I would disagree with the notion that the cost of mastering a world scales with the cost of the world model. For instance, the learning with errors problem has a completely straightforward mathematical description, and yet strong quantum-resistant public-key cryptosystems can be built on it; there is every possibility that even a superintelligence a million years from now will be unable to read a message encrypted today using AES-256 encapsulated using a known Kyber public key with conservatively chosen security parameters.
Similarly, it is not clear to me at all what is even meant by saying that a tiny neural network can perfectly predict the “world” of Go. I would expect that even predicting the mere mechanics of the game, for instance determining that a group has just been captured by the last move of the opponent, will be difficult for small neural networks when examples are adversarially chosen (think of a group that snakes around the whole board, overwhelming the small NN capability to count liberties). The complexity of determining consequences of actions in Go is much more dependent on the depth of the required search than on the size of the game state, and it is easy to find examples on the 19x19 standard board size that will overwhelm any feed-forward neural network of reasonable size (but not necessarily networks augmented with tree search).
With regards to FOOM, I agree that doom from foom seems like an unlikely prospect (mainly due to diminishing returns on the utility of intelligence in many competitive settings) and I would agree that FOOM would require some experimental loop to be closed, which will push out time scales. I would also agree that the example of Go does not show what Yudkowsky thinks it does (it does help that this is a small world where it is feasible to do large reinforcement learning runs, and even then, Go programs have mostly confirmed human strategy, not totally upended it). But the possibility that if an unaided large NN achieved AGI or weak ASI, it would then be able to bootstrap itself to a much stronger level of ASI in a relatively short time (similar to the development cycle timeframe that led to the AGI/weak ASI itself; but involving extensive experimentation, so neither undetectable nor done in minutes or days) by combining improved algorithmic scaffolding with a smaller/faster policy network does not seem outlandish to me.
Lastly, I would argue that foom is in fact an observable phenomenon today. We see self-reinforcing, rapid, sudden onset improvement every time a neural network during training discovers a substantially new capability and then improves on it before settling into a new plateau. This is known as grokking and well-described in the literature on neural networks; there are even simple synthetic problems that produce a nice repeated pattern of grokking at successive levels of performance when a neural network is trained to solve them. I would expect that fooming can occur at various scales. However, I find the case that a large grokking step automatically happens when a system approaches human-level competence on general problem unconvincing (on the other hand, of course a large grokking step could happen in a system already at human-level competence by chance or happenstance and push into the weak ASI regime in a short time frame).
Similarly, it is not clear to me at all what is even meant by saying that a tiny neural network can perfectly predict the “world” of Go
By world model I specifically meant a model of the world physics. For chess/go this is just a tiny amount of memory to store the board state, and a simple set of rules that are very fast to evaluate. I agree that evaluating the rules of go is a bit more complex than chess, especially in edge cases, but still enormously simpler than evaluating the physics of the real world.
I think we probably agree about grokking in NNs but I am doubting that EY would describe that as foom.
I would disagree with the notion that the cost of mastering a world scales with the cost of the world model. For instance, the learning with errors problem has a completely straightforward mathematical description, and yet strong quantum-resistant public-key cryptosystems can be built on it; there is every possibility that even a superintelligence a million years from now will be unable to read a message encrypted today using AES-256 encapsulated using a known Kyber public key with conservatively chosen security parameters.
Similarly, it is not clear to me at all what is even meant by saying that a tiny neural network can perfectly predict the “world” of Go. I would expect that even predicting the mere mechanics of the game, for instance determining that a group has just been captured by the last move of the opponent, will be difficult for small neural networks when examples are adversarially chosen (think of a group that snakes around the whole board, overwhelming the small NN capability to count liberties). The complexity of determining consequences of actions in Go is much more dependent on the depth of the required search than on the size of the game state, and it is easy to find examples on the 19x19 standard board size that will overwhelm any feed-forward neural network of reasonable size (but not necessarily networks augmented with tree search).
With regards to FOOM, I agree that doom from foom seems like an unlikely prospect (mainly due to diminishing returns on the utility of intelligence in many competitive settings) and I would agree that FOOM would require some experimental loop to be closed, which will push out time scales. I would also agree that the example of Go does not show what Yudkowsky thinks it does (it does help that this is a small world where it is feasible to do large reinforcement learning runs, and even then, Go programs have mostly confirmed human strategy, not totally upended it). But the possibility that if an unaided large NN achieved AGI or weak ASI, it would then be able to bootstrap itself to a much stronger level of ASI in a relatively short time (similar to the development cycle timeframe that led to the AGI/weak ASI itself; but involving extensive experimentation, so neither undetectable nor done in minutes or days) by combining improved algorithmic scaffolding with a smaller/faster policy network does not seem outlandish to me.
Lastly, I would argue that foom is in fact an observable phenomenon today. We see self-reinforcing, rapid, sudden onset improvement every time a neural network during training discovers a substantially new capability and then improves on it before settling into a new plateau. This is known as grokking and well-described in the literature on neural networks; there are even simple synthetic problems that produce a nice repeated pattern of grokking at successive levels of performance when a neural network is trained to solve them. I would expect that fooming can occur at various scales. However, I find the case that a large grokking step automatically happens when a system approaches human-level competence on general problem unconvincing (on the other hand, of course a large grokking step could happen in a system already at human-level competence by chance or happenstance and push into the weak ASI regime in a short time frame).
By world model I specifically meant a model of the world physics. For chess/go this is just a tiny amount of memory to store the board state, and a simple set of rules that are very fast to evaluate. I agree that evaluating the rules of go is a bit more complex than chess, especially in edge cases, but still enormously simpler than evaluating the physics of the real world.
I think we probably agree about grokking in NNs but I am doubting that EY would describe that as foom.