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Geoff Hinton on AlphaGo and the Future of AI

77 points by tim_sw 11y ago

jedharris 11y ago
Hinton has a very balanced perspective. His comments on the short and medium term dangers of AI are interesting. He considers the main risks to come from politics, which sounds right to me -- deliberate (mis)use of AI, not drama intrinsic to AI self-improvement. Look at the current FBI vs. Apple mess, and our drone program(s). His comments on his own professional experience, toward the end, are also very interesting -- not the sort of discussion one usually sees from senior scientists.
- pinouchon 11y ago
  Hinton seems to refuse to talk about a timeline longer than 5 years, and 5 years seems to short to have any kind of human-level AI, let alone a self improving superintelligence. The real danger lies when we approach the human-level threshold.
  - chubot 11y ago
    I thought this quote was pretty hilarious (unintentionally): [on rendering humanity obsolete] A: It’s hard to predict beyond five years. I’m pretty confident it won’t happen in the next five years, and I’m fairly confident that it won’t be something I’m going to have to deal with. But it’s something people should definitely be thinking about. OK kids, were making AI -- deal with it!!! FWIW I agree with him on the time frame -- we're in another phase where AI successes have led to overoptimism. The human brain will humble us in the next decade....
  - marvin 11y ago
    Good point. I'm not aware of any high-profile technologist or commentator that claims self-improving, superhuman artificial intelligence is a likely risk this decade. This doesn't imply that self-improving or superhuman AI will never be a risk. I get the sense that some machine learning/AI researchers downplay this long-term concern, while at the same time misattributing discussons around AI safety as criticism of the current approach to AI. It must be possible to have two thoughts in our heads at once. Machine learning is making massive strides at the moment, and we must use this for all the benefit it's worth while keeping an eye on the current risks that AI represents -- for instance job loss and displacement, misuse by governments for e.g. surveillance purposes and so on. But it's probably also a good idea to keep one eye on the horizon as well, because things are likely to happen pretty quickly if the state of the art advances further. It's best to be prepared for this eventuality well in advance, because successful navigation of a potential risk scenario there requires the cooperation of a lot of people. And people are slow to change their expectations. Don't get me wrong, I greatly appreciate the current discussions around this subject. There are a lot of good points being made.
rdlecler1 11y ago
I applied to Geoff's lab fora postdocs in 2009. He said that he didn't have any grant money to take on anyone else. Those were dark days.
return0 11y ago
He remarks about the need to simulate the entire brain. However, at the moment, artificial neurons do really well while being a tiny minuscule proportion of the real neurons in the brain. It's possible neural nets capture the level of description required to reproduce intelligence and we don't need to go any deeper. Learning in real neurons is extremely more messy and probably more distributed, but that does not mean that all this mess is necessary to improve current models.
- visarga 11y ago
  I agree, we might do OK with less than the power of the brain. On the other hand Hinton also says that we need to invest more computation in order to achieve AI with common sense. So, it would seem, even if we don't need as much power as the brain has, our road ahead is still tied to the advancement of computation. It's not a matter of guessing the right learning algorithm.
- fla 11y ago
  On the other hand, the human brain cosumes approximately 20 Watts.
  - return0 11y ago
    you mean it's power efficient? Consider that it takes the brain, let's say, ~5 years to learn the stuff that a visual NN learns in days.
- argonaut 11y ago
  He did not say we need to simulate the entire brain. He said neural nets would need about the same number of parameters. Not the same thing.
dang 11y ago
The story about Hinton's entomologist father is interesting, and reminded me that he is also a direct descendant of Boole.
- graycat 11y ago
  What's he talking except some non-linear data fitting with a lot of data and a lot of variables? In what sense is this anything like intelligence? Moreover, from such fitting, can apply it to data like that used in the fitting but where else? What hope is that that there is any progress at all on AI?
graycat 11y ago
A better way, when can do it: From basic game theory, each board game such a Go, chess, checkers, Nim, has an optimal strategy. Then the only issue left is who moves first. If the advantage is to the player who moves first, then they should always win; similarly for the player who moves second. Details in, say, T. Parthasarathy and T. E. S. Raghavan, Some Topics in Two-Person Games. Well, such a strategy is known for Nim, e.g., Courant and Robbins, What Is Mathematics? So, when the strategy is known, then following the strategy stands to beat Hinton's AI. Or, Hinton's million node AI can learn and learn all it wants, millions of games, maybe against itself, and just a tiny computer program, if it has choice of who moves first, will always win (no ties in Nim). More generally, for now, when can solve a cleanly specified problem mathematically, that is, complete with theorems and proofs, then that is better than Hinton's AI. Maybe in time something like Hinton's AI will be able to do research in math and produce results as theorems and proofs, maybe find the optimal chess strategy, and then, with choice of who moves first, never lose at chess, etc. But here's Lesson 1: For now, when math can solve a problem, it is better than Hinton's AI. So, sure, for startups, here's Lesson 2: F'get about Hinton's AI. Instead, pick a cleanly specified problem where can do some math research and get a solid math solution. Then stand to beat intuition, Hinton's AI, and, in practice, about everything else. Are there some problems now where math can do better than Hinton's AI? Yup. Point 1: Whatever can be done with Hinton's AI broadly in the economy, one of the keys to a successful startup is being exceptionally good. Well, a way to never lose and usually to win, usually win big, is to have the best math solution. So, that's one of the criteria in picking what a startup is to do. Again, it's just crucial to be exceptional. Simple example: Hinton's AI would have one heck of a time competing with the simplex algorithm on realistic problems, say, from running an oil refinery, on linear programming. A worse time competing on non-linear programming, say, also for running an oil refinery. A worse time competing on stochastic optimal control. Really hopeless on competing with Issac's differential game theory in pursuit-evasion games. And there's lots more.
j2kun 11y ago
Am I the only one that cringed when Hinton described neural networks as enabling intuition in machines? I'm really hoping this was just a tortured analogy for a pop-science audience (and I really hate it when world-renowned scientists do that). Why not just admit that we don't have anything close to a deep understanding of why AlphaGo did so well compared to previous approaches?
- deleted 11y ago
  [deleted]
- tim333 11y ago
  I don't think it was a tortured analogy - more a guess that human intuition works much the same was as pattern recognition by a neural network.
huahaiy 11y ago
I am actually disappointed with his view. I thought he had a psychology background and should have known better than his obvious reductionist view. What neural network have demonstrated so far is that it can do a form of perception given enough ground truth data. Speech recognition and image recognition are all form of perception. Go playing is also mostly about perception: recognizing good patterns. There are multiple issues with extrapolating this achievement to anything more than what it is. First, perception is a only minuscule part of the brain's functionality. One only needs to glance at the table of content page of any cognitive psychology 101 textbook to see that. Second, it is not clear human's perceptual ability is the same as what NN has demonstrated. For example, human's perceptual learning is mostly based on unsupervised learning, not supervised like in NN. Finally, his dismissal of "traditional" approach is extremely short-sighted. He seems to suggest that we can solve AI by just throwing more hardware and power at NN. This view is very narrow minded. He also seems to overlook the high possibility that the "traditional" approach could benefit similarly from the advances in hardware and power. I predict the next breakthrough in AI will come from that direction.

77 points by tim_sw 11y ago

Hinton has a very balanced perspective. His comments on the short and medium term dangers of AI are interesting. He considers the main risks to come from politics, which sounds right to me -- deliberate (mis)use of AI, not drama intrinsic to AI self-improvement. Look at the current FBI vs. Apple mess, and our drone program(s). His comments on his own professional experience, toward the end, are also very interesting -- not the sort of discussion one usually sees from senior scientists.

pinouchon 11y ago
Hinton seems to refuse to talk about a timeline longer than 5 years, and 5 years seems to short to have any kind of human-level AI, let alone a self improving superintelligence. The real danger lies when we approach the human-level threshold.
- chubot 11y ago
  I thought this quote was pretty hilarious (unintentionally): [on rendering humanity obsolete] A: It’s hard to predict beyond five years. I’m pretty confident it won’t happen in the next five years, and I’m fairly confident that it won’t be something I’m going to have to deal with. But it’s something people should definitely be thinking about. OK kids, were making AI -- deal with it!!! FWIW I agree with him on the time frame -- we're in another phase where AI successes have led to overoptimism. The human brain will humble us in the next decade....
- marvin 11y ago
  Good point. I'm not aware of any high-profile technologist or commentator that claims self-improving, superhuman artificial intelligence is a likely risk this decade. This doesn't imply that self-improving or superhuman AI will never be a risk. I get the sense that some machine learning/AI researchers downplay this long-term concern, while at the same time misattributing discussons around AI safety as criticism of the current approach to AI. It must be possible to have two thoughts in our heads at once. Machine learning is making massive strides at the moment, and we must use this for all the benefit it's worth while keeping an eye on the current risks that AI represents -- for instance job loss and displacement, misuse by governments for e.g. surveillance purposes and so on. But it's probably also a good idea to keep one eye on the horizon as well, because things are likely to happen pretty quickly if the state of the art advances further. It's best to be prepared for this eventuality well in advance, because successful navigation of a potential risk scenario there requires the cooperation of a lot of people. And people are slow to change their expectations. Don't get me wrong, I greatly appreciate the current discussions around this subject. There are a lot of good points being made.

rdlecler1 11y ago

I applied to Geoff's lab fora postdocs in 2009. He said that he didn't have any grant money to take on anyone else. Those were dark days.

return0 11y ago

He remarks about the need to simulate the entire brain. However, at the moment, artificial neurons do really well while being a tiny minuscule proportion of the real neurons in the brain. It's possible neural nets capture the level of description required to reproduce intelligence and we don't need to go any deeper. Learning in real neurons is extremely more messy and probably more distributed, but that does not mean that all this mess is necessary to improve current models.

visarga 11y ago
I agree, we might do OK with less than the power of the brain. On the other hand Hinton also says that we need to invest more computation in order to achieve AI with common sense. So, it would seem, even if we don't need as much power as the brain has, our road ahead is still tied to the advancement of computation. It's not a matter of guessing the right learning algorithm.
fla 11y ago
On the other hand, the human brain cosumes approximately 20 Watts.
- return0 11y ago
  you mean it's power efficient? Consider that it takes the brain, let's say, ~5 years to learn the stuff that a visual NN learns in days.
argonaut 11y ago
He did not say we need to simulate the entire brain. He said neural nets would need about the same number of parameters. Not the same thing.

dang 11y ago

The story about Hinton's entomologist father is interesting, and reminded me that he is also a direct descendant of Boole.

graycat 11y ago
What's he talking except some non-linear data fitting with a lot of data and a lot of variables? In what sense is this anything like intelligence? Moreover, from such fitting, can apply it to data like that used in the fitting but where else? What hope is that that there is any progress at all on AI?

graycat 11y ago

A better way, when can do it: From basic game theory, each board game such a Go, chess, checkers, Nim, has an optimal strategy. Then the only issue left is who moves first. If the advantage is to the player who moves first, then they should always win; similarly for the player who moves second. Details in, say, T. Parthasarathy and T. E. S. Raghavan, Some Topics in Two-Person Games. Well, such a strategy is known for Nim, e.g., Courant and Robbins, What Is Mathematics? So, when the strategy is known, then following the strategy stands to beat Hinton's AI. Or, Hinton's million node AI can learn and learn all it wants, millions of games, maybe against itself, and just a tiny computer program, if it has choice of who moves first, will always win (no ties in Nim). More generally, for now, when can solve a cleanly specified problem mathematically, that is, complete with theorems and proofs, then that is better than Hinton's AI. Maybe in time something like Hinton's AI will be able to do research in math and produce results as theorems and proofs, maybe find the optimal chess strategy, and then, with choice of who moves first, never lose at chess, etc. But here's Lesson 1: For now, when math can solve a problem, it is better than Hinton's AI. So, sure, for startups, here's Lesson 2: F'get about Hinton's AI. Instead, pick a cleanly specified problem where can do some math research and get a solid math solution. Then stand to beat intuition, Hinton's AI, and, in practice, about everything else. Are there some problems now where math can do better than Hinton's AI? Yup. Point 1: Whatever can be done with Hinton's AI broadly in the economy, one of the keys to a successful startup is being exceptionally good. Well, a way to never lose and usually to win, usually win big, is to have the best math solution. So, that's one of the criteria in picking what a startup is to do. Again, it's just crucial to be exceptional. Simple example: Hinton's AI would have one heck of a time competing with the simplex algorithm on realistic problems, say, from running an oil refinery, on linear programming. A worse time competing on non-linear programming, say, also for running an oil refinery. A worse time competing on stochastic optimal control. Really hopeless on competing with Issac's differential game theory in pursuit-evasion games. And there's lots more.

j2kun 11y ago

Am I the only one that cringed when Hinton described neural networks as enabling intuition in machines? I'm really hoping this was just a tortured analogy for a pop-science audience (and I really hate it when world-renowned scientists do that). Why not just admit that we don't have anything close to a deep understanding of why AlphaGo did so well compared to previous approaches?

deleted 11y ago
[deleted]
tim333 11y ago
I don't think it was a tortured analogy - more a guess that human intuition works much the same was as pattern recognition by a neural network.

huahaiy 11y ago

I am actually disappointed with his view. I thought he had a psychology background and should have known better than his obvious reductionist view. What neural network have demonstrated so far is that it can do a form of perception given enough ground truth data. Speech recognition and image recognition are all form of perception. Go playing is also mostly about perception: recognizing good patterns. There are multiple issues with extrapolating this achievement to anything more than what it is. First, perception is a only minuscule part of the brain's functionality. One only needs to glance at the table of content page of any cognitive psychology 101 textbook to see that. Second, it is not clear human's perceptual ability is the same as what NN has demonstrated. For example, human's perceptual learning is mostly based on unsupervised learning, not supervised like in NN. Finally, his dismissal of "traditional" approach is extremely short-sighted. He seems to suggest that we can solve AI by just throwing more hardware and power at NN. This view is very narrow minded. He also seems to overlook the high possibility that the "traditional" approach could benefit similarly from the advances in hardware and power. I predict the next breakthrough in AI will come from that direction.