I like this comment, and more generally I feel like there’s more information to be gained from clarifying the analogies to evolution, and gaining clarity on when it’s possible for researchers to tune hyperparameters with shortcuts, vs. cases where they’d have to “boil the oceans.”
Do you have a rough sense on how using your analogy would affect the timeline estimates?
Using Steve’s analogy would make for much shorter timeline estimates. Steve guesses 10-100 runs of online-learning needed, i.e. 10-100 iterations to find the right hyperparameters before you get a training run that produces something actually smart like a human. This is only 1-2 orders of magnitude more compute than the human-brain-human-lifetime anchor, which is the nearest anchor (and which Ajeya assigns only 5% credence to!) Eyeballing the charts it looks like you’d end up with something like 50% probability by 2035, holding fixed all of Ajeya’s other assumptions.
I like this comment, and more generally I feel like there’s more information to be gained from clarifying the analogies to evolution, and gaining clarity on when it’s possible for researchers to tune hyperparameters with shortcuts, vs. cases where they’d have to “boil the oceans.”
Do you have a rough sense on how using your analogy would affect the timeline estimates?
Using Steve’s analogy would make for much shorter timeline estimates. Steve guesses 10-100 runs of online-learning needed, i.e. 10-100 iterations to find the right hyperparameters before you get a training run that produces something actually smart like a human. This is only 1-2 orders of magnitude more compute than the human-brain-human-lifetime anchor, which is the nearest anchor (and which Ajeya assigns only 5% credence to!) Eyeballing the charts it looks like you’d end up with something like 50% probability by 2035, holding fixed all of Ajeya’s other assumptions.
Thanks! I don’t know off the top of my head, sorry.