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You have a basis for that assertion? The human mind is basically lots of computing power, lots of data, and simple rules -- since all growth and learning is loc

by kobeya 10y ago

You have a basis for that assertion? The human mind is basically lots of computing power, lots of data, and simple rules -- since all growth and learning is local, it must be simple in that sense at least. Yes we don't understand the neuroscience fully yet, but we do understand the ways in which the underlying mechanisms must be constrained. And we routinely build software programs with structure and rules that are vastly more complex than the neurons that make up our gray matter. Maybe automatic hyper-parameter optimization of adversarial networks is all that it takes for general intelligence. If you look up the conference proceedings of NIPS or AAAI you'll see major progress being made on basically every type of learning and memory you can find in a psychological textbook. Of course these are controlled experiments being done, in the technical sense, and not agglomerative. But what's stopping us from combining a bunch of these algorithms together, or better yet a base framework that generically supports them all, and making it self-reflective? Just computing power and available data, really. So no, it's not at all clear that this is a problem that is "not conceptually solved." People keep saying that whenever the subject comes up, but the reality on the ground is otherwise.

justinpombrio 10y ago
> So no, it's not at all clear that this is a problem that is "not conceptually solved." People keep saying that whenever the subject comes up These people wouldn't happen to be domain experts...?
- DubiousPusher 10y ago
  The question still seems up for argument but there is a fairly popular view that "General AI" whatever that is, is an aergent property many local system working in concert.
adrusi 10y ago
If you think that we have all the conceptual understanding to assemble a general artificial intelligence then you should be able to give an outline of how it would work — beyond just lots of computing power and data, that doesn't differentiate the problem form the problem of object classification or natural language processing. Maybe what you mean to say is that once we have the computing power and data collection technology required, so that researches can experiment, the unsolved conceptual problems will become easy to solve. Do you have a basis for that assertion? Regardless, if that's what you meant then the fact remains that the conceptual problems are not all solved. I won't say that it's inconceivable that a sufficiently large and powerful neural net, with a simple structure based on simple rules, given a very comprehensive set of training data, could acheive a level of general intelligence. But we don't have evidence of intelligence arising that way in the past. What we have is the human brain, an incredibly complex structure resembling a neural net with simple rules for individual node (maybe?). It's the result of evolutionary processes, which means that we should expect it to have extremely hard-to-comprehend properties (example [0]). We've seen evolutionary processes produce intelligence, but not learning processes. Again, that's not to say that evolutionary processes are necessary, just that we can be sure that they can lead to intelligence, and that we can't be sure for other methods. [0] https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/
- kobeya 10y ago
  > If you think that we have all the conceptual understanding to assemble a general artificial intelligence then you should be able to give an outline of how it would work — beyond just lots of computing power and data, that doesn't differentiate the problem form the problem of object classification or natural language processing. Sure: https://www.amazon.com/Engineering-General-Intelligence-Part-Cognitive/dp/9462390266 https://www.amazon.com/Engineering-General-Intelligence-Part... > Maybe what you mean to say is that once we have the computing power and data collection technology required, so that researches can experiment, the unsolved conceptual problems will become easy to solve. No, I mean that there appears to be a basis of a solution already. Actually, multiple solutions being pursued by different groups. It's like asking a rocket engineer in 1955 how to build a rocketship to the Moon, or a physicist in 1936 how to build an atomic bomb, or the Wright brothers in 1900 how to build an airplane. Sure, in every single one of these cases you wouldn't get an exact, definitive answer. The Wright brothers didn't even understand the aerodynamics of their airplane, for example. But there were known avenues of inquiry for which there was very solid reason to believe that they would not be dead ends. We're at a point now with AI/ML where solutions can be learned by machines for any solvable problem. It just needs some humans doing the selection of algorithms and guiding in hyper parameter space. But there is active research on automating that meta level which is yielding results. And both the reinforcement learning and the older AGI communities have working, tested designs for cognitive architectures that are truly general. I'm not claiming we're done. I'm just saying that we're basically at the level of a working Wright flyer -- a bunch of research projects individually exhibit intelligence in separate domains, and a couple of cognitive architectures for generalizing them which have been shown to work on toy problems. There's no known unknowns that would cause these approaches to fail, so the reasonable expectation is that in the coming decades we will see the rise of useful AGI. Just like a reasonable observer with all the facts in 1905 should have predicted consumer passenger air travel. > But we don't have evidence of intelligence arising that way in the past. What we have is the human brain, an incredibly complex structure... Yes the human brain is difficult to understand. So is the flight of a bird. It's a good thing that we don't need to replicate the mechanics of bird flight to build flying machines -- otherwise you and I would still be stuck to trains and boats for getting around. I suggest looking not at a neuroscience text book but a psychology textbook. Ask yourself not whether you can replicate exactly the conditions going on in the brain, but rather ask if you can implement a program to the same general functional description as the psychology textbook provides. That's a much easier task, and one well within the capabilities we have today.

by kobeya 10y ago

justinpombrio 10y ago

> So no, it's not at all clear that this is a problem that is "not conceptually solved." People keep saying that whenever the subject comes up These people wouldn't happen to be domain experts...?

DubiousPusher 10y ago
The question still seems up for argument but there is a fairly popular view that "General AI" whatever that is, is an aergent property many local system working in concert.

adrusi 10y ago

If you think that we have all the conceptual understanding to assemble a general artificial intelligence then you should be able to give an outline of how it would work — beyond just lots of computing power and data, that doesn't differentiate the problem form the problem of object classification or natural language processing. Maybe what you mean to say is that once we have the computing power and data collection technology required, so that researches can experiment, the unsolved conceptual problems will become easy to solve. Do you have a basis for that assertion? Regardless, if that's what you meant then the fact remains that the conceptual problems are not all solved. I won't say that it's inconceivable that a sufficiently large and powerful neural net, with a simple structure based on simple rules, given a very comprehensive set of training data, could acheive a level of general intelligence. But we don't have evidence of intelligence arising that way in the past. What we have is the human brain, an incredibly complex structure resembling a neural net with simple rules for individual node (maybe?). It's the result of evolutionary processes, which means that we should expect it to have extremely hard-to-comprehend properties (example [0]). We've seen evolutionary processes produce intelligence, but not learning processes. Again, that's not to say that evolutionary processes are necessary, just that we can be sure that they can lead to intelligence, and that we can't be sure for other methods. [0] https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/

kobeya 10y ago
> If you think that we have all the conceptual understanding to assemble a general artificial intelligence then you should be able to give an outline of how it would work — beyond just lots of computing power and data, that doesn't differentiate the problem form the problem of object classification or natural language processing. Sure: https://www.amazon.com/Engineering-General-Intelligence-Part-Cognitive/dp/9462390266 https://www.amazon.com/Engineering-General-Intelligence-Part... > Maybe what you mean to say is that once we have the computing power and data collection technology required, so that researches can experiment, the unsolved conceptual problems will become easy to solve. No, I mean that there appears to be a basis of a solution already. Actually, multiple solutions being pursued by different groups. It's like asking a rocket engineer in 1955 how to build a rocketship to the Moon, or a physicist in 1936 how to build an atomic bomb, or the Wright brothers in 1900 how to build an airplane. Sure, in every single one of these cases you wouldn't get an exact, definitive answer. The Wright brothers didn't even understand the aerodynamics of their airplane, for example. But there were known avenues of inquiry for which there was very solid reason to believe that they would not be dead ends. We're at a point now with AI/ML where solutions can be learned by machines for any solvable problem. It just needs some humans doing the selection of algorithms and guiding in hyper parameter space. But there is active research on automating that meta level which is yielding results. And both the reinforcement learning and the older AGI communities have working, tested designs for cognitive architectures that are truly general. I'm not claiming we're done. I'm just saying that we're basically at the level of a working Wright flyer -- a bunch of research projects individually exhibit intelligence in separate domains, and a couple of cognitive architectures for generalizing them which have been shown to work on toy problems. There's no known unknowns that would cause these approaches to fail, so the reasonable expectation is that in the coming decades we will see the rise of useful AGI. Just like a reasonable observer with all the facts in 1905 should have predicted consumer passenger air travel. > But we don't have evidence of intelligence arising that way in the past. What we have is the human brain, an incredibly complex structure... Yes the human brain is difficult to understand. So is the flight of a bird. It's a good thing that we don't need to replicate the mechanics of bird flight to build flying machines -- otherwise you and I would still be stuck to trains and boats for getting around. I suggest looking not at a neuroscience text book but a psychology textbook. Ask yourself not whether you can replicate exactly the conditions going on in the brain, but rather ask if you can implement a program to the same general functional description as the psychology textbook provides. That's a much easier task, and one well within the capabilities we have today.