Hinton’s Forward-Forward Algorithm aims to do autonomous learning modelled off what the human brain does during sleep. I’m unsure how much relative optimisation power has been invested in exploring the fundamentals like this. I expect the deeplearning+backprop paradigm to have had a blocking effect preventing other potentially more exponential paradigms from being adequately pursued. It’s hard to work on reinventing the fundamentals when you know you’ll get much better immediate performance if you lose faith and switch to what’s known to work.
But I also expect Hinton is a nigh-unrivalled genius and there’s not a flood of people who have a chance to revolutionise the field even if they tried. A race for immediate performance gains may, in an unlikely hypothetical, be good for humanity because researchers won’t have as much slack to do long-shot innovation.
I think forward-forward is basically a drop-in replacement for backprop: they’re both approaches to update a set of adjustable parameters / weights in a supervised-learning setting (i.e. when there’s after-the-fact ground truth for what the output should have been). FF might work better or worse than backprop, FF might be more or less parallelizable than backprop, whatever, I dunno. My guess is that the thing backprop is doing, it’s doing it more-or-less optimally, and drop-in-replacements-for-backprop are mainly interesting for better scientific understanding of how the brain works (the brain doesn’t use backprop, but also the brain can’t use backprop because of limitations of biological neurons, so that fact provides no evidence either way about whether backprop is better than [whatever backprop-replacement is used by the brain, which is controversial]). But even if FF will lead to improvements over backprop, it wouldn’t be the kind of profound change you seem to be implying. It would look like “hey now the loss goes down faster during training” or whatever. It wouldn’t be progress towards autonomous learning, right?
Tbc, my understanding of FF is “I watched him explain it on YT”. My scary-feeling is just based on feeling like it could get close to mimicking what the brain does during sleep, and that plays a big part of autonomous learning. Sleeping is not just about cycles of encoding and consolidation, it’s also about mysterious tricks for internally reorganising and generalising knowledge. And/or maybe it’s about confabulating sensory input as adversarial training data for learning to discern between real and imagined input. Either way, I expect there to be untapped potential for ANN innovation at the bottom, and “sleep” is part of it.
One the other hand, if they don’t end up cracking the algorithms behind sleep and the like, this could be good wrt safety, given that I’m tentatively pessimistic about the potential of the leading paradigm to generalise far and learn to be “deeply” coherent.
Oh, and also… This post and the comment thread is full of ideas that people can use to fuel their interest in novel capabilities research. Seems risky. Quinton’s points about DNA and evolution can be extrapolated to the hypothesis that “information bottlenecks” could be a cost-effective way of increasing the rate at which networks generalise, and that may or may not be something we want. (This is a known thing, however, so it’s not the riskiest thing to say.)
FWIW my 2¢ are: I consider myself more paranoid than most, and don’t see anything here as “risky” enough to be worth thinking about, as of this writing. (E.g. people are already interested in novel capabilities research.)
I don’t know that much about the field or what top researchers are thinking about, so I know I’m naive about most of my independent models. But I think it’s good for my research trajectory to act on my inside views anyway. And to talk about them with people who may sooner show me how naive I am. :)
Hinton’s Forward-Forward Algorithm aims to do autonomous learning modelled off what the human brain does during sleep. I’m unsure how much relative optimisation power has been invested in exploring the fundamentals like this. I expect the deeplearning+backprop paradigm to have had a blocking effect preventing other potentially more exponential paradigms from being adequately pursued. It’s hard to work on reinventing the fundamentals when you know you’ll get much better immediate performance if you lose faith and switch to what’s known to work.
But I also expect Hinton is a nigh-unrivalled genius and there’s not a flood of people who have a chance to revolutionise the field even if they tried. A race for immediate performance gains may, in an unlikely hypothetical, be good for humanity because researchers won’t have as much slack to do long-shot innovation.
I’m scared of the FFA thing, though.
I think forward-forward is basically a drop-in replacement for backprop: they’re both approaches to update a set of adjustable parameters / weights in a supervised-learning setting (i.e. when there’s after-the-fact ground truth for what the output should have been). FF might work better or worse than backprop, FF might be more or less parallelizable than backprop, whatever, I dunno. My guess is that the thing backprop is doing, it’s doing it more-or-less optimally, and drop-in-replacements-for-backprop are mainly interesting for better scientific understanding of how the brain works (the brain doesn’t use backprop, but also the brain can’t use backprop because of limitations of biological neurons, so that fact provides no evidence either way about whether backprop is better than [whatever backprop-replacement is used by the brain, which is controversial]). But even if FF will lead to improvements over backprop, it wouldn’t be the kind of profound change you seem to be implying. It would look like “hey now the loss goes down faster during training” or whatever. It wouldn’t be progress towards autonomous learning, right?
Tbc, my understanding of FF is “I watched him explain it on YT”. My scary-feeling is just based on feeling like it could get close to mimicking what the brain does during sleep, and that plays a big part of autonomous learning. Sleeping is not just about cycles of encoding and consolidation, it’s also about mysterious tricks for internally reorganising and generalising knowledge. And/or maybe it’s about confabulating sensory input as adversarial training data for learning to discern between real and imagined input. Either way, I expect there to be untapped potential for ANN innovation at the bottom, and “sleep” is part of it.
One the other hand, if they don’t end up cracking the algorithms behind sleep and the like, this could be good wrt safety, given that I’m tentatively pessimistic about the potential of the leading paradigm to generalise far and learn to be “deeply” coherent.
Oh, and also… This post and the comment thread is full of ideas that people can use to fuel their interest in novel capabilities research. Seems risky. Quinton’s points about DNA and evolution can be extrapolated to the hypothesis that “information bottlenecks” could be a cost-effective way of increasing the rate at which networks generalise, and that may or may not be something we want. (This is a known thing, however, so it’s not the riskiest thing to say.)
FWIW my 2¢ are: I consider myself more paranoid than most, and don’t see anything here as “risky” enough to be worth thinking about, as of this writing. (E.g. people are already interested in novel capabilities research.)
I don’t know that much about the field or what top researchers are thinking about, so I know I’m naive about most of my independent models. But I think it’s good for my research trajectory to act on my inside views anyway. And to talk about them with people who may sooner show me how naive I am. :)