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> But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was solved, it is no

> But as soon as we are able to solve a problem that we think (feel?) only true AI (AGI) would be able to solve, as soon as we know how it was solved, it is no longer a mystery that warrants amazement and we argue that it is not real intelligence, just like the link below states. I think the issue is that current examples of AI can mostly only solve problems in a single domain instead of in a variety of domains. Developing a program that can generate solutions for a single specific niche of problems (while impressive) is not a convincing demonstration of intelligence. For example, while AlphaGo is incredibly impressive, it can only play Go and nothing else. I think once we have a comprehensive AI program that can play many kinds of games, can have conversations, can complete school exam questions, can write stories etc. the question of what intelligence is will become more interesting.

visarga 10y ago
> once we have a comprehensive AI program that can play many kinds of games Before AlphaGo, DeepMind released a reinforcement learning algorithm that could play many Atari games just from the raw pixels on the screen, in many games surpassing humans. The same algorithm. https://arxiv.org/pdf/1312.5602.pdf https://arxiv.org/pdf/1312.5602.pdf Reinforcement learning is a general framework for learning behavior from acting in an environment with the purpose to maximize a reward. It can be used, and was used, in multiple domains. AlphaGo used RL as well. Saying that AlphaGo is limited because it only knows to play one game, is like saying that humans are limited because Lee Sedol could only master at world level one game. In fact, if the software was set to learn more games, it could learn them in addition to Go. Also, regarding other tasks: a neural net that recognizes cats can be easily made to recognize dogs too. A program that translates English to French can be made to translate other languages too. We limit software to specific domains only on account of efficiency, not because algorithms are fundamentally limited. Recently there has been a paper "Learning without forgetting" (http://arxiv.org/abs/1606.09282v2 http://arxiv.org/abs/1606.09282v2) that underlines this very ability to span multiple domains and adapt easily to unseen tasks and data. > Developing a program that can generate solutions for a single specific niche of problems (while impressive) is not a convincing demonstration of intelligence. Saying that people can do many tasks is not exactly right because a particular person can only do a few tasks, those tasks she was trained to do. If I never learned German, I don't know German. That doesn't mean the brain itself is limited. AlphaGo was only trained on Go, and its internal architecture was optimized for this one task in order to make it more efficient, but the method is general and reusable. DeepMind said so themselves, the breakthrough is not that they beat Lee Sedol, but that they used a general method that can be used to do other tasks as well. It is not a limitation of AI that we generally make systems that are good at only one thing. If there is a limitation in AlphaGo, it is that it mastered a game where the whole situation is perfectly known (the Go board), while in reality many tasks are only partially known (such as card games, for example) so there is an extra uncertainty. But DeepMind and other researchers are working on that too.
- deleted 10y ago
  [deleted]
- cscurmudgeon 10y ago
  > but that they used a general method that can be used to do other tasks as well. It is not a limitation of AI that we generally make systems that are good at only one thing This is exactly the kind of hubris and reasoning that brought down good old fashioned AI initially. They also had a lot of initial impressive wins with methods that looked quite general at that time.
  - visarga 10y ago
    The difference being that this time there are real applications to deep learning.
    - cscurmudgeon 10y ago
      There are real applications to old AI even today. Look up ITA software, uses of theorem proving of hardware and software verification, etc. Let us not engage in FUD please.
- Cybiote 10y ago
  > Atari games just from the raw pixels on the screen It's important to distinguish between what sorts of games work well under this method and what sorts do not. Games that are variations of pole balancing, like Pong, fare better than more complex games like Asteroids, Frostbite or Montezuma's Revenge. > Saying that AlphaGo is limited because it only knows to play one game, is like saying that humans are limited because Lee Sedol could only master at world level one game. It's nothing of the sort. AlphaGo is a machine in the Turing sense. The neural network is a program that is the result of a search for a function specialized to playing Go. This machine, the program that the parameters across the edges in the graph represent, is logically unable to run any other program. Lee Sedol is a Universal Machine in the Turing sense, any statement contradicting this makes no mathematical sense. > We limit software to specific domains only on account of efficiency, not because algorithms are fundamentally limited. It is well known within the literature that these models do not make best available use of information when learning. They are exceedingly inefficient in their incorporation of new information. Issues include improper adjustment of learning rates, not using side information to constrain computation, having to experience many rewards before action distributions are adjusted in the case of reinforcement learning, samples per example in supervised learning. Note that animals are able to learn without explicit labels and clear 0/1 losses. Humans and animals generally, even in the supervised regime, are vastly more flexible in the format the supervision can take. For an example, look into the research on how children are able to generalize from ambiguous explanations as "that is a dog" and why difficulty in learning color from this kind of "supervision" shows just what priors are being leveraged to get that kind of learning power. See here for an excellent overview of limitations in our current approaches to AI: https://arxiv.org/pdf/1604.00289v2.pdf https://arxiv.org/pdf/1604.00289v2.pdf > "Learning without forgetting" That's a great paper but it does this by minimizing prediction error drift by comparing before and post performance on the old task while learning the new. I do not know that this method will scale with increasing task numbers, considering Neural Networks are already difficult and energy-time consuming enough to train as is.
cscurmudgeon 10y ago
> I think once we have a comprehensive AI program that can play many kinds of games, can have conversations, can complete school exam questions, can write stories etc. the question of what intelligence is will become more interesting. Or even just talk about Go, answer complex questions about it in natural language, and state and prove interesting mathematical results about the complexity of Go.

visarga 10y ago

> once we have a comprehensive AI program that can play many kinds of games Before AlphaGo, DeepMind released a reinforcement learning algorithm that could play many Atari games just from the raw pixels on the screen, in many games surpassing humans. The same algorithm. https://arxiv.org/pdf/1312.5602.pdf https://arxiv.org/pdf/1312.5602.pdf Reinforcement learning is a general framework for learning behavior from acting in an environment with the purpose to maximize a reward. It can be used, and was used, in multiple domains. AlphaGo used RL as well. Saying that AlphaGo is limited because it only knows to play one game, is like saying that humans are limited because Lee Sedol could only master at world level one game. In fact, if the software was set to learn more games, it could learn them in addition to Go. Also, regarding other tasks: a neural net that recognizes cats can be easily made to recognize dogs too. A program that translates English to French can be made to translate other languages too. We limit software to specific domains only on account of efficiency, not because algorithms are fundamentally limited. Recently there has been a paper "Learning without forgetting" (http://arxiv.org/abs/1606.09282v2 http://arxiv.org/abs/1606.09282v2) that underlines this very ability to span multiple domains and adapt easily to unseen tasks and data. > Developing a program that can generate solutions for a single specific niche of problems (while impressive) is not a convincing demonstration of intelligence. Saying that people can do many tasks is not exactly right because a particular person can only do a few tasks, those tasks she was trained to do. If I never learned German, I don't know German. That doesn't mean the brain itself is limited. AlphaGo was only trained on Go, and its internal architecture was optimized for this one task in order to make it more efficient, but the method is general and reusable. DeepMind said so themselves, the breakthrough is not that they beat Lee Sedol, but that they used a general method that can be used to do other tasks as well. It is not a limitation of AI that we generally make systems that are good at only one thing. If there is a limitation in AlphaGo, it is that it mastered a game where the whole situation is perfectly known (the Go board), while in reality many tasks are only partially known (such as card games, for example) so there is an extra uncertainty. But DeepMind and other researchers are working on that too.

deleted 10y ago
[deleted]
cscurmudgeon 10y ago
> but that they used a general method that can be used to do other tasks as well. It is not a limitation of AI that we generally make systems that are good at only one thing This is exactly the kind of hubris and reasoning that brought down good old fashioned AI initially. They also had a lot of initial impressive wins with methods that looked quite general at that time.
- visarga 10y ago
  The difference being that this time there are real applications to deep learning.
  - cscurmudgeon 10y ago
    There are real applications to old AI even today. Look up ITA software, uses of theorem proving of hardware and software verification, etc. Let us not engage in FUD please.
Cybiote 10y ago
> Atari games just from the raw pixels on the screen It's important to distinguish between what sorts of games work well under this method and what sorts do not. Games that are variations of pole balancing, like Pong, fare better than more complex games like Asteroids, Frostbite or Montezuma's Revenge. > Saying that AlphaGo is limited because it only knows to play one game, is like saying that humans are limited because Lee Sedol could only master at world level one game. It's nothing of the sort. AlphaGo is a machine in the Turing sense. The neural network is a program that is the result of a search for a function specialized to playing Go. This machine, the program that the parameters across the edges in the graph represent, is logically unable to run any other program. Lee Sedol is a Universal Machine in the Turing sense, any statement contradicting this makes no mathematical sense. > We limit software to specific domains only on account of efficiency, not because algorithms are fundamentally limited. It is well known within the literature that these models do not make best available use of information when learning. They are exceedingly inefficient in their incorporation of new information. Issues include improper adjustment of learning rates, not using side information to constrain computation, having to experience many rewards before action distributions are adjusted in the case of reinforcement learning, samples per example in supervised learning. Note that animals are able to learn without explicit labels and clear 0/1 losses. Humans and animals generally, even in the supervised regime, are vastly more flexible in the format the supervision can take. For an example, look into the research on how children are able to generalize from ambiguous explanations as "that is a dog" and why difficulty in learning color from this kind of "supervision" shows just what priors are being leveraged to get that kind of learning power. See here for an excellent overview of limitations in our current approaches to AI: https://arxiv.org/pdf/1604.00289v2.pdf https://arxiv.org/pdf/1604.00289v2.pdf > "Learning without forgetting" That's a great paper but it does this by minimizing prediction error drift by comparing before and post performance on the old task while learning the new. I do not know that this method will scale with increasing task numbers, considering Neural Networks are already difficult and energy-time consuming enough to train as is.

cscurmudgeon 10y ago

> I think once we have a comprehensive AI program that can play many kinds of games, can have conversations, can complete school exam questions, can write stories etc. the question of what intelligence is will become more interesting. Or even just talk about Go, answer complex questions about it in natural language, and state and prove interesting mathematical results about the complexity of Go.