Thank you for the explanations. They were crystal-clear.
[W]hat’s so interesting about the PP analysis of the Pong experiment, then, if you agree that the random-noise-RL thing doesn’t work very well compared to alternatives? IE, why derive excitement about a particular deep theory about intelligence (PP) based on a really dumb learning algorithm (noise-based RL)?
What alternatives? Do you mean like flooding the network with neurotransmitters? Or do you mean like the stuff we use in ML? There’s lots of better-performing algorithms that you can implement on an electronic computer, but many of them just won’t run on evolved biological neuron cells.
Why the Pong experiment caught my attention is that it is relatively simple on the hardware side, which means it could have evolved very early in the evolutionary chain.
Well, I guess another alternative (also very simple on the hardware side) would be the generalization of the Pavlov strategy which I mentioned earlier. This also has the nice feature that lots of little pieces with their own simple ‘goals’ can coalesce into one agent-like strategy, and it furthermore works with as much or little communication as you give it (so there’s not automatically a communication overhead to achieve the coordination).
However, I won’t try to argue that it’s plausible that biological brains are using something like that.
I guess the basic answer to my question is that you’re quite motivated by biological plausibility. There are many reasons why this might be, so I shouldn’t guess at the specific motives.
For myself, I tend to be disinterested in biologically plausible algorithms if it’s easy to point at other algorithms which do better with similar efficiency on computers. (Although results like the equivalence between predictive coding and gradient descent can be interesting for other reasons.) I think bounded computation has to do with important secrets of intelligence, but for example, I find logical induction to be a deeper theory of bounded rationality than (my understanding of) predictive processing—predictive processing seems closer to “getting excited about some specific approximation methods” whereas logical induction seems closer to “principled understanding of what good bounded reasoning even means” (and in particular, obsoletes the idea that bounded rationality is about approximating, in my mind).
I guess the basic answer to my question is that you’re quite motivated by biological plausibility. There are many reasons why this might be, so I shouldn’t guess at the specific motives.
You’re right. I want to know how my own brain works.
But if you’re more interested in a broader mathematical understanding of how intelligence, in general, works, then that could explain some of our motivational disconnect.
An important question is whether PP contains some secrets of intelligence which are critical for AI alignment. I think some intelligent people think the answer is yes. But the biological motivation doesn’t especially point to this (I think). If you have any arguments for such a conclusion I would be curious to hear it.
Maybe? It depends a lot on how I interpret your question. I’m trying to keep these posts contained and so I’d rather not answer that question in this thread.
Thank you for the explanations. They were crystal-clear.
What alternatives? Do you mean like flooding the network with neurotransmitters? Or do you mean like the stuff we use in ML? There’s lots of better-performing algorithms that you can implement on an electronic computer, but many of them just won’t run on evolved biological neuron cells.
Why the Pong experiment caught my attention is that it is relatively simple on the hardware side, which means it could have evolved very early in the evolutionary chain.
Well, I guess another alternative (also very simple on the hardware side) would be the generalization of the Pavlov strategy which I mentioned earlier. This also has the nice feature that lots of little pieces with their own simple ‘goals’ can coalesce into one agent-like strategy, and it furthermore works with as much or little communication as you give it (so there’s not automatically a communication overhead to achieve the coordination).
However, I won’t try to argue that it’s plausible that biological brains are using something like that.
I guess the basic answer to my question is that you’re quite motivated by biological plausibility. There are many reasons why this might be, so I shouldn’t guess at the specific motives.
For myself, I tend to be disinterested in biologically plausible algorithms if it’s easy to point at other algorithms which do better with similar efficiency on computers. (Although results like the equivalence between predictive coding and gradient descent can be interesting for other reasons.) I think bounded computation has to do with important secrets of intelligence, but for example, I find logical induction to be a deeper theory of bounded rationality than (my understanding of) predictive processing—predictive processing seems closer to “getting excited about some specific approximation methods” whereas logical induction seems closer to “principled understanding of what good bounded reasoning even means” (and in particular, obsoletes the idea that bounded rationality is about approximating, in my mind).
You’re right. I want to know how my own brain works.
But if you’re more interested in a broader mathematical understanding of how intelligence, in general, works, then that could explain some of our motivational disconnect.
An important question is whether PP contains some secrets of intelligence which are critical for AI alignment. I think some intelligent people think the answer is yes. But the biological motivation doesn’t especially point to this (I think). If you have any arguments for such a conclusion I would be curious to hear it.
Maybe? It depends a lot on how I interpret your question. I’m trying to keep these posts contained and so I’d rather not answer that question in this thread.