funnygiraffe

Maybe this? https://news.ycombinator.com/item?id=46589279

I wonder how much chance of broader adoption Mojo has now when seemingly everyone has succumbed to that LLM laziness.

Not sure this is fully accurate. Managing kids can use up one's will power or self control resources considerably. There can also be sleep deprivation with young children and other energy drain. I'm not really sure but my focus / ability to avoid distractions at work may actually have gotten worse since having kids.

Exactly this! When I had my first two kids only a year apart and early in my career, I thought I'm giving something up because I could no longer invest as much time and effort into my projects and research ideas as before. But in retrospect, looking at how the field has evolved since etc., those ideas, which I had regretted I couldn't push harder, anyway had only a small chance for some grand success (beyond the low-medium success of the low-medium effort versions I managed to put together).

For that particular issue you can improve your "gambling" odds by having kids at a younger age, rather than waiting until you've optimized your career and environment.

Why aren't those types of clinical trials, enabling faster iteration speed, moved to countries with less strict regulation?

What about the North Korean escapees who claim that, out of hunger, they have been extracting and (re-)eating corn kernels out of their own poop (implying there must be more than 2 for the effort to be worth it) - are they full of shit?

"226 of 521 FDA-approved medical devices, or approximately 43%, lacked published clinical validation data."

The lack of "published" clinical validation studies implies neither that the AI developer performed no clinical validation nor that the FDA hasn't seen it. So, it is not clear if the problem is with the lack of clinical validation or the lack of reporting. For some reason the title exaggerates yet further (half of FDA-approved AI not "trained" on real patient data).

I was under the impression that with LLMs, in order to get high-quality answers, it's always best to keep context short. Is that not the case anymore? Does Claude under this usage paradigm not struggle with very long contexts in ways as for example described in the "lost in the middle" paper (https://arxiv.org/abs/2307.03172)?

Why are we so sure that a lot of "previously intractable problems" are/will be solved with this family of methods? (and I mean real-life/real-world problems, not toy problems constructed specifically to show the proposed methods in the best light possible in the research papers) Of course others above have pointed out drug or protein design as a potential area, but there still seems uncertainty as to the practical impact on the real world. Other than that, I don't see areas of impact for these approaches so far.

Thanks. That's certainly very interesting. Albeit it seems to me that the number of jobs doing geometric and topological ML/AI work in the drug or protein design space would be quite limited, because any discovery ultimately has to be validated through a wet lab process (or perhaps phase 1-3 clinical trials for drugs) which is expensive and time-consuming. However, I'm very uninformed and perhaps there is indeed a sizable job market here.

Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success on that plan.

Are you also currently working on regulations in the medical and healthcare spaces?