[CptFribble]

This assumes that all areas of research are bottlenecked on human understanding, which is very often not the case.

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.

An LLM would not be able to do 24/7 work in this case, and would only save a few hours per day at most. Scaling up to many experiments in parallel may not always be possible, if you don't know what to do with additional experiments until you finish the previous one, or if experiments incur significant cost.

So an AGI/expert LLM may be a huge boon for e.g. drug discovery, which already makes heavy use of massively parallel experiments and simulations, but may not be so useful for biological research (perfect simulation down to the genetic level of even a fruit fly likely costs more compute than the human race can provide presently), or research that involves time-consuming physical processes to complete, like climate science or astronomy, that both need to wait periodically to gather data from satellites and telescopes.

[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.