Previously Jacob Cannell wrote the post "Brain Efficiency" which makes several radical claims: that the brain is at the pareto frontier of speed, energy efficiency and memory bandwith, that this represent a fundamental physical frontier.
Here's an AI-generated summary
The article “Brain Efficiency: Much More than You Wanted to Know” on LessWrong discusses the efficiency of physical learning machines. The article explains that there are several interconnected key measures of efficiency for physical learning machines: energy efficiency in ops/J, spatial efficiency in ops/mm^2 or ops/mm^3, speed efficiency in time/delay for key learned tasks, circuit/compute efficiency in size and steps for key low-level algorithmic tasks, and learning/data efficiency in samples/observations/bits required to achieve a level of circuit efficiency, or per unit thereof. The article also explains why brain efficiency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity. The article predicts that AGI will consume compute & data in predictable brain-like ways and suggests that AGI will be far more like human simulations/emulations than you’d otherwise expect and will require training/education/raising vaguely like humans1.
Jake further has argued that this has implication for FOOM and DOOM.
Considering the intense technical mastery of nanoelectronics, thermodynamics and neuroscience required to assess the arguments here I concluded that a public debate between experts was called for. This was the start of the Brain Efficiency Prize contest which attracted over a 100 in-depth technically informed comments.
Now for the winners! Please note that the criteria for winning the contest was based on bringing in novel and substantive technical arguments as assesed by me. In contrast, general arguments about the likelihood of FOOM or DOOM while no doubt interesting did not factor into the judgement.
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