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.
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.
Yes, that is not in contention. Not all transformers are LLMs, not all neural networks are transformers, not all machine learning methods are neural networks, not all statistical methods are machine learning.
I'm not saying this is an LLM, margalabargala is not saying this is an LLM. They only said they hoped that they did not integrate an LLM into the weather model, which is a reasonable and informed concern to have.
Sigmar is correctly pointing out that they're using a transformer model, and that transformers are effective for modeling things other than language. (And, implicitly, that this _isn't_ adding a step where they ask ChatGPT to vibe check the forecast.)
“I hope these experts who have worked in the field for years didn’t do something stupid that I imagine a novice would do” is a reasonable concern?
A simple explanation would be: orders from the top to integrate an LLM. The people at the top often aren't experts who have worked in the field for years.
Yes, it is a very reasonable concern.
The quoted NOAA Administrator, Neil Jacobs, published at least one falsified report during the first Trump administration to save face for Trump after he claimed Hurricane Dorian would hit Alabama.
It's about as stupid as replacing magnetic storage tapes with SSDs or HDDs, or using a commercial messaging app for war communications and adding a journalist to it.
It's about as stupid as using .unwrap() in production software impacting billions, or releasing a buggy and poorly-performing UX overhaul, or deploying a kernel-level antivirus update to every endpoint at once without a rolling release.
But especially, it's about as stupid as putting a language model into a keyboard, or an LLM in place of search results, or an LLM to mediate deals and sales in a storefront, or an LLM in a $700 box that is supported for less than a year.
Sometimes, people make stupid decisions even when they have fancy titles, and we've seen myriad LLMs inserted where they don't belong. Some of these people make intentionally malicious decisions.
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.
> Which is surprising to me because I didn't think it would work for this; they're bad at estimating uncertainty for instance.
FGN (the model that is 'WeatherNext 2'), FourCastNet 3 (NVIDIA's offering), and AIFS-CRPS (the model from ECMWF) have all moved to train on whole ensembles, using a cumulative ranked probability score (CRPS) loss function. Minimizing the CRPS minimizes the integrated square differences of the cumulative density function between the prediction and truth, so it's effectively teaching the model to have uncertainty proportional to its expected error.
GenCast is a more classic diffusion-based model trained on a mean-squared-error-type loss function, much like any of the image diffusion models. Nonetheless it performed well.
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.
Oh yeah, GNN (graph neural network) is the common term, "graph attention network" is pretty confusing because a GAN is a totally different architecture.
(Well, not necessarily architecture. Training method?)
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