141–150 of 216 posts
If you look at how child learns, it's huge amount of supervised learning. Parents spend lots of time in do and don't and giving specific instructions on everything from how to use toilet to how to construct a correct sentence. Lots of language development, object identification, pattern matching, comprehension, math skills, motor skills, developing logic - these activities has huge amount of supervised training that…
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I took a closer read through the AlphaGo paper today. There are some other features that make it not general. In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like: - How many turns since a move was played - Number of liberties (empty adjacent points) - How many opponent stones would be captured - How many of own stones would be captured - Num…
Note, that these features are for the RollOut fast policy. The reason is that this needs to be fast, so rather than a net they have a linear policy. A linear policy in order to work it requires good feature selection, which is what this is. In some future, when we have better hardware, you can imagine removing the roll out policy and having just one.
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> The hardware requirements were stunning (280 GPUs and 1920 CPUs for the largest variant) and were an integral part to how well AlphaGo performed Is that really true? Demis stated that the distributed version of AlphaGo beats a single machine version only 75% of the time. That's still stronger than virtually all human players, and probably would still beat Lee Sedol at least once.
The best discussion in terms of hardware adjusted algorithmic process I've seen is by Miles Brundage[1]. The additional hardware was the difference between amateur and pro level skills. It's also important to note that the non-distributed AlphaGo still had quite considerable compute power. Now was a sweet spot in time. This algorithm ten years ago would likely have been pathetic and in ten years time will likely supe…
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I took a closer read through the AlphaGo paper today. There are some other features that make it not general. In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like: - How many turns since a move was played - Number of liberties (empty adjacent points) - How many opponent stones would be captured - How many of own stones would be captured - Num…
Wow, this really took me by surprise. I thought the only input was (s_1...s_final, whowon) where s are statates during training and (s_current) during play, and the system would learn the game on its own. That's the way it worked with the Atari games anyway.
No one is claiming that alphaGo is close to AGI. At least not anyone that understands the methods it uses. What alphaGo is, is an example of AI progress. There has been a rapid increase in progress in the field of AI. We are still a ways away from AGI, but it's now in sight. Just outside the edge of our vision. Almost surely within our lifetime, at this rate.
Not to diminish that this isn't an achievement, but let's put it into context. To talk about being closer to AGI, a different kind of progress would need to be shown: AI solving different -new- kinds of problems.
I think we need more advances in neuroscience and, I know this will be controversial, psychology before we really know what the cake even is. Edit: I actually think the major AI breakthrough will come from either of those two fields, not computer science.
That's an equivalent of saying that advancements in airplanes will come from biology rather than engineering fields. Biology at best can give hints to improve aerodynamics, and it still can be solved mathematically better. Same will be with neuroscience. I believe it's more likely that engineering of AI will bring new ideas to neuroscience instead, just like after building helicopters we gained some intuition and und…
Despite the history and significant progress in AI we still don't know that much about what approach will result in the first strong AI, or even if it's possible to make one. In an important sense AI is more like aeronautical engineering in 1902 than aeronautical engineering today, so it's possible that better understanding of biology will result in an important innovation.
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This is besides your main point, but I just want to add that the 'problem' of Go is not nearly solved. Rather, nearly solved is the problem of beating a human Go player. There is quite a difference.
I wouldn't expect global maximums to even be definable for most problems
I don't know if I'd agree that unsupervised learning is the "cake" here, to paraphrase Yann LeCun. How do we know that the human brain is an unsupervised learner? The supervisor in our brains comes in the form of the dopamine feedback loop, and exactly what kinds of things it rewards aren't totally mapped out but pleasure and novelty seem to be high on the list. That counts as a "supervisor" from a machine learning p…
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I wouldn't expect global maximums to even be definable for most problems
I think a "solution" for Go is a program that can play to a win (or a draw, I guess) in any circumstance, right? I mean, like, you can create a tic-tac-toe program that plays perfectly and will never lose, but you cannot do this for go.
Growth mindset.
No one is claiming that alphaGo is close to AGI. At least not anyone that understands the methods it uses. What alphaGo is, is an example of AI progress. There has been a rapid increase in progress in the field of AI. We are still a ways away from AGI, but it's now in sight. Just outside the edge of our vision. Almost surely within our lifetime, at this rate.
It's progress, very neat progress, but it's not even progress in the sense that there are new kind of problems being solved. AI for Go and similar games has existed for a zillion times, but, given the huge space of the game of Go, it had never been that good at playing it as to beat a human. Not to diminish that this isn't an achievement, but let's put it into context. To talk about being closer to AGI, a different k…
Which of course, it does. Do you honestly believe Go and other games are the only AI problems where progress is being made?