Right. So, what do you think about the AI-timelines-related claim then? Will we need medium or long-horizon training for a number of episodes within an OOM or three of parameter count to get something x-risky?
ETA: To put it more provocatively: If EfficientZero can beat humans at Atari using less game experience starting from a completely blank slate whereas humans have decades of pre-training, then shouldn’t a human-brain-sized EfficientZero beat humans at any intellectual task given decades of experience at those tasks + decades of pre-training similar to human pre-training.
I have no good argument that a human-sized EfficientZero would somehow need to be much slower than humans.
Arguing otherwise sounds suspiciously like moving the goalposts after an AI effect: “look how stupid DL agents are, they need tons of data to few-shot stuff like challenging text tasks or image classifications, and they OOMs more data on even something as simple as ALE games! So inefficient! So un-human-like! This should deeply concern any naive DL enthusiast, that the archs are so bad & inefficient.” [later] “Oh no. Well… ‘the curves cross’, you know, this merely shows that DL agents can get good performance on uninteresting tasks, but human brains will surely continue showing their tremendous sample-efficiency in any real problem domain, no matter how you scale your little toys.”
As I’ve said before, I continue to ask myself what it is that the human brain does with all the resources it uses, particularly with the estimates that put it at like 7 OOMs more than models like GPT-3 or other wackily high FLOPS-equivalence. It does not seem like those models do ‘0.0000001% of human performance’, in some sense.
Not out of the box, but it’s also not designed at all for doing exploration. Exploration in MuZero is an obvious but largely (ahem) unexplored topic. Such is research: only a few people in the world can do research with MuZero on meaningful problems like ALE, and not everything will happen at once. I think the model-based nature of MuZero means that a lot of past approaches (like training an ensemble of MuZeros and targeting parts of the game tree where the models disagree most on their predictions) ought to port into it pretty easily. We’ll see if that’s enough to match Go-Explore.
Right. So, what do you think about the AI-timelines-related claim then? Will we need medium or long-horizon training for a number of episodes within an OOM or three of parameter count to get something x-risky?
ETA: To put it more provocatively: If EfficientZero can beat humans at Atari using less game experience starting from a completely blank slate whereas humans have decades of pre-training, then shouldn’t a human-brain-sized EfficientZero beat humans at any intellectual task given decades of experience at those tasks + decades of pre-training similar to human pre-training.
I have no good argument that a human-sized EfficientZero would somehow need to be much slower than humans.
Arguing otherwise sounds suspiciously like moving the goalposts after an AI effect: “look how stupid DL agents are, they need tons of data to few-shot stuff like challenging text tasks or image classifications, and they OOMs more data on even something as simple as ALE games! So inefficient! So un-human-like! This should deeply concern any naive DL enthusiast, that the archs are so bad & inefficient.” [later] “Oh no. Well… ‘the curves cross’, you know, this merely shows that DL agents can get good performance on uninteresting tasks, but human brains will surely continue showing their tremendous sample-efficiency in any real problem domain, no matter how you scale your little toys.”
As I’ve said before, I continue to ask myself what it is that the human brain does with all the resources it uses, particularly with the estimates that put it at like 7 OOMs more than models like GPT-3 or other wackily high FLOPS-equivalence. It does not seem like those models do ‘0.0000001% of human performance’, in some sense.
Can EfficientZero beat Montezuma’s Revenge?
Not out of the box, but it’s also not designed at all for doing exploration. Exploration in MuZero is an obvious but largely (ahem) unexplored topic. Such is research: only a few people in the world can do research with MuZero on meaningful problems like ALE, and not everything will happen at once. I think the model-based nature of MuZero means that a lot of past approaches (like training an ensemble of MuZeros and targeting parts of the game tree where the models disagree most on their predictions) ought to port into it pretty easily. We’ll see if that’s enough to match Go-Explore.