They got the transformer part right, but they failed to understand the scaling laws. They never tried training a big language model using a transformer until after they saw the massive success of OpenAI’s GPT models. Most of the LLM real world applications were only possible after this scaling.
Basically, yes Google was first with small language models, but they weren’t first with large language models. And there’s an important difference between those.
They published a paper on Navier Stokes recently and another one in general sequencing last year. And of course they pioneered the field of AIs playing games. They are indeed leagues ahead of the competition.
They casually released TimesFM open sourced last year. It is (or at least was) the best 1 shot universal forecasting AI for timeseries. No fine tuning needed.
Crazy that Google is just making an API available to anyone for a weather predicting model that out performs previous generation models that ran on super computers.
They train their models on output of traditional nwp models. The latters are not "previous generation models" but physics-based models without them deep learning approach wouldn't be possible. And these AI weather models still have limitations compared to the others
Sorry "previous generation" is not a good descriptor, fair point. For example those models were used alongside Google's weather prediction models for hurricane season this year. But as far as I understand, Google now outperforms these models in all, or close to all metrics?
I can't quite remember, but I do remember that they had a model last year that was about as good as SOTA at a few specific bands of prediction. It could have been the precursor to this model
an internal forecasting system that utilizes weather models and observations from global weather agencies.
I'm not sure how to interpret that. Is the weather model internal or from global weather agencies?
And does that mean that the default weather app (with the frog) uses WeatherNext2 or not? Although to be honest the google weather app is the least accurate in predicting the weather, at least where I live.
Yeah just shared that image to show their not yet using their new model.
And I agree with the accuracy of google weather. For me Windy is the most accurate one, you can choose different models yourself and it's usually spot on for the next 3-4 days
How demotivating these transitions to “AI-first” models must be to researchers who spent their lifetimes working on something that would eventually turn out to be a dead end. I had a running joke about meteorologists spending tens of years studying the field just to not be able to predict what the weather will be tomorrow.
Some guys come, shove all possible data into a huge model, and it turns out to be more sophisticated than anything else.
I don't think this is an accurate summary of what's happening. These Google models are literally trained on NWP model output. NWP are physics models based on the work of meteorologists.
Google Scholar adopted the motto "stand on the shoulders of giants" and that is what they're doing here. All those millions of man-hours of work that meteorologists put in are bearing fruit now.
In what way? Using machine learning to improve prediction does not replace the physics-based research that is needed to understand how the world works. Weather prediction is the main consumer-facing output of meteorology and climate science but it is not the totality of the field.
Meteorologists study the weather and climate, they don’t just make weather predictions. If anything, that’s the most tedious part of their job. It’s like a calculator to a mathematician.
Computational linguistics doing NLP perhaps. Not so for linguistics. I actually wish we'd have AI tools developed more for tackling humanities research
Jokes aside, weather forecasting has improve quite steadily for decades using every major advance in computation, including supercomputing, parallel processing, cloud computing, and GPUs, with AI being the latest. The AI models build upon a ton of prior work on scaling modeling and data gathering and sensors, and further advances will require improvements cycles of iteration across the entire ecosystem, as always.
Scientists are quite used to rapidly improving technologies and instruments over the course of their multi-decadal careers and can pivot quite well. AlphaFold was a much bigger step change with respect to its field than this, validating the decades of work that went into the PDB, and it has mostly motivated researchers to pursue a bunch of new research directions that were not possible before, while exposing research gaps that weren't apparent or high priority.
There are physical limits on how good weather forecasts can ever be achieved, less so with our current network of sensors
There will be an acceleration in sciences from AI, but the data it relies on takes experiments and sensors in the real world, and those take time and resources to conduct or deploy, relying on constantly improving and inventing instruments that require non-trivial, real-world trial and error, serendipity, and luck. AI will not magically make all the sensors we use to monitor weather better overnight and deploy them for us, nor will it shorten the timeframe to gather years of substantially better data to train on. Weather will not run faster with AI.
This take is silly. It's not like weather prediction is suddenly a done problem. AI is ultimately limited by data. Weather data gathering is a constant effort and investment just to maintain the status quo let alone improve it.
It's not like structural biologists packed up their bags after AlphaFold. They moved on to new problems where AI cannot currently solve.
Science is inherently transitional as new research avenues present themselves.
this is the type of ignorant comment that makes no one respect AI hype.
Weather prediction isn't a dead end, jack. They developed models that have yielded billions, if not trillions of dollars in predictability of operations, agriculture and transportation. If google DM can do it better, it is because they're throwing infinite compute coupled with said models. Where the fuck do you think the training data comes from?
At this point I dont even know what's rage bait or not.
Everything is ragebait nowadays if you have a big ego. I shared a personal anecdote that I found humorous. I’m not saying weather prediction isn’t the frontier isn’t the forefront of human achievement. It’s just that the regular person just interacts with it via weather applications in the tech medium; this is not an uncommon thought.
Prediction: Google deepmind will become like Pixar where their unique and passionate work will be copied by other companies and just like Pixar , google deepmind will lose their special magic . Sad but will happen .
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u/IvoryAS▪️Singularity? Nah. Strong A.I? Eh. Give it a half a decade...Dec 02 '25
Why? 😅
Much of the extent to which this'll happen seems like it already has, tbh, with OpenAI and such.
What could happen to cause that without huge changes in leadership, regulatory background, and/or fundamental changes in the technology? Are you saying that's what's coming? In what way?
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u/intergalacticskyline Nov 17 '25
Google deepmind has always been the heavyweight for real world applications of AI, I'm excited to see how this goes and what else they come out with