If it’s the same quality as other Google AI products, it surely will be great…
Hi, it’s me, the Google weather AI. Today will be couldy with a 69% chance of meatballs because I saw that once
You should drink some window cleaner and eat some sunblock lotion, because there will be sun.
and will be retiered between the next 12 and 18 months
Ummm was weather foresting not always done using probalistic interference models, i.e AI?
Weather forecasts have recently felt like they’ve been less accurate, i.e. you maybe get a good level of confidence for a day, but two days and it might be completely different. This makes sense given the climate is changing and previous models wont fit as well…
Are LLMs going to consume search data for raincoats and air-conditioning to improve the weather forecast. Clearly time to invest in AI now, the revolution is here!
Weather forecasting does create ensemble models to help constrain their forecasts. They’ll adjust some of their inputs in each model, mainly as a way of embedding the uncertainty in the measured data, then run that model and see if it changed.
This resembles AI on one level, but it’s at a dramatically different scale. An ensemble may contain a few hundred runs at most, but an AI needs tens of thousands of data points at minimum. In order to make predictions like what google is saying they can do, they’d need to train on billions or maybe trillions of data points.
This is still fundamentally different than ensemble modeling though. Ensembles are physically informed and the perturbations are based on real assumptions. Each model in an ensemble is based on validated physics equations. An AI model would undermine that completely. You can’t possibly describe the underlying equations because there aren’t any, so you can’t analyze its accuracy or propose a more accurate model, you’re just stuck with a bunch of coefficients that you’ll never understand.
I’ve worked in climate modeling, and this kind of AI work is nothing more than an electricity sink for at least a decade, maybe forever.
Because they’re both lazily just branded “AI”, people might conflate this with an LLM.
Obviously this isn’t an LLM implementation.
Not an LLM obviously & great work.
This is the best summary I could come up with:
Researchers from Google have built a new weather prediction model that combines machine learning with more conventional techniques, potentially yielding accurate forecasts at a fraction of the current cost.
The model, called NeuralGCM and described in a paper in Nature today, bridges a divide that’s grown among weather prediction experts in the last several years.
It then incorporates AI, which tends to do well where those larger models fall flat—typically for predictions on scales smaller than about 25 kilometers, like those dealing with cloud formations or regional microclimates (San Francisco’s fog, for example).
But the real promise of technology like this is not in better weather predictions for your local area, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research.
That means the best climate models are hamstrung by the high costs of computing power, which presents a real bottleneck to research.
While many of the AI skeptics in weather forecasting have been won over by recent developments, according to Hill, the fast pace is hard for the research community to keep up with.
The original article contains 773 words, the summary contains 188 words. Saved 76%. I’m a bot and I’m open source!
Oh man, Google looking for some major new revenue streams from the government who will want this most.
Uninstalling … ah , wait I don’t have it anyway
Not all AI is bad, this isn’t the “guuuuys… I swear, we just need 2 million more graphics cards and AI will be able to automate all your
wage slavesemployees away” type of AI, this can actually be kinda useful.
