Natural language has both noise (that you can never model) and signal (that you could model if you were just smart enough). GPT-3 is in the regime where it’s mostly signal (as evidenced by the fact that the loss keeps going down smoothly rather than approaching an asymptote). But it will soon get to the regime where there is a lot of noise, and by the time the model is 9 OOMs bigger I would guess (based on theory) that it will be overwhelmingly noise and training will be very expensive.
So it may or may not work in the sense of meeting some absolute performance threshold, but it will certainly be a very bad way to get there and we’ll do something smarter instead.
Hmm, I don’t count “It may work but we’ll do something smarter instead” as “it won’t work” for my purposes.
I totally agree that noise will start to dominate eventually… but the thing I’m especially interested in with Amp(GPT-7) is not the “7” part but the “Amp” part. Using prompt programming, fine-tuning on its own library, fine-tuning with RL, making chinese-room-bureaucracies, training/evolving those bureaucracies… what do you think about that? Naively the scaling laws would predict that we’d need far less long-horizon data to train them, since they have far fewer parameters, right? Moreover IMO evolved-chinese-room-bureaucracy is a pretty good model for how humans work, and in particular for how humans are able to generalize super well and make long-term plans etc. without many lifetimes of long-horizon training.
Natural language has both noise (that you can never model) and signal (that you could model if you were just smart enough). GPT-3 is in the regime where it’s mostly signal (as evidenced by the fact that the loss keeps going down smoothly rather than approaching an asymptote). But it will soon get to the regime where there is a lot of noise, and by the time the model is 9 OOMs bigger I would guess (based on theory) that it will be overwhelmingly noise and training will be very expensive.
So it may or may not work in the sense of meeting some absolute performance threshold, but it will certainly be a very bad way to get there and we’ll do something smarter instead.
Hmm, I don’t count “It may work but we’ll do something smarter instead” as “it won’t work” for my purposes.
I totally agree that noise will start to dominate eventually… but the thing I’m especially interested in with Amp(GPT-7) is not the “7” part but the “Amp” part. Using prompt programming, fine-tuning on its own library, fine-tuning with RL, making chinese-room-bureaucracies, training/evolving those bureaucracies… what do you think about that? Naively the scaling laws would predict that we’d need far less long-horizon data to train them, since they have far fewer parameters, right? Moreover IMO evolved-chinese-room-bureaucracy is a pretty good model for how humans work, and in particular for how humans are able to generalize super well and make long-term plans etc. without many lifetimes of long-horizon training.