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There are two ways to learn: from imitation (language models are stochastic parrots) and from validation (problem solving, playing games). Many people only rem

There are two ways to learn: from imitation (language models are stochastic parrots) and from validation (problem solving, playing games). Many people only remember the first and forget the second. But the second one has the key to increasing intelligence. I don't know what the authors mean by "current algorithmic frame of AI research" because it is very diverse. But evolutionary methods work well with neural networks, they are not just next token predictors. > Evolution through Large Models https://arxiv.org/abs/2206.08896 https://arxiv.org/abs/2206.08896 It is true that simply using a dataset or a bunch of environments is not enough. > General Intelligence Requires Rethinking Exploration https://arxiv.org/abs/2211.07819 https://arxiv.org/abs/2211.07819 But when sufficient data can be generated from acting in the environment, then a model can surpass human level, like AlphaZero.

usrbinbash 4y ago
> But when sufficient data can be generated from acting in the environment, then a model can surpass human level, like AlphaZero. Yes, in a very very very very very limited environment like a board game. Let's up the ante just a tiny bit and say the AI has to be able to learn how to play Go. But it also has to learn how to get and setup the board, and the cups holding the Go-Stones. It has to place them on the table, or find the table where someone else placed them. It has to figure out how to place the stones. And it has to do so regardless of what else is on the table, what table it is, where the table is, what the light conditions are, or if its playing with stones, chips, painted pebbles or peanuts vs. roasted almonds (yes, I played Go that way once :D ) Now, how much more difficult does this make the task to an AI? Bear in mind that all these changes to the task are absolutely trivial to the only (G)eneral(I)ntelligence that we know (humans). We effortlessly combine so much prior knowledge about movement, our limbs, the way physical objects work, how to gather and integrate information into our model of the world, the ability to formulate and adapt goals _and their similarity to prior goals_ and the solutions to those, that all these tasks I just added to the problem are absolutely trivial, and the only hard exercise is where to place stones on the board. That's how far our current systems are from an AGI.
- mannykannot 4y ago
  > Now, how much more difficult does this make the task to an AI? Given that I can do all these things, but I can't beat Lee Sedol, I suspect they don't add an insurmountable level of difficulty to the problem. The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible.
  - usrbinbash 4y ago
    > I suspect they don't add an insurmountable level of difficulty to the problem. Not for you, because to humans, moving, finding things, gathering information, formulating new goals, etc. are common tasks. And we generalized them to a point where we can integrate them with other tasks, even such which are completely abstract to our daily lives like a game where little stones are put on a grid. But to a machine? Is it easier to make a machine play Go or make it find arbitrary objects while walking around? Is gathering information on its own difficult? Is goal formulation difficult? Is moving through arbitrary spaces difficult? And how difficult are all these tasks relative to one another? > The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. As I understood it, impossible in the current research paradigms. And given the difference in complexity between the task of playing a board game, and playing a board game as I described above, this seems very likely.
    - mannykannot 4y ago
      >> The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. > As I understood it, impossible in the current research paradigms. You can't get around the fact that they are making a claim that something is impossible. If they merely said it was difficult, or the 'something' was something of no interest, there would be nothing here worth discussing.
    - naasking 4y ago
      > As I understood it, impossible in the current research paradigms. No, they literally say that biological "affordances" cannot be captured by algorithms that run on Turing machines.
ENGNR 4y ago
I agree with the two ways in general What if, the second way is easy? So long as you can understand and interface with the world effectively, which the first provides. From there what if it was just act and observe and a fitness function
- deleted 4y ago
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usrbinbash 4y ago

> But when sufficient data can be generated from acting in the environment, then a model can surpass human level, like AlphaZero. Yes, in a very very very very very limited environment like a board game. Let's up the ante just a tiny bit and say the AI has to be able to learn how to play Go. But it also has to learn how to get and setup the board, and the cups holding the Go-Stones. It has to place them on the table, or find the table where someone else placed them. It has to figure out how to place the stones. And it has to do so regardless of what else is on the table, what table it is, where the table is, what the light conditions are, or if its playing with stones, chips, painted pebbles or peanuts vs. roasted almonds (yes, I played Go that way once :D ) Now, how much more difficult does this make the task to an AI? Bear in mind that all these changes to the task are absolutely trivial to the only (G)eneral(I)ntelligence that we know (humans). We effortlessly combine so much prior knowledge about movement, our limbs, the way physical objects work, how to gather and integrate information into our model of the world, the ability to formulate and adapt goals _and their similarity to prior goals_ and the solutions to those, that all these tasks I just added to the problem are absolutely trivial, and the only hard exercise is where to place stones on the board. That's how far our current systems are from an AGI.

mannykannot 4y ago
> Now, how much more difficult does this make the task to an AI? Given that I can do all these things, but I can't beat Lee Sedol, I suspect they don't add an insurmountable level of difficulty to the problem. The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible.
- usrbinbash 4y ago
  > I suspect they don't add an insurmountable level of difficulty to the problem. Not for you, because to humans, moving, finding things, gathering information, formulating new goals, etc. are common tasks. And we generalized them to a point where we can integrate them with other tasks, even such which are completely abstract to our daily lives like a game where little stones are put on a grid. But to a machine? Is it easier to make a machine play Go or make it find arbitrary objects while walking around? Is gathering information on its own difficult? Is goal formulation difficult? Is moving through arbitrary spaces difficult? And how difficult are all these tasks relative to one another? > The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. As I understood it, impossible in the current research paradigms. And given the difference in complexity between the task of playing a board game, and playing a board game as I described above, this seems very likely.
  - mannykannot 4y ago
    >> The claim here is not that algorithmic AI is very [very...] difficult; it is that it is impossible. > As I understood it, impossible in the current research paradigms. You can't get around the fact that they are making a claim that something is impossible. If they merely said it was difficult, or the 'something' was something of no interest, there would be nothing here worth discussing.
  - naasking 4y ago
    > As I understood it, impossible in the current research paradigms. No, they literally say that biological "affordances" cannot be captured by algorithms that run on Turing machines.

ENGNR 4y ago

I agree with the two ways in general What if, the second way is easy? So long as you can understand and interface with the world effectively, which the first provides. From there what if it was just act and observe and a fitness function

deleted 4y ago
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