jalman

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.

You've brought a good point. The exact difference between ML and AI is in the focus. ML is focusing on tossing data. Its main success metric is "miles traveled". AI is focusing on producing sense. Its main success metric is "value delivered". Therefore ML is tending to ship empty containers, aka "research to be done by scientists who couldn't find a liver cancer". Adding AI perspective into the mix could actually turn the things around.

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 The research will not be published.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.

The industry does not distract from harm to shake the followers off the tail. Whoever comes next will have to bear huge costs getting over the insane regulatory requirements. The more politicians are involved in the process, the more secure are initial investments.

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.

It could be just the opposite. One of the cheapest ways to improve alignment would be to re-run the models iteratively. The AI was likely looking for precision in the aforementioned experiment. Precision in inference is a correlate for aligned inference. https://doi.org/10.22541/au.172116310.02818938/v1