Thanks again! Feel free to stop responding if you’re busy.
Here’s where I’m at so far. Let’s forget about human brains and just talk about how we should design an AGI.
One thing we can do is design an AGI whose source code straightforwardly resembles model-based reinforcement learning. So the code has data structures for a critic / value-function, and a reward function, and TD learning, and a world-model, and so on.
This has the advantage that (I claim) it can actually work. (Not today, but after further algorithmic progress.) After all, I am confident that human brains have all those things (critic ≈ striatum, TD learning ≈ dopamine, etc.) and the human brain can do lots of impressive things, like go to the moon invent and quantum mechanics and so on.
But it also has a disadvantage that it’s unclear how to make the AGI motivated to act in a human-like way and follow human norms. It’s not impossible, evidently—after all, I am a human and I am at least somewhat motivated to follow human norms, and when someone I greatly admire starts doing X or wanting X, then I also am more inclined to start doing X and wanting X. But it’s unclear how this works in terms of my brain’s reward functions / loss functions / neural architecture / whatever—or at least, it’s presently unclear to me. (It is one of my major areas of research interest, and I think I’m making gradual progress, but as of now I don’t have any good & complete answer.)
A different thing we can do is design an AGI whose source code straightforwardly resembles active inference / FEP. So the code has, umm, I’m not sure, something about generative models and probability distributions? But it definitely does NOT have a reward function or critic / value-function etc.
This has the advantage that (according to you, IIUC) there’s a straightforward way to make the AGI act in a human-like way and follow human norms.
And it has the disadvantage that I’m somewhat skeptical that it will ever be possible to actually code up an AGI that way.
So I’m pretty confused here.
For one thing, I’m not yet convinced that the first bullet point is actually straightforward (or even possible). Maybe I didn’t follow your previous response. Some of my concerns are: (1) most human actions are not visible (e.g. deciding what to think about, recalling a memory), (2) even the ones that are visible in principle are very hard to extract in practice (e.g. did I move deliberately or did was that a random jostle or gust of wind?) (3) almost all “outcomes” of interest in the AGI context are outcomes that have never happened in the training data, e.g. the AGI can invent a new gadget which no human had ever previously invented. So I’m not sure how you get p(o,a) from observations of humans.
For the second thing, among other issues, it seems to me that building a beyond-human-level understanding of the world requires RL-type trial-and-error exploration, for reasons in Section 1.1 here.
Thanks again! Feel free to stop responding if you’re busy.
Here’s where I’m at so far. Let’s forget about human brains and just talk about how we should design an AGI.
One thing we can do is design an AGI whose source code straightforwardly resembles model-based reinforcement learning. So the code has data structures for a critic / value-function, and a reward function, and TD learning, and a world-model, and so on.
This has the advantage that (I claim) it can actually work. (Not today, but after further algorithmic progress.) After all, I am confident that human brains have all those things (critic ≈ striatum, TD learning ≈ dopamine, etc.) and the human brain can do lots of impressive things, like go to the moon invent and quantum mechanics and so on.
But it also has a disadvantage that it’s unclear how to make the AGI motivated to act in a human-like way and follow human norms. It’s not impossible, evidently—after all, I am a human and I am at least somewhat motivated to follow human norms, and when someone I greatly admire starts doing X or wanting X, then I also am more inclined to start doing X and wanting X. But it’s unclear how this works in terms of my brain’s reward functions / loss functions / neural architecture / whatever—or at least, it’s presently unclear to me. (It is one of my major areas of research interest, and I think I’m making gradual progress, but as of now I don’t have any good & complete answer.)
A different thing we can do is design an AGI whose source code straightforwardly resembles active inference / FEP. So the code has, umm, I’m not sure, something about generative models and probability distributions? But it definitely does NOT have a reward function or critic / value-function etc.
This has the advantage that (according to you, IIUC) there’s a straightforward way to make the AGI act in a human-like way and follow human norms.
And it has the disadvantage that I’m somewhat skeptical that it will ever be possible to actually code up an AGI that way.
So I’m pretty confused here.
For one thing, I’m not yet convinced that the first bullet point is actually straightforward (or even possible). Maybe I didn’t follow your previous response. Some of my concerns are: (1) most human actions are not visible (e.g. deciding what to think about, recalling a memory), (2) even the ones that are visible in principle are very hard to extract in practice (e.g. did I move deliberately or did was that a random jostle or gust of wind?) (3) almost all “outcomes” of interest in the AGI context are outcomes that have never happened in the training data, e.g. the AGI can invent a new gadget which no human had ever previously invented. So I’m not sure how you get p(o,a) from observations of humans.
For the second thing, among other issues, it seems to me that building a beyond-human-level understanding of the world requires RL-type trial-and-error exploration, for reasons in Section 1.1 here.