121–130 of 199 posts
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Well LLMs are largely useless and people are realizing that.
Raw dog Chat LLMs are pretty worthless. But run an agent with tool invocation and they get scary good. It's amazing how much reasoning is packed into the English language. Provide your model with enough information and it can pull some miracles out of thin air. It's not the "Replace humans" level yet, but you can automate a lot of stuff you wouldn't expect to be able to automate.
Before LLMs the tech groups I followed were ripping with discussions about this and that topic, what to use and when; I believe these discussions sparked the creation of many frameworks and tools out of "this seems like a good idea, wouldn't hurt to implement it". Unfortunately it all resolves around LLMs nowadays and how to make some LLM work some way or another, we don't even discuss the very topics the groups were…
The ROIC is not gonna look good if they do not somehow make use of the existing assets...
Not an argument for btw, Im just saying. Ultimately the management answer to shareholders who look at return measures such as that.
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…
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…
Earlier quoted context omitted.
Raw dog Chat LLMs are pretty worthless. But run an agent with tool invocation and they get scary good. It's amazing how much reasoning is packed into the English language. Provide your model with enough information and it can pull some miracles out of thin air. It's not the "Replace humans" level yet, but you can automate a lot of stuff you wouldn't expect to be able to automate.
What you are saying, if I follow, is that LLMs basically worthless: it turns out that coding is so simple that verifiable rewards can tune weights surprisingly well for that one peculiar task. ('agentic' is fancy word for letting them run what they write - not to put too fine a point on it.) You've made the most damning remark against Planet LLM I've read.
Should be called Claude-bio-big-bucks. What about earth science, physics, engineering? The connectors and skills are all just biology and pharma. Boo
NSF annual budget (pre-Trump): ~$6-8 billion
NIH annual budget (pre-Trump) ~$50 billion
There it is.
Tried this to see how it goes in my particular field - computational design of RNAi-based biopesticides. One-shotted a design for targeting the DvSnf7 transcript of western corn rootworm. It took a fairly naive approach (maybe how a 1st year PhD student would go about it), but got the job done. Also noted caveats with its approach (e.g. using mammalian design rules, limited off-target screening). Not bad really. But…
If you think you can use this to land real positive impact, you, your institution, or your company should apply for OpenAI and Anthropic's bio programs!
Back then, we had data repositories, databases, Jupyter Notebooks, Slurm batches, open computing platforms, and so on. It could do similar things ---- just by hand.
While adding an LLM agent can indeed drastically improve usability, it must be a massive headache for system administrators. It honestly sounds like introducing a huge, uncontrollable wildcard into the system.
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Honestly quite excited to see what can happen here, I think biology has generally had a lack of data science expertise.
Tell us, what gives you that impression?
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.
I like how this implies parsing PDFs is as hard as like protein folding