[minimaxir]

When I saw "Science" I didn't think they meant Data Science, which is what the UIs full of pandas code and plots imply. Even if the focus is on the sciences, I suspect that's the less valuable part of the announcement particularly with the implication of Jupyter Notebook 2.0.

Image-understanding for data viz is a use case that has been ignored, and modern LLMs are getting better at proper EDA. But, uh, I may need to update my resume.

[ritzaco]

A lot of the soft and hard sciences use hacky matplotlib code to produce results and visualisation, without being necessarily data science

From the bits I've seen, I'd take claude-generated code any time over that written by maths, physics, biology, linguistics people. Even though I've seen Claude make some super-big mistakes while doing data analysis I'd guess it's already more reliable than most academics trying to code.

[beardedwizard]

This 100000x over. Nothing is worse than trying to productionize code coming from academics like this.

[eli_gottlieb]

Matplotlib? Ha! There are loads of academic fields where you still write data analyses by hand, one at a time, in Matlab, without proper version-control or libraries.

[__MatrixMan__]

Conveniently, you can use published results as tests of equivalence, provide the ugly code as context, and regenerate it to your liking. I think the odds of such a regeneration introducing a bug that's within the usage domain but that dodges the golden tests are quite low... so long as you resist the urge to add features along the way.

[imperor]

I think presentation via software just isn't a lot of their strong suits. A lot of researchers' personal or research lab sites too are usually way out of date or just really badly presented from what I've seen. They could all do with some thinking about aesthetics and understandability more.

[__MatrixMan__]

My take based on the video is that they're thinking more about bioinformatics, which might technically fall under the "data science" umbrella depending how you define your terms, but which is not described that way in common usage.

It's the content that determines the sort of science, not the toolchain.

[throwaway219450]

It's not obvious from the marketing if this is applicable to non-biosciences. If it is, they couldn't come up with a single example from another domain like astrophysics?

https://www.anthropic.com/news/claude-science-ai-workbench

EDIT: Installed the app, it has zero connectors for non biology, which is a shame. I assume they'll come later.

[winwang]

Honestly quite excited to see what can happen here, I think biology has generally had a lack of data science expertise.

[inciampati]

Tell us, what gives you that impression?

[__MatrixMan__]

I don't hold that view exactly. But something related...

I once tried to replicate a bioinformatics result based on published data (for a class). I found that although the process did indeed yield plots A and B, as the authors claimed, they were typeset wrong in the PDF so plot A had B's caption and plot B had A's caption.

It would be an easy thing to provide assurances against, if you wanted to. You could repeatably build the pdf so that such a mistake was in plain view, as a bug in the pipeline, rather than something you had to do offline calculations to support or reject.

The situation as it is is not ideal. Instead of anything that would verify either side, it's my word against the author's until a third party bothers to repeat the analysis. That's the best we can do for scientific claims, but there are friendlier ways to make the computational claims verifiable.

The Claude science video showed a little "provenance" button and talked about exactly this. Life sciences have their hands full with the actual science. They're not immature, but they are not in a great position to be proving the validity of the computational connective tissue that underlies their results. That's a whole thing on its own, independent of the underlying scientific reasoning being presented (though I wouldn't call it data science).

Plus, its exactly the sort of thing we need AI to get better at: sourcing evidence that proves its claims and stitching it together so the proof is easily verifiable.

I too am excited.

[imperor]

They do mention things like protein and chemical structure visualization though

[quijoteuniv]

All of these new things are starting to look like soviet space program propaganda. Is there something really new?

[dennis_jeeves2]

Old wine, new bottle...