Feedback from the physical experiments that will still be performed is probably more useful for fixing the errors in the simulation software/models, rather than for directly fixing the errors in cell/fly/robot designs.
This is something I want to poke at a bit, because it seems like a pretty core disagreement.
In a completely different domain, do you expect something like DGMR (DeepMind’s precipitation nowcasting ML thingy., basically a GAN over weather radar maps) would work better than non-ML weather models to predict US weather after being trained only on UK weather? I expect not, and I don’t expect the reason it wouldn’t work is anything like “the ML engineers weren’t clever enough”.
The bio simulations need to be more clever than ML on bio data, they need to incorporate feedback from simulations of more basic/fundamental/general principles of chemistry and physics. Making this possible is what the 100-200 subjective years of R&D are for.
I’m not confident 100-200 subjective years of R&D help enough, for the same reason I don’t think it would be enough for US weather forecasting to have 100-200 years to look at and build models of UK weather data in order to predict US weather well enough to make money in our crop futures markets. Training on UK data would definitely help more than zero at predicting US weather, but “more than zero” is not the bar.
Similarly, 200 years of improvements to biological simulations would help more than zero with predicting the behavior of engineered biosystems, but that’s not the bar. The bar is “build a functional general purpose biorobot more quickly and cheaply than the boring robotics/integration with world economy path”. I don’t think human civilization minus AI is on track to be able to do that in the next 200 years.
Similarly, 200 years of improvements to biological simulations would help more than zero with predicting the behavior of engineered biosystems, but that’s not the bar. The bar is “build a functional general purpose biorobot more quickly and cheaply than the boring robotics/integration with world economy path”. I don’t think human civilization minus AI is on track to be able to do that in the next 200 years.
I don’t think it’s on track to do so, but this is mostly because of the coming population decline meaning regression in tech is very likely.
If I instead assumed that the human population would expand in a similar manner to the AI population, and was willing to rewrite/ignore regulations, I’d put a >90% chance that we could build bio-robots more quickly and cheaply than the boring robotics path in 100-200 years.
This is something I want to poke at a bit, because it seems like a pretty core disagreement.
In a completely different domain, do you expect something like DGMR (DeepMind’s precipitation nowcasting ML thingy., basically a GAN over weather radar maps) would work better than non-ML weather models to predict US weather after being trained only on UK weather? I expect not, and I don’t expect the reason it wouldn’t work is anything like “the ML engineers weren’t clever enough”.
The bio simulations need to be more clever than ML on bio data, they need to incorporate feedback from simulations of more basic/fundamental/general principles of chemistry and physics. Making this possible is what the 100-200 subjective years of R&D are for.
I’m not confident 100-200 subjective years of R&D help enough, for the same reason I don’t think it would be enough for US weather forecasting to have 100-200 years to look at and build models of UK weather data in order to predict US weather well enough to make money in our crop futures markets. Training on UK data would definitely help more than zero at predicting US weather, but “more than zero” is not the bar.
Similarly, 200 years of improvements to biological simulations would help more than zero with predicting the behavior of engineered biosystems, but that’s not the bar. The bar is “build a functional general purpose biorobot more quickly and cheaply than the boring robotics/integration with world economy path”. I don’t think human civilization minus AI is on track to be able to do that in the next 200 years.
I don’t think it’s on track to do so, but this is mostly because of the coming population decline meaning regression in tech is very likely.
If I instead assumed that the human population would expand in a similar manner to the AI population, and was willing to rewrite/ignore regulations, I’d put a >90% chance that we could build bio-robots more quickly and cheaply than the boring robotics path in 100-200 years.