Enabling machines to reason like humans
Published: 05 Jul 2006 16:00 BST
...because some added facts may prevent it. Now, that was around 1980, or a little bit before, that formalising nonmonotonic reasoning began, and it's turned into a fairly big field now.
What would be the biggest achievements in the last 50 years? Or how much of the original goals were accomplished?
Well, we don't have human-level intelligence. However, I would say driving the car 128 miles shows a considerable advance. (Editors' note: In last autumn's DARPA Grand Challenge, the winning vehicle — Stanford's robotic car, "Stanley" — drove itself 131.6 miles across the Mojave Desert.)
What's the next big thing, then, to accomplish?
I would like to see further progress in formalising commonsense knowledge and reasoning, taking context into account. That's something I've been working on for a long time and that some other people also work on, and which DARPA supports, but I think the ideas that are available are not sufficient to reach human-level intelligence.
A goal in AI is not so much to make machines be like humans, having human intellectual capabilities, but to have the equivalent of human intellectual capabilities, correct? In other words, not reinventing the human but creating something that thinks similarly to humans and surpasses human thought?
That's the way I see the problem. There are certainly other people who are interested in simulating human intelligence, even aspects of it that are not optimal. In particular, Allen Newell and Herbert Simon tended to look at it that way.
Another sort of high-level goal that may or may not be reachable seems to be to try to program originality into machine thinking.
Yes. That would be worth some efforts. I did something that was part way to that in 1963, in which I talked about a creative solution to a problem, a solution that involved elements that were not in the problem, the statement itself. But that was just a start.
And originality — is that as simple as trying to introduce some randomness into the programs, or was it a different order of magnitude?
Well, in principle, in a logical system, you could generate sentences systematically or randomly... and any idea would eventually turn up, but the "eventually" is likely to be extremely far in the future. So that hasn't done much, either using randomness or otherwise. What's needed is to figure out good ways of constructing new ideas from old ones.
Going back for a second to the notion of having machine capability versus programming and the right source of ideas — today we have so much more computational capability than was available 50 years ago. What difference is that making, with the state of the art of computer chips and memory these days?
I would say that 50 years ago, the machine capability was much too small, but by 30 years ago, machine capability wasn't the real problem.
The real problem still being the basic ideas?
Yes.
How do robots factor into thinking about artificial intelligence? I guess in the popular vision (in movie images of humanoid robots), that's where people would tend to see human-level intelligence, but are robots a real factor, or does it really matter what shape or form the machine takes?
Certainly, robots present some problems. That is, they have to operate in an environment, and some of the even rather elementary problems have not been solved yet — that is, combining the ability to walk the way a human walks, which is falling forward rather than just shuffling, and with the ability to understand a three-dimensional scene and so forth. These ideas have been worked on sort of separately...
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