I feel like the counterpoint here is that R&D is incredibly hard. In regular development, you have established methods of how to do things, established benchmarks of when things are going well, and a long period of testing to discover errors, flaws, and mistakes through trial and error.
In R&D, you’re trying to do things that nobody has ever done before, and simultaneously establish methods, benchmarks, and errors for that new method, which carries a ton of potential pitfalls. Also, nobody has ever done it before, so the AI is always inherently out-of-training to a much greater degree than in regular work.
Yes, this seems right, hard to know which effect will dominate. I’m guessing you could assemble pretty useful training data of past R&D breakthroughs which might help, but that will only get you so far.
I feel like the counterpoint here is that R&D is incredibly hard. In regular development, you have established methods of how to do things, established benchmarks of when things are going well, and a long period of testing to discover errors, flaws, and mistakes through trial and error.
In R&D, you’re trying to do things that nobody has ever done before, and simultaneously establish methods, benchmarks, and errors for that new method, which carries a ton of potential pitfalls. Also, nobody has ever done it before, so the AI is always inherently out-of-training to a much greater degree than in regular work.
Yes, this seems right, hard to know which effect will dominate. I’m guessing you could assemble pretty useful training data of past R&D breakthroughs which might help, but that will only get you so far.