idontknowmuch

5 ms·

1.
by idontknowmuch 7mo ago
Viruses are just another "mutagen". No different from UV causing DNA damage in your skin cells, other than the mechanism in which it occurs. The cause for cancer is well-known and, in hindisght, obvious, which is mutation. The cha
2.
by idontknowmuch 8mo ago
If you think these types of tools are going to be generating "the most and best research coming out of any lab", then I have to assume you aren't actively doing any sort of research. LLMs are undeniably great for interactive
3.
by idontknowmuch 8mo ago
As noted, I agree on the great strides made in the protein space. However, the over saturation and redundancy in tools and products in this space should make it pretty obvious that selling API calls and compute time for protein binding, ann
4.
by idontknowmuch 8mo ago
What tools are "actually working" as of a few years ago? Foundation models, LLMs, computer vision models? Lab automation software and hardware? If you look at the recent research on ML/AI applications in biology, the majority
5.
Annotated command-line interfaces in Python(github.com)
1 points by idontknowmuch 11mo ago|0 comments
6.
by idontknowmuch 2y ago
I don’t think the basic usage of references is hard to grok for a beginner. If you aren’t going to mutate data and only access it, then pass a reference. No need for over-complicated semantics when describing it to a new Rust user.
7.
by idontknowmuch 2y ago
That's completely understandable - leveraging the train set. But what I was trying to say is that the comparison is relative to models that were actually zero-shot and not tuned. It isn't apples to apples, it's apples to orch
8.
by idontknowmuch 2y ago
What's your opinion on the veracity of this benchmark - given o3 was fine-tuned and others were not? Can you give more details on how much data was used to fine-tune o3? It's hard to put this into perspective given this confounder
9.
by idontknowmuch 2y ago
Somewhat related — I’d love to hear your thoughts on dex-Lang and Haskell for array programming?
10.
by idontknowmuch 2y ago
Ahh that's an interesting paper I must of missed that one - thanks for the link. I think another paper that recently got a lot of hype has been the Matroyshka representation learning paper -- essentially training models with different
11.
by idontknowmuch 2y ago
Afaik, they aren't really trained independently -- for most models, e.g. DINO, etc., the smaller sizes are actually distilled from larger models. It's much easier to generate performant models at smaller size via distillation. And