I do think there’s going to be significant AI capabilities advances from improved understanding of how mammal and bird brains work.
I disagree that more complete scanning of mammalian brains is the bottleneck. I think we actually know enough about mammalian brains and their features which are invariant across members of a species. I think the bottlenecks are:
Understanding the information we do have (scattered across terms of thousands of research papers)
Building compute efficient emulations which accurately reproduce the critical details while abstracting away the unimportant details. Since our limited understanding can’t give certain answers about which details are key, this probably involves quite a bit of parallelizable brute-forceable empirical research.
I think current LLMs can absolutely scale fast enough to be very helpful with these two tasks. So if something still seems to be missing from LLMs after the next scale-up in 2025, I expect hunting for further inspiration from the brain will seem tempting and tractable.
Thus, I think we are well on track for AGI by 2026-2028 even if LLMs don’t continue scaling.
Perhaps LLM will help with that. The reason I think that is less likely is
Deep mind etc is already heavily across biology from what I gather from interview with Demis. If the knowledge was there already there’s a good chance they would have found it
Its something specific we are after, not many small improvements, i.e. the neural code. Specifically back propagation is not how neurons learn. I’m pretty sure how they actually do is not in the literature. Attempts have been made such as the forward-forward algorithm by Hinton, but that didn’t come to anything as far as i can tell. I havn’t seen any suggestion that even with too much detail on biology we know what it is. i.e. can a very detailed neural sim with extreme processing power learn as data efficiently as biology?
If progress must come from a large jump rather than small steps, then LLM have quite a long way to go, i.e. LLM need to speed up coming up ideas as novel as the forward-forward algo to help much. If they are still below that threshold in 2026 then those possible insights are still almost entirely done by people.
Even the smartest minds in the past have been beaten by copying biology in AI. The idea for neural nets came from copying biology. (Though the transformer arch and back prop didn’t)
Deep mind etc is already heavily across biology from what I gather from interview with Demis. If the knowledge was there already there’s a good chance they would have found it
I’ve heard this viewpoint expressed before, and find it extremely confusing. I’ve been studying neuroscience and it’s implications for AI for twenty years now. I’ve read thousands of papers, including most of what DeepMind has produced. There’s still so many untested ideas because biology and the brain are so complex. Also because people tend to flock to popular paradigms, rehashing old ideas rather than testing new ones.
I’m not saying I know where the good ideas are, just that I perceive the explored portions of the Pareto frontier of plausible experiments to be extremely ragged. The are tons of places covered by “Fog of War” where good ideas could be hiding.
DeepMind is a tiny fraction of the scientists in the world that have been working on understanding and emulating the brain. Not all the scientists in the world have managed to test all the reasonable ideas, much less DeepMind alone.
Saying DeepMind has explored the implications of biology for AI is like saying that the Opportunity Rover has explored Mars. Yes, this is absolutely true, but the unexplored area vastly outweighs the explored area. If you think the statement implies “explored ALL of Mars” then you have a very inaccurate picture in mind.
OK fair point. If we are going to use analogies, then my point #2 about a specific neural code shows our different positions I think.
Lets say we are trying to get a simple aircraft of the ground and we have detailed instructions for a large passenger jet. Our problem is that the metal is too weak and cannot be used to make wings, engines etc. In that case detailed plans for aircraft are no use, a single minded focus on getting better metal is what its all about. To me the neural code is like the metal and all the neuroscience is like the plane schematics. Note that I am wary of analogies—you obviously don’t see things like that or you wouldn’t have the position you do. Analogies can explain, but rarely persuade.
A more single minded focus on the neural code would be trying to watch neural connections form in real time while learning is happening. Fixed connectome scans of say mice can somewhat help with that, more direct control of dishbrain, watching the zebra fish brain would all count, however the details of neural biology that are specific to higher mammals would be ignored.
Its possible also that there is a hybrid process, that is the AI looks at all the ideas in the literature then suggests bio experiments to get things over the line.
I do think there’s going to be significant AI capabilities advances from improved understanding of how mammal and bird brains work. I disagree that more complete scanning of mammalian brains is the bottleneck. I think we actually know enough about mammalian brains and their features which are invariant across members of a species. I think the bottlenecks are: Understanding the information we do have (scattered across terms of thousands of research papers) Building compute efficient emulations which accurately reproduce the critical details while abstracting away the unimportant details. Since our limited understanding can’t give certain answers about which details are key, this probably involves quite a bit of parallelizable brute-forceable empirical research.
I think current LLMs can absolutely scale fast enough to be very helpful with these two tasks. So if something still seems to be missing from LLMs after the next scale-up in 2025, I expect hunting for further inspiration from the brain will seem tempting and tractable. Thus, I think we are well on track for AGI by 2026-2028 even if LLMs don’t continue scaling.
Perhaps LLM will help with that. The reason I think that is less likely is
Deep mind etc is already heavily across biology from what I gather from interview with Demis. If the knowledge was there already there’s a good chance they would have found it
Its something specific we are after, not many small improvements, i.e. the neural code. Specifically back propagation is not how neurons learn. I’m pretty sure how they actually do is not in the literature. Attempts have been made such as the forward-forward algorithm by Hinton, but that didn’t come to anything as far as i can tell. I havn’t seen any suggestion that even with too much detail on biology we know what it is. i.e. can a very detailed neural sim with extreme processing power learn as data efficiently as biology?
If progress must come from a large jump rather than small steps, then LLM have quite a long way to go, i.e. LLM need to speed up coming up ideas as novel as the forward-forward algo to help much. If they are still below that threshold in 2026 then those possible insights are still almost entirely done by people.
Even the smartest minds in the past have been beaten by copying biology in AI. The idea for neural nets came from copying biology. (Though the transformer arch and back prop didn’t)
I’ve heard this viewpoint expressed before, and find it extremely confusing. I’ve been studying neuroscience and it’s implications for AI for twenty years now. I’ve read thousands of papers, including most of what DeepMind has produced. There’s still so many untested ideas because biology and the brain are so complex. Also because people tend to flock to popular paradigms, rehashing old ideas rather than testing new ones.
I’m not saying I know where the good ideas are, just that I perceive the explored portions of the Pareto frontier of plausible experiments to be extremely ragged. The are tons of places covered by “Fog of War” where good ideas could be hiding.
DeepMind is a tiny fraction of the scientists in the world that have been working on understanding and emulating the brain. Not all the scientists in the world have managed to test all the reasonable ideas, much less DeepMind alone.
Saying DeepMind has explored the implications of biology for AI is like saying that the Opportunity Rover has explored Mars. Yes, this is absolutely true, but the unexplored area vastly outweighs the explored area. If you think the statement implies “explored ALL of Mars” then you have a very inaccurate picture in mind.
OK fair point. If we are going to use analogies, then my point #2 about a specific neural code shows our different positions I think.
Lets say we are trying to get a simple aircraft of the ground and we have detailed instructions for a large passenger jet. Our problem is that the metal is too weak and cannot be used to make wings, engines etc. In that case detailed plans for aircraft are no use, a single minded focus on getting better metal is what its all about. To me the neural code is like the metal and all the neuroscience is like the plane schematics. Note that I am wary of analogies—you obviously don’t see things like that or you wouldn’t have the position you do. Analogies can explain, but rarely persuade.
A more single minded focus on the neural code would be trying to watch neural connections form in real time while learning is happening. Fixed connectome scans of say mice can somewhat help with that, more direct control of dishbrain, watching the zebra fish brain would all count, however the details of neural biology that are specific to higher mammals would be ignored.
Its possible also that there is a hybrid process, that is the AI looks at all the ideas in the literature then suggests bio experiments to get things over the line.