There seems to be much more diversity in human cognitive performance than there is in human-brain-energy-efficiency; whether this is due to larger differences in the underlying software (to the extent that this is meaningfully commensurable with differences in hardware) or because smaller differences in that domain result in much larger differences in observable outputs, or both, none of that really takes away from the fact that brain software does not seem to be anywhere near the relevant efficiency frontier, especially since many trade-offs which were operative at an evolutionary scale simply aren’t when it comes to software.
Human mind software evolves at cultural speeds so its recent age isn’t comparably relevant. Diversity in human cognitive capabilities results from the combined oft multiplicative effects of brain hardware differences compounding unique training datasets/experiences.
Its well known in DL that you can’t really compare systems trained on vary different datasets, but that is always the case with humans.
The trained model can change at cultural speeds but the human neural network architecture, hyperparameters, and reward functions can’t. (Or do you think those are already close to optimal for doing science & technology or executing complicated projects etc.?)
(Whatever current or future chips we wind up using for AGI, we’ll almost definitely be able to change the architecture, hyperparameters, and reward functions without fabricating new chips. So I count those as “software not hardware”. I’m unsure how you’re defining those terms.)
Relatedly, if we can run a brain-like algorithm on computer chips at all (or (eventually) use synth-bio to grow brains in vats, or whatever), then we can increase the number of cortical columns / number of neurons / whatever to be 3× more than any human, and hence (presumably) we would get an AI that would be dramatically more insightful than any human who has ever existed. Specifically, it could hold a far richer and more complicated thought in working memory, whereas humans would have to chunk it and explore it sequentially, which makes it harder to notice connections / analogies / interactions between the parts. I’m unclear on how you’re thinking about things like that. It seems pretty important on my models.
The trained model can change at cultural speeds but the human neural network architecture, hyperparameters, and reward functions can’t. (Or do you think those are already close to optimal for doing science & technology or executing complicated projects etc.?)
You can’t easily completely change either the ANN or BNN architecture/hyperparams after training, as the weights you invest so much compute in learning are largely dependent on those decisions—and actually the architecture is just equivalent to weights. Sure there are ways to add new modules later or regraft things, but that is a very limited scope for improving the already trained modules.
As to your second question—no I don’t think there are huge gains over the human brain arch. In part because the initial arch doesn’t/shouldn’t matter that much. If it does then it wasn’t flexible enough in the first place. One of the key points of my ULM post was pointing out how the human brain—unlike current DL systems—learns the architecture during training through high level sparse wiring patterns. “Architecture” is largely just wiring patterns, and in huge flexible network you can learn architecture.
Relatedly, if we can run a brain-like algorithm on computer chips at all (or (eventually) use synth-bio to grow brains in vats, or whatever), then we can increase the number of cortical columns / number of neurons / whatever to be 3× more than any human,
Sure, but the human brain is already massive and far off chinchilla scaling. It seems much better currently to use your compute/energy budget on running a smaller model much faster (to learn more quickly).
Specifically, it could hold a far richer and more complicated thought in working memory, whereas humans would have to chunk it and explore it sequentially,
GPT4 probably doesn’t have that same working memory limitation baked into its architecture but it doesn’t seem to matter much. I guess its possible it learns that limitation to imitate humans, but regardless I don’t see much evidence that the human working memory limitation is all that constraining.
Sure, but the human brain is already massive and far off chinchilla scaling. It seems much better currently to use your compute/energy budget on running a smaller model much faster (to learn more quickly).
I thought your belief was that the human brain is a scaled up chimp brain, right? If so:
If I compare “one human” versus “lots of chimps working together and running at super-speed”, in terms of ability to do science & technology, the former would obviously absolutely crush the latter.
…So by the same token, if I compare “one model that’s like a 3×-scaled-up human brain” to “lots of models that are like normal (non-scaled-up) human brains, working together and running at super-speed”, in terms of ability to do science & technology, it should be at least plausible that the former would absolutely crush the latter, right?
First the human brain uses perhaps 10x the net effective training compute (3x size, 2x neotany extending training of higher modules, a bit from arch changes), and scale alone leads to new capabilities.
But the main key new capability was the evolution of language, and the resulting cultural revolution. Chimps train on 1e8s of lifetime data or so, and that’s it. Humans train on 1e9s, but that 1e9s dataset is a compression of all the experience of all humans who have ever lived. So the effective dataset size scales with human population size vs being constant, and even a sublinear scaling with population size leads to a radically different regime. The most important inventions driving human civilization progress indirectly or directly drive up that scaling factor.
OK, so in your picture chimps had less training / less scale / worse arch than humans, and this is related to the fact that humans have language and chimps don’t. “Scale alone leads to new capabilities.”
But if we explore the regime of “even more training than humans / even more scale than humans / even better arch than humans”, your claim is that this whole regime is just a giant dead zone where nothing interesting happens, and thus you’re just being inefficient—really you should have split it into multiple smaller models. Correct? If so, why do you think that?
In other words, if scaling up from chimp brains to human brains unlocked new capabilities (namely language), why shouldn’t scaling up from human brains to superhuman brains unlock new capabilities too? Do you think there are no capabilities left, or something?
(Sorry if you’ve already talked about this elsewhere.)
OK, so in your picture chimps had less training / less scale / worse arch than humans, and this is related to the fact that humans have language and chimps don’t. “Scale alone leads to new capabilities.”
Scale in compute and data—as according to NN scaling laws. The language/culture/tech leading to new effective data scaling regime quickly reconfigured the pareto surface payoff for brain size, so its more of a feedback loop rather than a clear cause effect (which is why I would consider it a foom in terms of evolutionary timescales).
In other words, if scaling up from chimp brains to human brains unlocked new capabilities (namely language), why shouldn’t scaling up from human brains to superhuman brains unlock new capabilities too?
Of course, but the new capabilities are more like new skills, mental programs, and wisdom not metasystems transitions (changes to core scaling regime).
A metasystems transition would be something as profound, rare, and as important as transitioning from effective lifetime training data being a constant to effective lifetime data scaling with population size, or transitioning from non-programmable to programmable.
Zoom in and look at what a large NN is for—what does it do? It can soak up more data to acquire more knowledge/skills, and it also learns faster per timestep (as it’s searching in parallel over a wider circuit space per time step), but the latter is already captured in net training compute anyway. So intelligence is mostly about the volume of search space explored, which scales with net training compute—this is almost an obvious direct consequence of Solomon induction or derivation thereof.
I am not arguing that there are no more metasystems transitions, only that “make brains bigger” doesn’t automatically enable them. The single largest impact of digital minds is probably just speed. Not energy efficiency or software efficiency, just raw speed.
There seems to be much more diversity in human cognitive performance than there is in human-brain-energy-efficiency; whether this is due to larger differences in the underlying software (to the extent that this is meaningfully commensurable with differences in hardware) or because smaller differences in that domain result in much larger differences in observable outputs, or both, none of that really takes away from the fact that brain software does not seem to be anywhere near the relevant efficiency frontier, especially since many trade-offs which were operative at an evolutionary scale simply aren’t when it comes to software.
Human mind software evolves at cultural speeds so its recent age isn’t comparably relevant. Diversity in human cognitive capabilities results from the combined oft multiplicative effects of brain hardware differences compounding unique training datasets/experiences.
Its well known in DL that you can’t really compare systems trained on vary different datasets, but that is always the case with humans.
The trained model can change at cultural speeds but the human neural network architecture, hyperparameters, and reward functions can’t. (Or do you think those are already close to optimal for doing science & technology or executing complicated projects etc.?)
(Whatever current or future chips we wind up using for AGI, we’ll almost definitely be able to change the architecture, hyperparameters, and reward functions without fabricating new chips. So I count those as “software not hardware”. I’m unsure how you’re defining those terms.)
Relatedly, if we can run a brain-like algorithm on computer chips at all (or (eventually) use synth-bio to grow brains in vats, or whatever), then we can increase the number of cortical columns / number of neurons / whatever to be 3× more than any human, and hence (presumably) we would get an AI that would be dramatically more insightful than any human who has ever existed. Specifically, it could hold a far richer and more complicated thought in working memory, whereas humans would have to chunk it and explore it sequentially, which makes it harder to notice connections / analogies / interactions between the parts. I’m unclear on how you’re thinking about things like that. It seems pretty important on my models.
You can’t easily completely change either the ANN or BNN architecture/hyperparams after training, as the weights you invest so much compute in learning are largely dependent on those decisions—and actually the architecture is just equivalent to weights. Sure there are ways to add new modules later or regraft things, but that is a very limited scope for improving the already trained modules.
As to your second question—no I don’t think there are huge gains over the human brain arch. In part because the initial arch doesn’t/shouldn’t matter that much. If it does then it wasn’t flexible enough in the first place. One of the key points of my ULM post was pointing out how the human brain—unlike current DL systems—learns the architecture during training through high level sparse wiring patterns. “Architecture” is largely just wiring patterns, and in huge flexible network you can learn architecture.
Sure, but the human brain is already massive and far off chinchilla scaling. It seems much better currently to use your compute/energy budget on running a smaller model much faster (to learn more quickly).
GPT4 probably doesn’t have that same working memory limitation baked into its architecture but it doesn’t seem to matter much. I guess its possible it learns that limitation to imitate humans, but regardless I don’t see much evidence that the human working memory limitation is all that constraining.
I thought your belief was that the human brain is a scaled up chimp brain, right? If so:
If I compare “one human” versus “lots of chimps working together and running at super-speed”, in terms of ability to do science & technology, the former would obviously absolutely crush the latter.
…So by the same token, if I compare “one model that’s like a 3×-scaled-up human brain” to “lots of models that are like normal (non-scaled-up) human brains, working together and running at super-speed”, in terms of ability to do science & technology, it should be at least plausible that the former would absolutely crush the latter, right?
Or if that’s not a good analogy, why not? Thanks.
First the human brain uses perhaps 10x the net effective training compute (3x size, 2x neotany extending training of higher modules, a bit from arch changes), and scale alone leads to new capabilities.
But the main key new capability was the evolution of language, and the resulting cultural revolution. Chimps train on 1e8s of lifetime data or so, and that’s it. Humans train on 1e9s, but that 1e9s dataset is a compression of all the experience of all humans who have ever lived. So the effective dataset size scales with human population size vs being constant, and even a sublinear scaling with population size leads to a radically different regime. The most important inventions driving human civilization progress indirectly or directly drive up that scaling factor.
OK, so in your picture chimps had less training / less scale / worse arch than humans, and this is related to the fact that humans have language and chimps don’t. “Scale alone leads to new capabilities.”
But if we explore the regime of “even more training than humans / even more scale than humans / even better arch than humans”, your claim is that this whole regime is just a giant dead zone where nothing interesting happens, and thus you’re just being inefficient—really you should have split it into multiple smaller models. Correct? If so, why do you think that?
In other words, if scaling up from chimp brains to human brains unlocked new capabilities (namely language), why shouldn’t scaling up from human brains to superhuman brains unlock new capabilities too? Do you think there are no capabilities left, or something?
(Sorry if you’ve already talked about this elsewhere.)
Scale in compute and data—as according to NN scaling laws. The language/culture/tech leading to new effective data scaling regime quickly reconfigured the pareto surface payoff for brain size, so its more of a feedback loop rather than a clear cause effect (which is why I would consider it a foom in terms of evolutionary timescales).
Of course, but the new capabilities are more like new skills, mental programs, and wisdom not metasystems transitions (changes to core scaling regime).
A metasystems transition would be something as profound, rare, and as important as transitioning from effective lifetime training data being a constant to effective lifetime data scaling with population size, or transitioning from non-programmable to programmable.
Zoom in and look at what a large NN is for—what does it do? It can soak up more data to acquire more knowledge/skills, and it also learns faster per timestep (as it’s searching in parallel over a wider circuit space per time step), but the latter is already captured in net training compute anyway. So intelligence is mostly about the volume of search space explored, which scales with net training compute—this is almost an obvious direct consequence of Solomon induction or derivation thereof.
I am not arguing that there are no more metasystems transitions, only that “make brains bigger” doesn’t automatically enable them. The single largest impact of digital minds is probably just speed. Not energy efficiency or software efficiency, just raw speed.