https://doi.org/10.1053/j.ajkd.2016.02.058

Personally I think this resource mismatch can help drive creative choice of research problems that don’t require massive resources. To misquote Feynman, there’s plenty of room at the bottom

My usual workflow is find the person on google scholar, find their uni/lab homepage, and hope they published their email there.

How many Taiwanese, German, Indian, French, South Korean, etc scientists are working in the US? The ones working at NIST are facing being pushed out at the end of September.

I can understand a clearly communicated need for additional security requirements. But NIST operates almost totally in open science mode, with the main exceptions of being industry cooperative agreements. I don’t think this move to shed international researchers by reneging on commitments from the lab has been at all justified from a security standpoint.

Power consumption and emissions are already increasing, and any regulatory changes in 2025 are not factored in to discussion of those numbers. It’s more interesting to discuss what these changes mean when they are a factor in 2026 and on.

I think a lot of the issues are the same. Like you might expect the model to go off the rails if you venture away from the bulk of the training distribution. Or maybe the b most effective way to use it creatively is in some kind of interactive workflow revising specific chunks of the project instead of vibe-coding/composing from whole cloth.

https://arxiv.org/abs/2410.01201

This is in a conventional HPC environment, and I’ve found it way better than conda since the dependency solves are so much faster and I no longer experience PyTorch silently getting downgraded to cpu version of I install a new library. Maybe I’ve been using conda poorly though?

By all means we should discuss the transparency of this process, what those national priorities are, and exactly what we (collectively as taxpayers) the risk-reward tradeoff should be. But let’s not pretend that the funding agencies don’t already view science as a public investment, or be too hasty about dismissing the potential medium term economic value of research into for example geology and geochemistry on mars

The disconnect here is that the cost of iteration is low and it’s relatively easy to verify the quality of a generated C program (does the compiler issue warnings or errors? Does it pass a test suite?) or a recipe (basic experience is probably enough to tell if an ingredient sends out of place or proportions are wildly off)

In science, verifying a prediction is often super difficult and/or expensive because at prediction time we’re trying to shortcut around an expensive or intractable measurement or simulation. Unreliable models can really change the tradeoff point of whether AI accelerates science or just massively inflated the burn rate

They have some interesting analysis of the elastic deformation that happens during the rolling process (as opposed to the ball just falling or sliding). Turns out it’s pretty sensitive to the elastic constant of both the ball and the wall