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Hans Moravec – 2

little bit about some of the latest developments in robotics?

Hans: The main thing to notice about robotics is that nobody’s made any money doing it yet.

David: It seems like they have in Disney World.

Hans: Okay, there are entertainment robots. But there are no big industries making robots, and selling lots of them. Only some of the companies that have tried to do that have barely survived. Most of them went out of business. Even the Disney robots are not really making a lot of money. Maybe for Disney in the context of the entire park, but not for the companies that are making the robots. There’s a company called Sarcosink in Utah that’s made some of the very best robots that Disney uses, and they’re just a little company, living from contract to contract. But discounting entertainment robots, which have their own kind of economics, we don’t have robots cleaning your floor, vacuuming your rug, cleaning the streets, or delivering packages.

David: Right, all we have are Furbys.

Hans: Or toys. But toys don’t count. You can make a robot toy that doesn’t work at all–like wind-up toys all along–and it can still sell. The main reason we don’t have really good utilitarian robots is that actually doing work in the world is hard–although we never realized how hard it was. But just pushing a broom is very hard. It requires navigational, perceptual and motor skills that are in an absolute sense very complicated, but are cheap, because everybody that we know pretty much has them. In fact, most animals have them, although maybe not the discipline to use them the way that we need.

The reason we have them is because they were life or death matters all through our evolution. We’ve been practicing for 500 million years, and those individuals that did those things the best were the ones that survived in each generation and passed on their genes to the next. So it’s like we’ve running a repeated Olympics. Only the winners get to have offspring. We have hundreds of billions of neurons devoted to seeing, moving, modeling the world, and socially interacting. That’s just a really hard target. Building a robot to mimic that means we have to rediscover all of those things, and build a mechanism as powerful as we have in us.

We didn’t realize how hard it was, because when we first started building computers we didn’t use them for things like that. We used them for things like arithmetic, which is something that human beings often do badly. We have a hundred billion neurons, but we can only add one number every fifteen seconds. Any competent computer designer could take a few thousand of our neurons and wire them up into an adding circuit, or a more general arithmetic circuit, that could probably do a thousand calculations a second. If they took a large fraction of our hundred billion neurons and wired them up, they could make a calculator that could do a trillion calculations a second. Yet we manage one every fifteen seconds. We’re inefficient by a factor of about a quadrillion.

David: Unless you happen to be an idiot savant.

Hans: Right. But even there, they might be able to do it in a few seconds. That’s inefficient by a factor of a hundred trillion, instead of a factor of a quadrillion–still vastly bad. On the other hand, our neurons probably couldn’t be wired much better for moving around. The neurons in our visual system are probably close to optimum in how they’re organized to let us see things, because evolution’s really been working at that. Evolution, of course, didn’t give a damn about whether we could multiply two numbers. It probably wasn’t an issue at all. It’s just a side-effect of some of our general purpose thinking ability. But it’s very weak.

The general purpose part of our thinking is extremely weak compared to the specialized parts of our thinking. But the specialized parts of our thinking are only good for things that we’ve been doing for many millions of years. So when computers first did arithmetic it really seemed that these were powerful thinking machines. At first doing arithmetic was considered thinking. After all, who else but an intelligent person could do arithmetic? Then when the first AI programs started being written in the Fifties and Sixties, the computer still seemed pretty powerful. They were able to solve these new mathematical problems, intelligence test problems, and intellectual games about as well as a single person.

Already there’s a little bit of a letdown there. (laughter) We went from thousands of mathematicians to one freshman. Then when the computers were used in the first robot setups, using cameras to look at a table top with blocks on it, and an electric arm to

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