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by jalman
669 days ago
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The value of AI-mated research is results. This tool will aid engineering. It will offer a pinpointed research to resolve a particular issue at hand. It will close the verification loop naturally by proving that the research has indeed facilitated a useful result. Most of such useful research will never be publicly available. What you are complaining about is a legitimately brocken verification loop. Let's consider a case. An engineering department has a problem. It passes the problem to the research department. The research department slacks out, while produces junk. It makes tons of papers that are hard to prove, not to say apply. They drink champagne with other researchers, so they can publish the junk and defend the turf. AI-mated research will finish the racket and corruption. |
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Also I'd say that what you describe is quite different from what many people believe is the problem with research, either academic or industrial.
Academic research has a problem of low-quality outputs, but it's not necessarily "hard to apply" kind of problems. There is no "engineering department" that comes with problems to people in academia, and there is no inherent expectation of the research to be immediately applicable or applicable at all. A lot of high quality research is not immediately applicable and is not engineering-oriented, in fact. And this kind of research is still worth automatization, just like it is worth doing while we can't automatize it. There is of course a feedback between experiment and theory, but I would say it's the thing that's broken in the academia.
As for industrial research, which I guess you referred to in first place, I have less experience with it. But the goal of industrial research is not publishing papers, so it's not in their KPI, and they won't be drinking champaign for long if they are publishing junk papers instead of doing what they're hired for. Note that not any research done in industry is "industrial research" by this classification, and companies do academic research occasionally (Bell Labs or Google Brain for example). So if industrial research lab is created to solve engineering problems and doesn't do it properly, then, well, any sane company will fire them. It doesn't have wrong incentives problem like academic research, because its incentive is to make money for the parent company.
Now back to AI. Assuming theoretical research is easier for AI, the bottleneck is going to be human doing experiments. Obviously, AI output should be better than human output for it to be tested by experimentalists. What I was saying is that even worse, since the volume of AI output is higher, and the usual heuristics (name of author, personal discussions, etc.) won't work for filtering AI output, it should be better than high-quality human work and not just average human work. I'll be happy if this can be achieved, but we are very far from there.