Marvin Minsky on Common Sense and Computers That EmoteContinued from page 2
TR: You mentioned that a computer needs to know a couple million things in order to make common-sense connections. But Lenat and his colleagues have been working on exactly that, spending years feeding common-sense knowledge into Cyc. Why is another database needed? MM: When Lenat started Cyc in 1985, it was pretty ambitious, and there was no other such project. My colleagues and I said let's wait and see how this works. And then nothing happened for a while. Lenat has done some very good things. The trouble is that Cyc is very hard to use and it's proprietary, so it isn't used by researchers much. And there are a lot of problems with his system that didn't show up earlier because there wasn't any competition. They've made it consistent, so it actually doesn't know much. Should a whale be considered a mammal or a fish? Whales have many fish-like characteristics, so most people are surprised when they hear it's a mammal. But the real answer is, it should be both. A common-sense database shouldn't necessarily be logically consistent. Lenat finally realized that they should restructure Cyc by providing for the different contexts in which a question may come up. But the database was originally structured to make things very logical, and its language is predicate calculus. Our hope is to make the Open Mind system use natural language -- which is of course full of ambiguities, but ambiguities are both good and bad. TR: What are some of the main arguments or research recommendations in your upcoming book, The Emotion Machine? MM: The main idea in the book is what I call resourcefulness. Unless you understand something in several different ways, you are likely to get stuck. So the first thing in the book is that you have got to have different ways of describing things. I made up a word for it: "panalogy." When you represent something, you should represent it in several different ways, so that you can switch from one to another without thinking. The second thing is that you should have several ways to think. The trouble with AI is that each person says they're going to make a system based on statistical inference or genetic algorithms, or whatever, and each system is good for some problems but not for most others. The reason for the title The Emotion Machine is that we have these things called emotions, and people think of them as mysterious additions to rational thinking. My view is that an emotional state is a different way of thinking. When you're angry, you give up your long-range planning and you think more quickly. You are changing the set of resources you activate. A machine is going to need a hundred ways to think. And we happen to have a hundred names for emotions, but not for ways to think. So the book discusses about 20 different directions people can go in their thinking. But they need to have extra meta-knowledge about which way of thinking is appropriate in each situation. TR: Are you saying that computers should get angry? MM: If somebody is in your way, and they won't get out of your way, you have to intimidate them or scare them or make them be afraid. That's a perfectly reasonable way to solve the problem if you're in a hurry and if something bad will happen if you can't get around them. I propose that we need about 20 different words for these ways of thinking. Then you can throw "rational" away. Minsky's The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind is scheduled to be published in hardcover by Simon & Schuster in November 2006. Minsky has published a draft of the book online. |









Comments
07/13/2006
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cassiuszedak...
10/15/2006
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santi.ontano...
10/06/2009
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nkryten
03/24/2007
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santi.ontano...
10/06/2009
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It's too bad that his book "Perceptrons", a term coined by Frank Rosenblatt, put an end for twenty years to the neural modeling approach to understanding intellgence, and may have contributed to Rosenblatt's solo boating accident on Cayuga Lake, especially since the book was a smear designed to divert funding to Minsky's "black box" approach to AI (and was fabulously successful in doing so).
The book basically discredited Rosenblatt's work by proving that a two layer Perceptron couldn't separate figure from ground, so Perceptrons weren't worthy of further investigation. Rosenblatt was aware of the simplicity of the two layer approach he was using, and did so so that we could begin to understand neural nets without the complexity of hidden layers (which could be explored later, along with other learning algorithms).
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07/14/2006
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[The book Perceptrons by Minsky and Papert] basically discredited Rosenblatt's work by proving that a two layer Perceptron couldn't separate figure from ground, so Perceptrons weren't worthy of further investigation. Rosenblatt was aware of the simplicity of the two layer approach he was using, and did so so that we could begin to understand neural nets without the complexity of hidden layers (which could be explored later, along with other learning algorithms).
I presume that you are repeating a rumor, and did not actually read the book. So far as I know, no addition of "hidden" layers will help to enable a loop-free (non-recursive) neural network to recognize or separate connected images on a retina. And because of this limitation of the networks themselves, no improvement in learning procedures will help.
Indeed, loopfree neural networks can do many useful things indeed, but no matter how many layers they have, there is no reason (or evidence) that they can surmount the topological limitations discussed in the book.
I should add that while there have been many statement that try to discredit the book, it is very strange that there has been virtually no further research to show that the same limitations do not apply to networks with multiple layers -- unless one allows unlimited numbers of inputs to each neuron. (Of course, in that case, a 2-layer network can compute any Boolean function by expressing it in disjunctive normal form.
Perhaps we did make a mistake by emphasizing the problem of computing parity, because this particular function can be computed in logarithmically few layers. However, if one cask about the "majority" function instead, I suspect that the problem remains quite difficult, and I'd like to see some of those critics demonstrate how large a network would be required to recognize whether an input contains more zeros than ones.
No such problems have been solve the kinds of invective that we see in this letter.
minsky
10/22/2006
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07/14/2006
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http://www.ieee.org/portal/pages/about/awards/sums/rosensum.html
07/20/2006
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Aloha,
Steve
07/21/2006
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07/14/2006
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07/14/2006
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07/14/2006
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nkryten
03/24/2007
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all data is a pathway / index to other data. Ideas are built up as a result of the path you follow. Each branch in the path builds up parts of the data 'solution'. Think of the most general concepts as being closer to the root of the decision tree, a very specific idea / object a leaf. See the following:
The memory code.
By Joe Z. Tsien
Scientific American, July 2007 (vol 297-1, pg 52)
Researchers are closing in on the rules that the brain uses to lay down memories. Discovery of this memory code could lead to the design of smarter computers and Robots and even to new ways to peer into the mind.
Dr. Tsien is noteworthy for creating a new strain of lab mice ‘Doogie’(s) that have enhanced memory abilities.
stan@adnamis...
07/26/2007
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07/14/2006
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07/18/2006
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07/20/2006
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Unfortunately Minsky _built_ the GOFAI road. His legacy will be "A bright guy, with lots of ideas, who led us astray for 20 years."
07/27/2006
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And the astonishing experimental work of Luc Steels at the Paris Sony Labs demonstrating how languages are created by communicating robots deserves a Nobel prize.
07/27/2006
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07/27/2006
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