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DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

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Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#81

Post by amarcheschi »

tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

One of my professors is referenced in the Wikipedia page of graph neural networks. It's funny that he explained them in the worse way possible and I eventually understood them better with another professor

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#84

Post by hammock »

tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

Is there any website publishing these forecasts? I imagine NWS/NOAA isn’t doing anything different yet on their public websites.

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#85

Post by KennyBlanken »

tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

Everything in the western world isn't focused on LLM. The top western players are heavily focused on AGI.

Meanwhile the Chinese are using LLMs and other non-AGI AI tech at the edge wherever they think to put it for task-specific productivity or optimization. They don't really care about AGI, or more accurately: they're working on getting their society more efficient and decarbonized, and then they'll be free to work on AGI with far fewer resources.

OpenAI, Anthropic, et al are working toward someday having AGI, and if they ever do, when they do, the Chinese will be hopelessly far ahead of us on energy, manufacturing, logistics (especially low/zero carbon transport of goods and people) and so on.

Once the Chinese figure out how to train an AI for ULEV lithography, especially once they figure out how to train it for semiconductor design or validation - it's game over for the semiconductor industry, and the big AI players will follow, because they won't possibly be able to compete against a Chinese version of NVIDIA with TSMC-like capabilities, or Chinese AI companies running on those much cheaper chips, with cheap, zero carbon power.

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#86

Post by testfoobar »

numbers_guy wrote: post #68
tcumulus wrote: post #32

Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The o…

I can assure you that anyone who touches numerical simulations of any kind (physicists, engineers, chemists, biophysicts...etc) has tried their hand at ML based surrogate models in the last 5 years, so it's not like they aren't being tested. From my experience, they aren't very robust. Weather modeling is actually one of the very few areas where it seems to work half decently.

Why does it work for weather at all? Is there something that the mathematical models are over-simulating? Is weather easier to predict than we thought? Just curious what the intuition is to regarding the success of ML weather modelling...

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#87

Post by hammock »

esperent wrote: post #67
baq wrote: post #59

Earlier quoted context omitted.

You can board up a helluva lot more stuff in a week than in two days. Crucially, you can move more of the most expensive stuff out of the storm surge zone, which is where the biggest damage happens and try to flood proof more of the things which can’t be moved.

> You can board up a helluva lot more stuff in a week than in two days TFA says they might be able to predict one extra day ahead (three days instead of two). No prediction system will ever give you a week's notice on a typhoon.

Why couldn’t we get a week?

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#88

Post by rcpt »

xyzzy123 wrote: post #27
trescenzi wrote: post #17

Earlier quoted context omitted.

Ironically typhoon forecasting, at this moment, is more valuable. These predictions are matters of life, death, and billions of dollars in damage.

I know this is uncharitable and I am wrong but I am having trouble coming up with concrete scenarios where you die with 2 days notice but survive with 3. I am nonethless a believer that more accurate forecasting has value.

Hurricane blast radius is very small

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#89

Post by vickychijwani »

HardCodedBias wrote: post #69

And this is why GDM has to go. It's crazy that when Google is struggling so badly that efforts like this that have no path to revenue at all were funded. GDM management really thought that they were some kind of charity. UNREAL.

Are you being sarcastic? Even if “Google is struggling so badly” (which it really is not - the narrative will flip again at some point), these efforts will have a lasting impact on the world. Not everything good is about bringing in revenue.

Re: DeepMind's WeatherNext model achieves breakthrough forecasting cyclones

#90

Post by ObscureScience »

It would be interesting to research more into parameter analysis to learn more about what parameters a trained network has "extracted" that has the strongest predictive power; assuming the model itself has such preductive power. I guess I'm imagining some kind of alternative to embeddings where you get answers like: Predicted percipitation P(accumulated humidity over n days, net wind vector over n days, humity today, temperature today, ...)
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