The default hypothesis—the one that requires evidence to update against—is now that the brain is efficient in most respects, rather than the converse.
I think you have basically not made that case, certainly not to such a degree that people who previously believed the opposite will be convinced. You explored a few specific dimensions—like energy use, heat dissipation, circuit depth. But these are all things which we’d expect to have been under lots of evolutionary pressure for a long time. They’re also all relatively “low-level” things, in the sense that we wouldn’t expect to need unusually intricate genetic machinery to fine-tune them; we’d expect all those dimensions to be relatively accessible to evolutionary exploration.
If you want to make the case of that brain efficiency is the default hypothesis, then you need to argue it in cases where the relevant capabilities weren’t obviously under lots of selection pressure for a long time (e.g. recently acquired capabilities like language), or where someone might expect architectural complexity to be too great for the genetic information bottleneck. You need to address at least some “hard” cases for brain efficiency, not just “easy” cases.
Or, another angle: I’d expect that, by all of the efficiency measures in your brain efficiency post, a rat brain also looks near-optimal. Therefore, by anology to your argument, we should conclude that it is not possible for some new biological organism to undergo a “hard takeoff” (relative to evolutionary timescales) in intelligent reasoning capabilities. Where does that argument fail? What inefficiency in the rat brain did humanity improve on? If it was language, why do expect that the apparently-all-important language capability is near-optimal in humans? Also, why do we expect there won’t be some other all-important capability, just like language was a new super-important capability in the rat → human transition?
In a nutshell EY/LW folks got much of their brain model from the heuristics and biases, ev psych literature which is based on the evolved modularity hypothesis, which turned out to be near completely wrong. So just by merely reading the sequences and associated lit LW folks have unfortunately picked up a fairly inaccurate default view of the brain.
In a nutshell the brain is a very generic/universal learning system built mostly out of a few different complimentary types of neural computronium (cortex, cerebellum, etc) and an actual practical recursive self improvement learning system that rapidly learns efficient circuit architecture from lifetime experience. The general meta-architecture is not specific to humans, primates, or even mammals, and in fact is highly convergent and conserved—evolution found and preserved it again and again across wildly divergent lineages. So there isn’t so much room for improvement in architecture, most of the improvement comes solely from scaling.
Nonetheless there are important differences across the lineages: primates along with some birds and perhaps some octopoda have the most scaling efficient archs in terms of neuron/synapse density, but these differences are most likely due to diverging optimization pressures along a pareto efficiency frontier.
The difference in brain capabilities are then mostly just scaling differences: human brains are just 4x scaled up primate brains, having nearly zero detectable divergences from the core primate architecture (brain size is not a static feature of arch, the arch also defines a scaling plan, so you can think of size as being a tunable hyperparam with many downstream modifications to the wiring prior). Rodent brain arch has probably the worst scaling plan, probably they are optimized for speed and rarely grew large.
I think you have basically not made that case, certainly not to such a degree that people who previously believed the opposite will be convinced. You explored a few specific dimensions—like energy use, heat dissipation, circuit depth. But these are all things which we’d expect to have been under lots of evolutionary pressure for a long time. They’re also all relatively “low-level” things, in the sense that we wouldn’t expect to need unusually intricate genetic machinery to fine-tune them; we’d expect all those dimensions to be relatively accessible to evolutionary exploration.
If you want to make the case of that brain efficiency is the default hypothesis, then you need to argue it in cases where the relevant capabilities weren’t obviously under lots of selection pressure for a long time (e.g. recently acquired capabilities like language), or where someone might expect architectural complexity to be too great for the genetic information bottleneck. You need to address at least some “hard” cases for brain efficiency, not just “easy” cases.
Or, another angle: I’d expect that, by all of the efficiency measures in your brain efficiency post, a rat brain also looks near-optimal. Therefore, by anology to your argument, we should conclude that it is not possible for some new biological organism to undergo a “hard takeoff” (relative to evolutionary timescales) in intelligent reasoning capabilities. Where does that argument fail? What inefficiency in the rat brain did humanity improve on? If it was language, why do expect that the apparently-all-important language capability is near-optimal in humans? Also, why do we expect there won’t be some other all-important capability, just like language was a new super-important capability in the rat → human transition?
I already made much of the brain architecture/algorithms argument in an earlier post: “The Brain as a Universal Learning Machine”.
In a nutshell EY/LW folks got much of their brain model from the heuristics and biases, ev psych literature which is based on the evolved modularity hypothesis, which turned out to be near completely wrong. So just by merely reading the sequences and associated lit LW folks have unfortunately picked up a fairly inaccurate default view of the brain.
In a nutshell the brain is a very generic/universal learning system built mostly out of a few different complimentary types of neural computronium (cortex, cerebellum, etc) and an actual practical recursive self improvement learning system that rapidly learns efficient circuit architecture from lifetime experience. The general meta-architecture is not specific to humans, primates, or even mammals, and in fact is highly convergent and conserved—evolution found and preserved it again and again across wildly divergent lineages. So there isn’t so much room for improvement in architecture, most of the improvement comes solely from scaling.
Nonetheless there are important differences across the lineages: primates along with some birds and perhaps some octopoda have the most scaling efficient archs in terms of neuron/synapse density, but these differences are most likely due to diverging optimization pressures along a pareto efficiency frontier.
The difference in brain capabilities are then mostly just scaling differences: human brains are just 4x scaled up primate brains, having nearly zero detectable divergences from the core primate architecture (brain size is not a static feature of arch, the arch also defines a scaling plan, so you can think of size as being a tunable hyperparam with many downstream modifications to the wiring prior). Rodent brain arch has probably the worst scaling plan, probably they are optimized for speed and rarely grew large.