OK yeah, “AGI is possible on chips but only if you have 1e100 of them or whatever” is certainly a conceivable possibility. :) For example, here’s me responding to someone arguing along those lines.
If there are any neuroscientists who have investigated this I would be interested!
(1) if you look at how human brains, say, go to the moon, or invent quantum mechanics, and you think about what algorithms could underlie that, then you would start talking about algorithms that entail building generative models, and editing them, and querying them, and searching through them, and composing them, blah blah.
(2) if you look at a biological brain’s low-level affordances, it’s a bunch of things related to somatic spikes and dendritic spikes and protein cascades and releasing and detecting neuropeptides etc.
(3) if you look at silicon chip’s low-level affordances, it’s a bunch of things related to switching transistors and currents going down wires and charging up capacitors and so on.
My view is: implementing (1) via (3) would involve a lot of inefficient bottlenecks where there’s no low-level affordance that’s a good match to the algorithmic operation we want … but the same is true of implementing (1) via (2). Indeed, I think the human brain does what it does via some atrociously inefficient workarounds to the limitations of biological neurons, limitations which would not be applicable to silicon chips.
By contrast, many people thinking about this problem are often thinking about “how hard is it to use (3) to precisely emulate (2)?”, rather than “what’s the comparison between (1)←(3) versus (1)←(2)?”. (If you’re still not following, see my discussion here—search for “transistor-by-transistor simulation of a pocket calculator microcontroller chip”.)
Another thing is that, if you look at what a single consumer GPU can do when it runs an LLM or diffusion model… well it’s not doing human-level AGI, but it’s sure doing something, and I think it’s a sound intuition (albeit hard to formalize) to say “well it kinda seems implausible that the brain is doing something that’s >1000× harder to calculate than that”.
Thanks for those links, this is an interesting topic I may look into more in the future.
Another thing is that, if you look at what a single consumer GPU can do when it runs an LLM or diffusion model… well it’s not doing human-level AGI, but it’s sure doing something, and I think it’s a sound intuition (albeit hard to formalize) to say “well it kinda seems implausible that the brain is doing something that’s >1000× harder to calculate than that”.
It doesn’t seem that implausible to me. In general I find the computational power required for different tasks (such as what I do in computational physics) frequently varies by many orders of magnitude. LLMs get to their level of performance by sifting throughall the data on the internet, something we can’t do, and yet still perform worse than a regular human on many tasks, so clearly theres a lot of extra something going on here. It actually seems kind of likely to me that what the brain is doing is more than 3 orders of magnitude more difficult.
I don’t know enough to be confident on any of this, but If AGI turns out to be impossible on silicon chips with earths resources, I would be surprised but not totally shocked.
OK yeah, “AGI is possible on chips but only if you have 1e100 of them or whatever” is certainly a conceivable possibility. :) For example, here’s me responding to someone arguing along those lines.
There is never a neuroscience consensus but fwiw I fancy myself a neuroscientist and have some thoughts at: Thoughts on hardware / compute requirements for AGI.
One of various points I bring up is that:
(1) if you look at how human brains, say, go to the moon, or invent quantum mechanics, and you think about what algorithms could underlie that, then you would start talking about algorithms that entail building generative models, and editing them, and querying them, and searching through them, and composing them, blah blah.
(2) if you look at a biological brain’s low-level affordances, it’s a bunch of things related to somatic spikes and dendritic spikes and protein cascades and releasing and detecting neuropeptides etc.
(3) if you look at silicon chip’s low-level affordances, it’s a bunch of things related to switching transistors and currents going down wires and charging up capacitors and so on.
My view is: implementing (1) via (3) would involve a lot of inefficient bottlenecks where there’s no low-level affordance that’s a good match to the algorithmic operation we want … but the same is true of implementing (1) via (2). Indeed, I think the human brain does what it does via some atrociously inefficient workarounds to the limitations of biological neurons, limitations which would not be applicable to silicon chips.
By contrast, many people thinking about this problem are often thinking about “how hard is it to use (3) to precisely emulate (2)?”, rather than “what’s the comparison between (1)←(3) versus (1)←(2)?”. (If you’re still not following, see my discussion here—search for “transistor-by-transistor simulation of a pocket calculator microcontroller chip”.)
Another thing is that, if you look at what a single consumer GPU can do when it runs an LLM or diffusion model… well it’s not doing human-level AGI, but it’s sure doing something, and I think it’s a sound intuition (albeit hard to formalize) to say “well it kinda seems implausible that the brain is doing something that’s >1000× harder to calculate than that”.
Thanks for those links, this is an interesting topic I may look into more in the future.
It doesn’t seem that implausible to me. In general I find the computational power required for different tasks (such as what I do in computational physics) frequently varies by many orders of magnitude. LLMs get to their level of performance by sifting through all the data on the internet, something we can’t do, and yet still perform worse than a regular human on many tasks, so clearly theres a lot of extra something going on here. It actually seems kind of likely to me that what the brain is doing is more than 3 orders of magnitude more difficult.
I don’t know enough to be confident on any of this, but If AGI turns out to be impossible on silicon chips with earths resources, I would be surprised but not totally shocked.