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I think they'd still say roughly what I'm saying - we'd need to actually understand how intelligence works to make changes that go that far from natural designs and we don't. There's a decent amount of funding in genetics and neuroscience, not 60B/year but decent. They'd also probably say that IQ might not mean anything at 300 and that being that smart might not actually be physically possible even if it did mean something.
Are there examples of actual geneticists speaking about limits of linear regression PGI?
Depends on what you mean by "that far".
there isn't, say, IQ genetics study with sample of 10 million with full genome sequencing.
Because what? Smaller animals significantly outperform larger animals with same brain size, their brains are more tightly packed. Why wouldn't be elephant sized brain with density of sparrow be smarter? Cooling might be a problem, though.
(wrote in a hurry) IQ is basically defined to be a normal distribution. 250 iq is 10 standard deviations above the mean, and the probability of being >= that is like 1 in 10^23. We can't sample from the distribution of 10^23 humans, and even if we could it's not obvious what distribution to pick or why, so even in theory I'm not sure what 250 would mean.
10^10 data points is still a lot of data. I think it would be enough to crack nonlinearity to process to human twice further from baseline than existing best individuals. It's unknown nonlinearity, but not cryptographically secure nonlinearity.
AGI is ... also "unknown linearity", and arguably in the exact same sense. The data points mostly duplicate each other and test the effects of small incremental changes. It takes a while to build intelligence out of tiny changes, as the history of the planet suggests
I do not think given random two individuals of 160 IQ are mostly duplicates of each other.
If industry could profit from engineering 300 IQ humans as they can profit from LLM AIs, we would have it by now. .... i think "we" will have ASI sooner.
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