This is pretty interesting. There is a lot to quibble about here, but overall I think the information about bees here is quite valuable for people thinking about where AI is at right now and trying to extrapolate forward.
A different approach, perhaps more illuminating would be to ask how much of a bee’s behavior could we plausibly emulate today by globing together a bunch of different ML algorithms into some sort of virtual bee cognitive architecture—if say we wanted to make a drone that behaved like a bee ala Black Mirror. Obviously that’s a much more complicated question, though.
There were a few sections I skipped, if I have time I’ll come back and do a more thorough reading and give some more comments.
The compute comparison seems pretty sketchy to me. A bee’s visual cortex can classify many different things, and the part responsible for doing the classification task in the few shot learning study is probably just a small subset. [I think below Rohin made a similar point below.] Deep learning models can be pruned somewhat without loosing much accuracy, but generally all the parameters are used. Another wrinkle is the rate of firing activity in the visual cortex depends on the input, although there is a baseline rate too. The point I’m getting at is it’s sort of an apples-to-oranges comparison. If the bee only had to do the one task in the study to survive, evolution probably would have found a much more economical way of doing it, with far fewer neurons.
My other big quibble I have is I would have made transparent that Cotra’s biological anchors method for forecasting TAI assumes that we will know the right algorithm before the hardware becomes available. That is a big questionable assumption and thus should be stated clearly. Arguably algorithmic advancement in AI at the level of core algorithms (not ML-ops / dev ops / GPU coding) is actually quite slow. In any case, it just seems very hard to predict algorithmic advancement. Plausibly a team at DeepMind might discover the key cortical learning algorithm underlying human intelligence tomorrow, but there’s other reasons to think it could take decades.
This is pretty interesting. There is a lot to quibble about here, but overall I think the information about bees here is quite valuable for people thinking about where AI is at right now and trying to extrapolate forward.
A different approach, perhaps more illuminating would be to ask how much of a bee’s behavior could we plausibly emulate today by globing together a bunch of different ML algorithms into some sort of virtual bee cognitive architecture—if say we wanted to make a drone that behaved like a bee ala Black Mirror. Obviously that’s a much more complicated question, though.
I feel compelled to mention my friend Logan Thrasher Collins’ paper, The case for emulating insect brains using anatomical “wiring diagrams” equipped with biophysical models of neuronal activity. He thinks we may be able to emulate the fruit fly brain in about 20 years at near-full accuracy, and this estimate seems quite plausible.
There were a few sections I skipped, if I have time I’ll come back and do a more thorough reading and give some more comments.
The compute comparison seems pretty sketchy to me. A bee’s visual cortex can classify many different things, and the part responsible for doing the classification task in the few shot learning study is probably just a small subset. [I think below Rohin made a similar point below.] Deep learning models can be pruned somewhat without loosing much accuracy, but generally all the parameters are used. Another wrinkle is the rate of firing activity in the visual cortex depends on the input, although there is a baseline rate too. The point I’m getting at is it’s sort of an apples-to-oranges comparison. If the bee only had to do the one task in the study to survive, evolution probably would have found a much more economical way of doing it, with far fewer neurons.
My other big quibble I have is I would have made transparent that Cotra’s biological anchors method for forecasting TAI assumes that we will know the right algorithm before the hardware becomes available. That is a big questionable assumption and thus should be stated clearly. Arguably algorithmic advancement in AI at the level of core algorithms (not ML-ops / dev ops / GPU coding) is actually quite slow. In any case, it just seems very hard to predict algorithmic advancement. Plausibly a team at DeepMind might discover the key cortical learning algorithm underlying human intelligence tomorrow, but there’s other reasons to think it could take decades.