Comment by margalabargala
5 days ago
I am dearly hoping that they are using the current "AI" craze to talk up the machine learning methods they have presumably been using for a decade at this point, and not that they have actually integrated an LLM into a weather model.
Graphcast (the model this is based on) has been validated in weather models for a while[1]. It uses transformers, much like LLMs. Transformers are really impressive at modeling a variety of things and have become very common throughout a lot of ML models, there's no reason to besmirch these methods as "integrating an LLM into a weather model"
[1] https://github.com/google-deepmind/graphcast
A lot of shiny new "AI" features being shipped are language models being placed where they don't belong. It's reasonable to be skeptical here, not just because of the AI label, but especially for the troubled history of neural-network based ML methods for weather prediction.
Even before LLMs got big, a lot of machine learning research being published were models which underperformed SOTA (which was the case for weather modeling for a long time!) or models which are far far larger than they need to be (e.g. this [1] Nature paper using 'deep learning' for aftershock prediction being bested by this [2] Nature paper using one neuron.
[1] https://www.nature.com/articles/s41586-018-0438-y
[2] https://www.nature.com/articles/s41586-019-1582-8
Not all transformers are LLMs.
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It’s not an LLM, but it is genAI. It’s based on the same idea of predict-the-next-thing, but instead of predicting words it predicts the next state of the atmosphere from the current state.
It is in fact one of the least generalized forms of "AI" out there. A model focused solely on predicting weather.
"gen" stands for "generative". If you read the GenCast paper they call it a generative AI - IIRC it's an autoregressive GNN plus a diffusion model.
Which is surprising to me because I didn't think it would work for this; they're bad at estimating uncertainty for instance.
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You're absolutely right! That was a category 5. Thanks for pointing that out.
The GraphCast paper says "GraphCast is implemented using GNNs" without explaining that the acronym stands for Graph Neural Networks. It contrasts GNNs to the " convolutional neural network (CNN)" and "graph attention network." (GAN?) It doesn't really explain the difference between GAN and a GNN. I think LLMs are GANs. So no, it's not an LLM in a weather model, but it's very similar to an LLM in terms of how it is trained.
> I think LLMs are GANs.
They aren't, but both of them are transformer models.
nb GAN usually means something else (Generative Adversarial Network).
I used GAN to mean graph attention network in my comment, which is how the GraphCast paper defines transformers. https://arxiv.org/pdf/2212.12794
I was looking at this part in particular:
> And while Transformers [48] can also compute arbitrarily long-range computations, they do not scale well with very large inputs (e.g., the 1 million-plus grid points in GraphCast’s global inputs) because of the quadratic memory complexity induced by computing all-to-all interactions. Contemporary extensions of Transformers often sparsify possible interactions to reduce the complexity, which in effect makes them analogous to GNNs (e.g., graph attention networks [49]).
Which kind of makes a soup of the whole thing and suggests that LLMs/Graph Attention Networks are "extensions to transformers" and not exactly transformers themselves.
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Hopefully they weren’t all forced out this year. The NOAA had massive cuts.
NCAR is being dismantled as we speak.
I suspect the names of those perpetrating this kind of destruction will become synonymous with ignorance and intellectual cowardice.
Same. I hope this was written by hardened greybeards who have dedicated their lives to weather prediction and atmospheric modeling, and have "weathered" a few funding cycles.
inb4 it’s actually an intern maintaining a 3000+ line markdown file
I can see it now