How many ideas of the same size as “maybe a piecewise linear non-linearity would work better than a sigmoid for not having vanishing gradients” are we away from knowing how to build human-level AI technology?
I think it’s >50% chance that ideas like ReLUs or soft attention are best though of as multiplicative improvements on top of hardware progress (as are many other ideas like auxiliary objectives, objectives that better capture relevant tasks, infrastructure for training more efficiently, dense datasets, etc.), because the basic approach of “optimize for a task that requires cognitive competence” will eventually yield human-level competence. In that sense I think the answer is probably 0.
Maybe my median number of OOMs left before human-level intelligence, including both hardware and software progress, is 10 (pretty made-up). Of that I’d guess around half will come from hardware, so call it 5 OOMs of software progress. Don’t know how big that is relative to ReLUs, maybe 5-10x? (But hard to define the counterfactual w.r.t. activation functions.)
(I think that may imply much shorter timelines than my normal view. That’s mostly from thoughtlessness in this answer which was quickly composed and didn’t take into account many sources of evidence, some is from legit correlations not taken into account here, some is maybe legitimate signal from an alternative estimation approach, not sure.)
When you say hardware progress, do you just mean compute getting cheaper or do you include people spending more on compute? So you are saying, you guess that if we had 10 OOMs of compute today that would have a 50% chance of leading to human-level AI without any further software progress, but realistically you expect that what’ll happen is we get +5 OOMs from increased spending and cheaper hardware, and then +5 “virtual OOMs” from better software?
How many ideas of the same size as “maybe a piecewise linear non-linearity would work better than a sigmoid for not having vanishing gradients” are we away from knowing how to build human-level AI technology?
I think it’s >50% chance that ideas like ReLUs or soft attention are best though of as multiplicative improvements on top of hardware progress (as are many other ideas like auxiliary objectives, objectives that better capture relevant tasks, infrastructure for training more efficiently, dense datasets, etc.), because the basic approach of “optimize for a task that requires cognitive competence” will eventually yield human-level competence. In that sense I think the answer is probably 0.
Maybe my median number of OOMs left before human-level intelligence, including both hardware and software progress, is 10 (pretty made-up). Of that I’d guess around half will come from hardware, so call it 5 OOMs of software progress. Don’t know how big that is relative to ReLUs, maybe 5-10x? (But hard to define the counterfactual w.r.t. activation functions.)
(I think that may imply much shorter timelines than my normal view. That’s mostly from thoughtlessness in this answer which was quickly composed and didn’t take into account many sources of evidence, some is from legit correlations not taken into account here, some is maybe legitimate signal from an alternative estimation approach, not sure.)
When you say hardware progress, do you just mean compute getting cheaper or do you include people spending more on compute? So you are saying, you guess that if we had 10 OOMs of compute today that would have a 50% chance of leading to human-level AI without any further software progress, but realistically you expect that what’ll happen is we get +5 OOMs from increased spending and cheaper hardware, and then +5 “virtual OOMs” from better software?