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This reminds me of reinforcement learning and the Bellman equations. Let a neural network based agent act in an environment. Record its rewards. Update the neu

This reminds me of reinforcement learning and the Bellman equations. Let a neural network based agent act in an environment. Record its rewards. Update the neural network so that the best actions become a little more likely, and the worse actions become a little less likely. Thus, we have demonstrated a training step that improves the agent. Now just run that training step in a loop forever and you'll improve each step and reach Godlike intelligence, right? Right? Well, no, there is instability in "pulling yourself up by the bootstraps", and while I expect to see more GTP improvements, I believe there may be limitations we don't know about yet.

blagie 3y ago
That sounds like technobabble, which is to say big words like "instability" and "Bellman equations," but with little connecting logic. If there is meaning behind those words, please write something with multiple paragraphs. I don't see any reason why there would be a limit, instability, or otherwise, any more or less than there is on biological evolution. I also don't see any reason why there wouldn't be a limit. We just have no idea at this point. I do think a diversity of tasks is critical. An AGI should be able to not just complete human language but: - Play a diversity of strategic games like chess or Starcraft - Perform machine vision like Stable Diffusion - Predict the output of Python programs (and vice-versa) - Write (correct) mathematical proofs and arguments - Control a robotic arm or airplane ... and so on. We can train all of those independently -- and we could do so in a single network now -- but we're at the very early stages of architecture for how those things would integrate. At some point, the best solution is a very sophisticated intelligence. How close deep learning in current -- and future -- architectures gets us to super-intelligence is an open question.

blagie 3y ago

That sounds like technobabble, which is to say big words like "instability" and "Bellman equations," but with little connecting logic. If there is meaning behind those words, please write something with multiple paragraphs. I don't see any reason why there would be a limit, instability, or otherwise, any more or less than there is on biological evolution. I also don't see any reason why there wouldn't be a limit. We just have no idea at this point. I do think a diversity of tasks is critical. An AGI should be able to not just complete human language but: - Play a diversity of strategic games like chess or Starcraft - Perform machine vision like Stable Diffusion - Predict the output of Python programs (and vice-versa) - Write (correct) mathematical proofs and arguments - Control a robotic arm or airplane ... and so on. We can train all of those independently -- and we could do so in a single network now -- but we're at the very early stages of architecture for how those things would integrate. At some point, the best solution is a very sophisticated intelligence. How close deep learning in current -- and future -- architectures gets us to super-intelligence is an open question.