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The summer rendezvous in the Connecticut River Valley town of Hanover, N.H., served as a springboard for discussions on ways that machines could simulate aspects of human cognition: How can computers use language? Can machines improve themselves? Is randomness a factor in the difference between creative thinking and unimaginative competent thinking?
The underlying assumption was that, in principle, learning and other aspects of human intelligence could be described precisely enough that a machine could be programmed to simulate it.
Principal figures at the Dartmouth conference included such notables as Marvin Minsky, then of Harvard University; Claude Shannon of Bell Laboratories; Nathaniel Rochester of IBM; and Dartmouth's own John McCarthy.
It was McCarthy who put the name "artificial intelligence" to the field of study, just ahead of the conference. With Dartmouth hosting a 50th anniversary conference this month, McCarthy--now a professor emeritus at Stanford University--spoke with CNET News.com about the early expectations for AI, the accomplishments since then and what remains to be done.
You're credited with coining the term "artificial intelligence" just in time for the 1956 conference. Were you just putting a name to existing ideas, or was it something new that was in the air at that time?McCarthy: Well, I came up with the name when I had to write the proposal to get research support for the conference from the Rockefeller Foundation. And to tell you the truth, the reason for the name is, I was thinking about the participants rather than the funder.
Claude Shannon and I had done this book called "Automata Studies," and I had felt that not enough of the papers that were submitted to it were about artificial intelligence, so I thought I would try to think of some name that would nail the flag to the mast.
And looking back, do you think that that's the right term? It seems fairly self-evident, but would there be a better way to describe this kind of research?
McCarthy: Well, there are some people who want to change the name to "computational intelligence"...It seems to me I couldn't have used (that term in 1955) because the idea that computers would be the main vehicle for doing AI was far from unanimous. In fact, it would have been a minority view at that time.
At the time, in that proposal, you had said (about using computers to simulate the higher functions of the brain) that "the major obstacle is not the lack of machine capacity but our inability to write programs taking full advantage of what we have." So the machinery was there, but the programming skills weren't?
McCarthy: It wasn't a question of skills, it was a question of basic ideas, and it still is. One of them that comes up very clearly is when you compare how well computers play chess with how badly they play go, in spite of comparable effort having been put in. The reason is that in go, you have to consider the situation, the position...and furthermore, you have to identify the parts--and that's something that isn't really well understood how to do even yet.
So the attendees in 1956--and I'm sure you, too--were very optimistic about what could be done by, say, the 1970s with chess playing, with composing classical music, understanding speech. How far did we get in the 50 years? Were the initial expectations too optimistic?
McCarthy: Mine were, certainly. I think there were some others there who were rather pessimistic.
What was there to be pessimistic about?
McCarthy: Well, the thing is, you can only take into account the obstacles that you know about, and we know about more than we knew then.
What are some of the big things that have been learned over the last 50 years that have helped shape research in artificial intelligence?
McCarthy: Well, I suppose one of the big things was the recognition that computers would have to do nonmonotonic reasoning.
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think better than we do. That wouldn't seem hard.
Next, they could program unmanned aerial vehicles to see and
avoid other air traffic, just like a live pilot does. So far, they are
dumb and blind, and so a danger to all other air traffic.
think better than we do. That wouldn't seem hard.
Next, they could program unmanned aerial vehicles to see and
avoid other air traffic, just like a live pilot does. So far, they are
dumb and blind, and so a danger to all other air traffic.
think better than we do. That wouldn't seem hard.
Next, they could program unmanned aerial vehicles to see and
avoid other air traffic, just like a live pilot does. So far, they are
dumb and blind, and so a danger to all other air traffic.
The only way that we are ever going to create an intelligent machine that approaches the level of thought found in the human mind is through pulse coded neural networks used to implement positive feedback cortical planes. Current Von-Neuman machines don't have the power to emulate a neural network containing billions of neurons with trillions of interconnectsion - maybe they never will.
The problem is that current AI research still revolves around how to get Von Neuman machines to act intelligently. This is moronic! Its like looking for your eyeglasses out by the streetlight when you lost them in a dark alley because the light is better there. It just ain't going to happen! Of course, I am not saying that such research is useless - that would also be moronic. It just won't lead to a concious machine.
If you want a concious machine, there is no alternative, but to create specialized hardware to emulate the neural network. Three dimensional circuitry implemented with proximity field effect gates might work. However, this is a chicken and egg problem because there will be little motivation to develop the proper circuitry since you get so much bang for the buck with von neuman machines.
Of course if you had enough Von Neuman machines that could handle trillions of interconnections maybe it would be possible to make a concious machine. However, this would be prohibitively expensive --- wait, what about the internet....
The problem is that even if we had the specialized hardware you propose, we wouldn't know what to do with it. We understand too little about the problem. We don't even uderstand what questions to ask. Technology normally evolves in small incremental steps, but we still don't know what the incremental steps to get from "Elisa" to "HAL". It is today just a big step from Simulated Intelligence to Artificial Intelligence.
Since Von Newman machines have proven to be so good at emulating anything they allow us to emulate massively parallel systems in a cheap lab. There's nothing, NOTHING, a neural net or parallel machine can do that a Von Newman cannot do slowly. Maybe they are orders of magnitude too slow to do any practical AI, buit that doesn't matter. For research purposes slow AI is the same as AI, so if we can use the VN machines to emulate a machine that can, in a few months of computation, deduce something simple that requires actual intelligence, it would be a great step.
If we'd rather concentrated on creating specialized machines we would instead be spending millions and probably some of the best brains on earth in building thousands of generations of machines that don't work and that are probably trying to solve problems we don't understand, machines that could have been emulated, even if slowly, by other machines we already have.
Once we get Von Newman machines to do some intelligent things we would have achieved something: understanding some of the fundamental questions and mechanisms. And then, only then, investing in designing a platform to do that at a bigger and faster scale, is worth the effort.
Why waste billions on designing hundreds of generations of machines to do tasks we don't even understand?
It is better to rely on emulation until we understand the questions, until we can say "this is what I want the machine to do", and then we can move our efforts into building efficient and scalable machines.
Speed is not of the essence. Slow AI is still AI.
Today we don't know how to go from Elisa to Hal. We don't have a roadmap or a path, we have no incremental steps to go by. Building extremely capable hardware such as your proposed neural nets is not going to help us get there.
Once we've built emulations that can show some signs of being on that path it is worth to concentrate on building specialized hardware to do practical implementations.
The only way that we are ever going to create an intelligent machine that approaches the level of thought found in the human mind is through pulse coded neural networks used to implement positive feedback cortical planes. Current Von-Neuman machines don't have the power to emulate a neural network containing billions of neurons with trillions of interconnectsion - maybe they never will.
The problem is that current AI research still revolves around how to get Von Neuman machines to act intelligently. This is moronic! Its like looking for your eyeglasses out by the streetlight when you lost them in a dark alley because the light is better there. It just ain't going to happen! Of course, I am not saying that such research is useless - that would also be moronic. It just won't lead to a concious machine.
If you want a concious machine, there is no alternative, but to create specialized hardware to emulate the neural network. Three dimensional circuitry implemented with proximity field effect gates might work. However, this is a chicken and egg problem because there will be little motivation to develop the proper circuitry since you get so much bang for the buck with von neuman machines.
Of course if you had enough Von Neuman machines that could handle trillions of interconnections maybe it would be possible to make a concious machine. However, this would be prohibitively expensive --- wait, what about the internet....
The problem is that even if we had the specialized hardware you propose, we wouldn't know what to do with it. We understand too little about the problem. We don't even uderstand what questions to ask. Technology normally evolves in small incremental steps, but we still don't know what the incremental steps to get from "Elisa" to "HAL". It is today just a big step from Simulated Intelligence to Artificial Intelligence.
Since Von Newman machines have proven to be so good at emulating anything they allow us to emulate massively parallel systems in a cheap lab. There's nothing, NOTHING, a neural net or parallel machine can do that a Von Newman cannot do slowly. Maybe they are orders of magnitude too slow to do any practical AI, buit that doesn't matter. For research purposes slow AI is the same as AI, so if we can use the VN machines to emulate a machine that can, in a few months of computation, deduce something simple that requires actual intelligence, it would be a great step.
If we'd rather concentrated on creating specialized machines we would instead be spending millions and probably some of the best brains on earth in building thousands of generations of machines that don't work and that are probably trying to solve problems we don't understand, machines that could have been emulated, even if slowly, by other machines we already have.
Once we get Von Newman machines to do some intelligent things we would have achieved something: understanding some of the fundamental questions and mechanisms. And then, only then, investing in designing a platform to do that at a bigger and faster scale, is worth the effort.
Why waste billions on designing hundreds of generations of machines to do tasks we don't even understand?
It is better to rely on emulation until we understand the questions, until we can say "this is what I want the machine to do", and then we can move our efforts into building efficient and scalable machines.
Speed is not of the essence. Slow AI is still AI.
Today we don't know how to go from Elisa to Hal. We don't have a roadmap or a path, we have no incremental steps to go by. Building extremely capable hardware such as your proposed neural nets is not going to help us get there.
Once we've built emulations that can show some signs of being on that path it is worth to concentrate on building specialized hardware to do practical implementations.
The only way that we are ever going to create an intelligent machine that approaches the level of thought found in the human mind is through pulse coded neural networks used to implement positive feedback cortical planes. Current Von-Neuman machines don't have the power to emulate a neural network containing billions of neurons with trillions of interconnectsion - maybe they never will.
The problem is that current AI research still revolves around how to get Von Neuman machines to act intelligently. This is moronic! Its like looking for your eyeglasses out by the streetlight when you lost them in a dark alley because the light is better there. It just ain't going to happen! Of course, I am not saying that such research is useless - that would also be moronic. It just won't lead to a concious machine.
If you want a concious machine, there is no alternative, but to create specialized hardware to emulate the neural network. Three dimensional circuitry implemented with proximity field effect gates might work. However, this is a chicken and egg problem because there will be little motivation to develop the proper circuitry since you get so much bang for the buck with von neuman machines.
Of course if you had enough Von Neuman machines that could handle trillions of interconnections maybe it would be possible to make a concious machine. However, this would be prohibitively expensive --- wait, what about the internet....
The problem is that even if we had the specialized hardware you propose, we wouldn't know what to do with it. We understand too little about the problem. We don't even uderstand what questions to ask. Technology normally evolves in small incremental steps, but we still don't know what the incremental steps to get from "Elisa" to "HAL". It is today just a big step from Simulated Intelligence to Artificial Intelligence.
Since Von Newman machines have proven to be so good at emulating anything they allow us to emulate massively parallel systems in a cheap lab. There's nothing, NOTHING, a neural net or parallel machine can do that a Von Newman cannot do slowly. Maybe they are orders of magnitude too slow to do any practical AI, buit that doesn't matter. For research purposes slow AI is the same as AI, so if we can use the VN machines to emulate a machine that can, in a few months of computation, deduce something simple that requires actual intelligence, it would be a great step.
If we'd rather concentrated on creating specialized machines we would instead be spending millions and probably some of the best brains on earth in building thousands of generations of machines that don't work and that are probably trying to solve problems we don't understand, machines that could have been emulated, even if slowly, by other machines we already have.
Once we get Von Newman machines to do some intelligent things we would have achieved something: understanding some of the fundamental questions and mechanisms. And then, only then, investing in designing a platform to do that at a bigger and faster scale, is worth the effort.
Why waste billions on designing hundreds of generations of machines to do tasks we don't even understand?
It is better to rely on emulation until we understand the questions, until we can say "this is what I want the machine to do", and then we can move our efforts into building efficient and scalable machines.
Speed is not of the essence. Slow AI is still AI.
Today we don't know how to go from Elisa to Hal. We don't have a roadmap or a path, we have no incremental steps to go by. Building extremely capable hardware such as your proposed neural nets is not going to help us get there.
Once we've built emulations that can show some signs of being on that path it is worth to concentrate on building specialized hardware to do practical implementations.
one) first requires understanding our own evolved intelligence. To
do that we need to start over. See "Wholly holistic evolution, Mr.
Darwin" at http://we.karleklund.net.
one) first requires understanding our own evolved intelligence. To
do that we need to start over. See "Wholly holistic evolution, Mr.
Darwin" at http://we.karleklund.net.
- Designed Intelligence
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by karlek76
July 5, 2006 1:21 PM PDT
- Creating a stable designed intelligence (as opposed to an evolved
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Reply to this comment
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- designed intelligence?
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by newcreation
July 6, 2006 11:50 AM PDT
- no wonder why he said it he rejected "conventional christianity "at an early age" so he would see darwin like that..nice in theory if you buy all the loop wholes in thinking
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(33 Comments)one) first requires understanding our own evolved intelligence. To
do that we need to start over. See "Wholly holistic evolution, Mr.
Darwin" at http://we.karleklund.net.