Nils John Nilsson
Nils John Nilsson (February 6, 1933 – April 23, 2019) was an American computer scientist. He was one of the founding researchers in the discipline of artificial intelligence. He was the first Kumagai Professor of Engineering in computer science at Stanford University from 1991 until his retirement. He is particularly known for his contributions to search, planning, knowledge representation, and robotics
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[edit]- I recorded all of that and I looked at it. I've forgotten some of the questions that were asked. Well, for example, on this Toronto thing. Remember the example in which it was asked, well the clue was World War II aviators or naval battles for which airports were named. And the answer would be Midway and Chicago and O'Hare. And it mentioned something in Toronto, I think, even though it was supposed to be a US city. And I think the people who designed it explained that well, oftentimes the category US city doesn't really mean exactly US city, it's sort of general. And so, Watson didn't count that as heavily as it should have. But, maybe it had some inaccurate common sense that allowed it to answer Toronto. But, anyway it was a failure of some sort of common sense reasoning.
- Well, if you had asked me that a few years ago I might have said "No," because I think that whatever the power of computing was at the time it was fully able to handle any of the ideas that we had at the time. I mean, we were idea short, we weren't hardware short mainly. Now, I'm not so sure because the new idea that's come up, the use of lots and lots of data, well, lots and lots of data requires lots and lots of computing. And so, the more computing, the faster it can go the better. I don't know that that's the bottleneck at the moment. After all if you talk to the Watson people which I haven't, but if you did talk to them and you asked them, "Gee, how could Watson have been better? If you had a computer, this IBM 7000 series or whatever it was, if it were 10 times as fast, 100 times as fast, 100 times as much memory would you have done better?" I think they'd still answer "Well, not necessarily. We would need more ideas about how to program all that."
- Well, large corpuses of data are going to be useful in lots of them. I don't know that it'll solve all the problems in AI. I mean, right now we can't do all the things that humans can do. Look around you and you can see. I mean, we have office buildings full of people. And many of them aren't using their hand eye coordination. It isn't mechanical engineering which is a problem. You might ask, why are they there? What are they doing? Well, they're having meetings, they're filling out paperwork, they're doing studies, they're communicating with other humans, they're making plans. And those are things, why can't computers do all that? Why is there anybody in those buildings except the janitors and maybe a few top bosses? Well, and computers would be cheap if we could do it, cheaper than those people. And so, why are they there? Well, because computers can't do it yet. And will lots and lots of data solve that problem? I don't think so. Might help, might be part of the solution, but the reason we don't have what you might call human level intelligence yet is that we just don't have the ideas needed in order to write the programs that would allow us to achieve human level AI. But, we have a lot of smart people and I think we're making some progress.
- It will be a problem in the end, I think, for society what happens, what do we do with all the people that computers replace? And eventually, I mean, right now you need more and more skills in order to have jobs. But, there's this guy Robin Hansen, you know about Robin Hansen? Robin Hansen is an economist and he has got this interesting metaphor of sea level rising. Sea level is what computers can do. And land and the land that's inhabited, the jobs that require humans to do. And sea level's been rising. And on the shore a lot of people are displaced. Well, they've had to move to higher levels. But, to move to higher levels they have to have more training. Now, the fact that sea level's rising itself makes some higher levels. It's a funny thing. At sea level build some mountains so people can climb those mountains, but sea level will keep rising. And the question is will it rise above even those mountains? And so, what do we end up having people do?
- Well, there's certain jobs that only people can do. You can't have a machine make sweaters made by hand. And there's kinds of things which involve social interaction which only people can do. Some of the social interaction maybe machines can do. But, if you really want a human you got to have a human. And so, I'm not saying that all jobs will be replaced, but we're already seeing a trend of many and I think that trend will continue and you read people who talk about the current slow recovery, the economic situation, and many people say "Well, we've laid off a lot of people because of the recession, but in the meantime we've found out we could do some of those jobs that those people did with machines and by the way, we're not going to hire those people back." And so, I think that's going to be a continuing difficulty.
- Right now, I don't know, I think I'd try to get involved in the bridge between AI and neurophysiology. Let's take neuroscience in particular because I think there's still a lot of secrets about how the brain works that we don't understand that'll be helpful in engineering. The reason we don't understand them, I think, is we haven't invented the concepts needed to understand them. I have this analogy with computers. If you had a Martian coming down looking at computers, measuring all the currents flowing back and forth from the transistors, no amount of all that measuring, no amount of understanding how a transistor works is going to tell you how say an online banking system works or how an airline reservation system works. You have to have concepts that got invented in computer science to even understand them. You have to have concepts like lists, programs, data structures, compilers and there are probably a whole set of analogous concepts in helping us understand the brain.
- So, yes it's important to understand how a neuron works and a lot of neurophysiologists might complain that models of the neurons that the AI people are cooking up aren’t accurate enough. Well, that might be, but no amount of detailed understanding of an individual neuron or even how neurons get interconnected I think will be sufficient to have a good explanation of how it is that we do what we do. We have to have some higher level things and there are people working on that. But, some of it, I think, will get developed by those very people who have one foot in artificial intelligence and one foot in neuroscience to say, "Ah, the analogy to these high level programs is such and such to the way the brain does this." And they will invent concepts that will then help us understand the brain better.
