Disagreements here are largely going to revolve around how this observation and similar ones are interpreted. This kind of evidence must push us in some direction. We all agree that what we saw was surprising—a difficult task was solved by a system with no prior knowledge or specific information to this task baked in. Surprise implies a model update. The question seems to be which model.
The debate referenced above is about the likelihood of AGI “FOOM”. The Hansonian position seems to be that a FOOM is unlikely because obtaining generality across many different domains at once is unlikely. Is AlphaGo evidence for or against this position?
There is definitely room for more than one interpretation. On the one hand, AG0 did not require any human games to learn from. It was trained via a variety of methods that were not specific to Go itself. It used neural net components that were proven to work well on very different domains such as Atari. This is evidence that the components and techniques used to create a narrow AI system can also be used on a wide variety of domains.
On the other hand, it’s not clear whether the “AI system” itself should be considered as only the trained neural network, or the entire apparatus including using MCTS to simulate self play in order to generate supervised training data. The network by itself plays one game, the apparatus learns to play games. You could choose to see this observation instead as “humans tinkered for years to create a narrow system that only plays Go.” AG0, once trained, cannot go train on an entirely different game and then know how to play both at a superhuman level (as far as I know, anyway. There are some results that suggest it’s possible for some models to learn different tasks in sequence without forgetting). So one hypothesis to update in favor of is “there is a tool that allows a system to learn to do one task, this tool can be applied to many different tasks, but only one task at a time.”
But would future, more general, AI systems do something similar to human researchers, in order to train narrow AI subcomponents used for more specific tasks? Could another AI do the “tinkering” that humans do, trained via similar methods? Perhaps not with AG0′s training method specifically. But maybe there are other similar, general training algorithms that could do it, and we want to know if this one method that proves to be more general than expected suggests the existence of even more general methods.
It’s hard to see how this observation can be evidence against this, but there are also no good ways to determine how strongly it is for it, either. So I don’t see how this can favor Hanson’s position at all, but how much it favors EY’s is open to debate.
The techniques you outline for incorporating narrow agents into more general systems have already been demoed, I’m pretty sure. A coordinator can apply multiple narrow algorithms to a task and select the most effective one, a la IBM Watson. And I’ve seen at least one paper that uses a RNN to cultivate a custom RNN with the appropriate parameters for a new situation.
Disagreements here are largely going to revolve around how this observation and similar ones are interpreted. This kind of evidence must push us in some direction. We all agree that what we saw was surprising—a difficult task was solved by a system with no prior knowledge or specific information to this task baked in. Surprise implies a model update. The question seems to be which model.
The debate referenced above is about the likelihood of AGI “FOOM”. The Hansonian position seems to be that a FOOM is unlikely because obtaining generality across many different domains at once is unlikely. Is AlphaGo evidence for or against this position?
There is definitely room for more than one interpretation. On the one hand, AG0 did not require any human games to learn from. It was trained via a variety of methods that were not specific to Go itself. It used neural net components that were proven to work well on very different domains such as Atari. This is evidence that the components and techniques used to create a narrow AI system can also be used on a wide variety of domains.
On the other hand, it’s not clear whether the “AI system” itself should be considered as only the trained neural network, or the entire apparatus including using MCTS to simulate self play in order to generate supervised training data. The network by itself plays one game, the apparatus learns to play games. You could choose to see this observation instead as “humans tinkered for years to create a narrow system that only plays Go.” AG0, once trained, cannot go train on an entirely different game and then know how to play both at a superhuman level (as far as I know, anyway. There are some results that suggest it’s possible for some models to learn different tasks in sequence without forgetting). So one hypothesis to update in favor of is “there is a tool that allows a system to learn to do one task, this tool can be applied to many different tasks, but only one task at a time.”
But would future, more general, AI systems do something similar to human researchers, in order to train narrow AI subcomponents used for more specific tasks? Could another AI do the “tinkering” that humans do, trained via similar methods? Perhaps not with AG0′s training method specifically. But maybe there are other similar, general training algorithms that could do it, and we want to know if this one method that proves to be more general than expected suggests the existence of even more general methods.
It’s hard to see how this observation can be evidence against this, but there are also no good ways to determine how strongly it is for it, either. So I don’t see how this can favor Hanson’s position at all, but how much it favors EY’s is open to debate.
The techniques you outline for incorporating narrow agents into more general systems have already been demoed, I’m pretty sure. A coordinator can apply multiple narrow algorithms to a task and select the most effective one, a la IBM Watson. And I’ve seen at least one paper that uses a RNN to cultivate a custom RNN with the appropriate parameters for a new situation.