The AI Scientist: Towards Automated Open-Ended Scientific Discovery

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.

Any theory compresses experimental data (a mountain of data) into a palatable bit (pocketable item) of knowledge. One can go without theory just by stacking the original measured ratios dA -> dB. The Solutions, that are generated upon raw data, would likely produce less waste, as they fit the exact phenomena. Generalizations do emphasize the main effects and level out the minor effects, introducing an error.

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. The research will not be published. It will remain a trade secret.

What you are complaining about is a brocken labor division. 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. A "$15-scientist" will kill the no-output individuals, who are just a sophisticated flavor of bureaucracy. A science bureaucrat is a bureaucrat with elevated rights and virtually no responsibility.

great point. I think the AI scientist is already a winner. If the likelihood of false outcome is FNR+FPR, then machine would fail 0.7 and humans 0.69 times. Humans do win nominally. In terms of costs humans loose. For every FPR 0.31-0.17 = 0.14 you spend additionally, you'd gain FNR 0.52-0.39 = 0.13. The paper production costs discrepancy is at least factor 100. The value of the least useful research typically drives factor two or more benefit in comparison to production and validation costs. So the final balance is 0.014 to 0.36 -> x25 gain in favor of AI.

What you describe is quite different from what the original post describes.

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.

The specific mechanism of action here needs a drill-down. Did "The AI Scientist" (ugh) generate a patch to its code and prompt a user to apply it, as the screenshots would seem to indicate? If so I don't find this worrying at all: people write all kinds of stupid code all the time--often, impressively, without any help from "AI"! ;)

Did it apply the patch itself, then reset the session to whatever extent necessary to have the new code take effect? TBH I'm not really worried about this either, as long as the execution environment doesn't grant it the ability to bring unlimited additional hardware to bear. Even in that case, presumably some human would be paying attention to the AWS bills, or the moral equivalent.