[naasking]

> Imagine a field where experiments take days to complete, and reviewing the results and doing deep thought work to figure out the next experiment takes maybe an hour or two for an expert.

With automation, one AI can presumably do a whole lab's worth of parallel lab experiments. Not to mention, they'd be more adept at creating simulations that obviates the need for some types of experiments, or at least, reduces the likelihood of dead end experiments.

[goatlover]

Presumably ... the problem is this is an argument that has been made purely as a thought experiment. Same as gray goo or the paper clip argument. It assumes any real world hurdles to self improvement (or self-growth for gray goo and paper clipping the world) will be overcome by the AGI because it can self-improve. Which doesn't explain how it overcomes those hurdles in the real world. It's a circular presumption.

[tart-lemonade]

What fields do you expect these hyper-parallel experiments to take place in? Advanced robotics aren't cheap, so even if your AI has perfect simulations (which we're nowhere close to) it still needs to replicate experiments in the real world, which means relying on grad students who still need to eat and sleep.

[naasking]

Biochemistry is one plausible example. Deep Mind made hug strides in protein folding satisfying the simulation part, and in vitro experiments can be automated to a significant degree. Automation is never about eliminating all human labour, but how much of it you can eliminate.

[dkersten]

Only if it’s economically feasible. If it takes a city sized data center and five countries worth of energy, then… probably not going to happen.

There are too many unknowns to make any assertions about what will or won’t happen.