Marvin Minsky on Common Sense and Computers That EmoteAs artificial intelligence research celebrates its 50th birthday, the MIT icon asks what makes the minds of three-year-olds tick.
Top computer scientists from around the world are meeting today at Dartmouth College in Hanover, NH, to mark the 50th anniversary of "artificial intelligence." Back in 1956, John McCarthy, then a member of Dartmouth's mathematics faculty, invented the term for the field's seminal gathering, the Dartmouth Summer Research Project on Artificial Intelligence. McCarthy and four other participants in the 1956 project, including MIT's Marvin Minsky, are participating in this week's meeting, which focuses on AI's next 50 years.
Mathematical and philosophical breakthroughs by Alan Turing, John von Neumann, Herbert Simon, Allen Newell, and other giants of computer science made the 1950s a time of great optimism about machine intelligence. Researchers believed they would soon be able to program computers to simulate many forms of human reasoning. Expert systems would embody and manipulate knowledge in the form of symbolic logic. Artificial neural networks would be trained to evolve toward correct answers. This optimism even spilled over into popular culture, where HAL, the intelligent (and profoundly disturbed) computer in Stanley Kubrick's 1968 film 2001: A Space Odyssey, upstaged the human actors. But by the late 1960s it was clear that approximating even child-like human reasoning in a computer would require vastly complex webs of logical equations or neural connections. So researchers retrenched. They began breaking down problems, focusing on replicating simple human feats such as moving children's blocks (the subject of Stanford computer scientist Terry Winograd's now-famous program SHRDLU, which used natural-language instructions to manipulate a robotic arm). Minsky, who will open the Dartmouth conference with McCarthy, admired Winograd's work. But he's long eschewed reductionistic demonstrations in favor of exploring the real mechanisms behind human thought. Working with Seymour Papert in the MIT AI Lab, for instance, Minsky began in the 1970s to develop the "Society of Mind" theory, which posits that layers of purposeful yet mindless "agents" work together to generate consciousness. Technology Review interrupted Minsky on July 11, as he was proofing the galleys for his forthcoming book, The Emotion Machine, which reinterprets the human mind as a "cloud of resources," or mini-machines that turn on and off depending on the situation and give rise to our various emotional and mental states. Technology Review: Can you believe that it's been 50 years since the first Dartmouth AI meeting? Does it feel like five decades have passed? Marvin Minsky: I haven't experienced many intervals of 50 years, so it's hard for me to say. TR: Fair enough. So, what are your thoughts about the state of AI research today, compared to where it was in 1956? MM: What surprises me is how few people have been working on higher-level theories of how thinking works. That's been a big disappointment. I'm just publishing a big new book on what we should be thinking about: How does a three- or four-year-old do the common-sense reasoning that they're so good at and that no machine seems to be able to do? The main difference being that if you are having trouble understanding something, you usually think, "What's wrong with me?" or "What's wasting my time?" or "Why isn't this way of thinking working? Is there some other way of thinking that might be better?" But the kinds of AI projects that have been happening for the last 30 or 40 years have had almost no reflective thinking at all. It's all reacting to a situation and collecting statistics. We organized a conference about common-sense thinking about three years ago and we were only able to find about a dozen researchers in the whole world who were interested in that.
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Software That Learns from Users
11/30/2007
Massachusetts Institute of Technology
Comments
07/13/2006
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cassiuszedak...
10/15/2006
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nkryten
03/24/2007
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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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