscent of human-like, what we have traditionally called intelligent behavior. But yet, they don’t work in anything like the same way and we can argue back and forth forever about is this really intelligence or not. And I think it becomes just a question of what do we choose to make the word mean.

In my life I’ve been involved in a lot of, kind of, making computers do things that before only humans could do. And people had often said, “Oh, well, when computers can do this or that thing, then we’ll know they’re intelligent.” And one could go through the list of some of those things whether it’s doing mathematical computation or doing image recognition, or doing whatever. Every time when computers actually managed to do these things the typical response is, “Oh, well, that isn’t really intelligence because…” Well, because what? Usually, the real reason people think it isn’t really intelligence is because somehow you can look inside and see how it works. Now, of course, to some extent, you can do that with brains too. But I think one of the things that’s sort of new in recent times, is something that I’ve long been expecting, anticipating, working on actually, which is the appearance of computation that is doing things that are really interesting to humans but where we as humans can’t really understand what’s going on inside. In other words, the typical model of computation has been, you want to build a program for a particular task, you the human engineer, put the pieces together in a kind of very intentional way where you know, when I put this piece, and this piece, and this piece together then it’s going to do this, and that’s what I wanted it to do. Well, for example, I’ve been interested for a really long time in, what I call, mining the computational universe of possible programs. Just studying simple programs, for example, then going and searching trillions of them to find ones that behave in particular ways that turn out to be useful for some purpose that we have.

Well, the thing that’s happened in modern times with deep learning and neural networks, and so on, is it’s become possible to do that same kind of program discovery in a slightly different way than I’ve done it, because it turns out that one can use actually the ideas of calculus to make incremental progress in finding programs that do the things one wants them to do. But the basic idea is the same, that is, you are, by some criterion, you’re finding from this, sort of, computational universe of possible programs, you’re finding programs that serve some purpose that’s useful to us. Whether that purpose is identifying elephants from tea cups, or whether that purpose is translating between human languages or whatever else. And, the thing that is interesting and maybe a little bit shocking right now, is the extent to which when you take one of these programs that have been found by, essentially search, in this space of possible programs, and you look at it, and you say, “How does it work?” And you realize you really don’t know how it works. Each individual piece you can identify what it’s doing, you can break it down, look at the atoms of the program and see how they work. But when you say, “What’s the big picture? What’s it really doing? What’s the ultimate story here?” The answer is we just don’t know.

You mean like move 37 in AlphaGo? This move that even the greatest player in the world was like, “What?”

I haven’t followed that particular system. But I tried to program a computer to play Go in 1973 and discovered it was hard.

But to back up a minute, wouldn’t you say Google passed that point a long time ago? If you say, “Why did this page rank number two and not number three?” Even Google would look at it and go, “I don’t know. Who knows?” It’s an alchemy of so many different things.

I don’t know, I haven’t seen the source code of the Google search engine. I know in my own search engines, search systems are kind of messy. Hundreds of signals go in and they’re ranked in some way or another. I think in that particular case, the backtrace of, “OK, it was these signals that were important in this thing.” I think, to some extent, it’s a little simpler, but it’s the same. That’s a case where it tends to be more of a, I think, one-shot kind of thing. That is, you evaluate the values of these signals and then you say, “OK, let’s feed them into some function that mushes together the signals and decides what the ranking should be.” I think what tends to be a more shocking, more interesting, it hasn’t really happened completely with the current generation of deep learning neural nets, although it’s beginning to happen. It has happened very much so with the kind of programs that I studied a lot, like cellular automata, and a bunch of the kinds of programs that we’ve discovered, sort of, out in the computational universe that we use to make Wolfram Alpha work, and to make lots of other algorithms that we build work. In those kinds of programs what happens is, it’s not just, a one-shot thing where it’s just this one messy function that’s applied to some data to get a result. It’s a sequence of, actually not very messy, steps. Often, a sequence of simple, identical steps, but together, you apply it 10,000 times, and it’s really unclear what’s going on.

I want to back up if I can just a minute, because my original question was, what is intelligence? And you said it’s computation. You’re well known for believing everything is computation — time and space, and the hurricane, and the icicle, and DNA and all of that. If you really are saying everything is intelligence, isn’t that like, to beg the question, like you’re saying, “Well, everything’s intelligence.” What is it? I mean, for instance, the hurricane has no purpose. You could say intelligence is a purposeful action with the goal in mind.

Purposeful action? You’re then going to slide down another slippery slope. When you try and start defining purpose, for us as humans, we say, “Well, we’re doing this because…” and there’ll be some story that eventually involves our own history, or the history of our civilization, or our cultural beliefs, or whatever else and it ends up being really specific. If you say, “Why is the earth going around in its orbit?” Does it have a purpose? I don’t know. You could say, “Well, it’s going around in this particular orbit because that minimizes the action,” in a, sort of, technical term in mechanics, associated with this mechanical system. Or, you could say it’s going around in its orbit because it’s following these equations. Or, it’s going around in its orbit because the solar system was formed in this way and it started going around in its orbit. I don’t think that when we talk, with the human explanations of purpose, they quickly devolve into a discussion of things that are pretty specific to human history, and so on. If you say, “Why did the pulsar magnetosphere produce this blip?” Well, the answer is there’ll be a story behind it. It produced that blip because there was this imperfection, and there was a space-time position, something in the crust of the pulsar, a neutron star, and that had this consequence, and that had this consequence, and so on. There’s a story.

Well, you’re convoluting words. ‘Because’ is intentional, but ‘how’ is not. So, the question you’re asking is, “How did that happen?” And that is bereft of purpose and therefore bereft of intelligence. But, to your point, if computation is intelligence, then, by definition, there’s no such thing as non-intelligence. And I’m sure you’ve looked at something and said, “That’s not very intelligent.”

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