Sounds like a very narrow view on what constitutes science. There are many fields of science where there is existing data against which new ideas can be tested without additional 'real-world' measurements. Newton's theory of gravitation relied entirely on pre-existing astronomical data for which there was no existing unifying theory. He made progress by putting forward a theory which explained that data. Now you can argue that it's not really science unless you include the original data collection and subsequent real-world measurement validation steps. But I'd be comfortable saying that Newton was indeed a scientists and did make progress in science despite only doing what some might say is the 'middle' part of the process. There are plenty of modern analogs where work like this sits out there waiting to be done using existing data.

I suggest you to give a look to the MCP protocol for hardware that is being proposed by Anthropic. The hardware will be the next harness of LLMs, they will be able to operate machines to reinforce their theories.

I still think that a major problem is that biological processes are not “fast” as coding, but they are verifiable. If during post processing we are able to give enough harness to test and verify this kind of environment (maybe via simulation and real data) we will for sure achieve incredible performance also in this domain.

Great news, then! TFA: "Last week, we previewed the Model Hardware Standard, which allows Claude to directly and safely operate laboratory equipment."

When I looked at “Claude Science” which is a beta, separate desktop app, I came away with the impression that it was mostly for biology and a bit of chemistry - presumably there’s some value it can get from consulting obscure literature and uniting disparate threads of already-known stuff, but since I don’t work in either field I can’t speak much more to it.

> As someone working in science, this belief confuses me. How (by what means) do you think Fable 5.1 will be able to make further progress in scientific domains?

The same way it did in the previous versions: brute force.

I don't believe that LLMs have any particular intelligence we don't, but there's an endless list of problems we either don't have bodies to throw at, or the bodies we can throw at it, don't have such a huge large context to crunch problems.

What LLMs will always intrinsically fail at is showing us genuine new intuitions. The technology is about predicting the next plausible token/sentence.

They will not revolutionize human knowledge, but they can definitely widen it a lot.