Pretty much the only thing I think AI could be useful for - forecasting the weather based off tracking massive amounts of data. I look forward to seeing how this particular field of study is improved.
Bonus points, AI weather modeling, for once, saves energy relative to physics models. Pair it with some sort of light weight physical model to keep the hallucinations at bay, and you’ve got a good combo.
What they leave off is how much goes into training the model, but I imagine once they settle on a trained model it can carry on pretty efficiently for a long time, especially if they’re baking in things like atmospheric CO2 levels to help keep forecasts in line with global warming.
Absolutely, but training is only once, being so efficient to make the actual forecast, you could have a forecast personally made for your own garden, which may be very different than a generic one covering hundreds of km². Then the about 90% accuracy will feel WAY more accurate.
I feel this personally, I live in the hills outside of a valley metro. All weather data is forecasted off of valley sensors, but shit gets weird when you suddenly climb 2000+ ft.
The best weather services in my area are those that can factor in peoples household meters into their forecasting, but those services still aren’t perfect.
I live in a hilly county in a country at the intersection of two weather cells, with a warm ocean current bathing our coast. Prediction in those conditions is a real challenge. For example, my neighbors 50 metres from me get consistently more snow and ice than I do. More stations would really help, but moving from there to crowd-sourced forecasting has issues due to lack of calibration and other biases. It can help, but not as much as you might think.
The non-AI models in use now all get feedback on each run from actual observations, that’s used to correct model parameters for later runs.
I’m sure the model would need to be continuously updated to take in more recent weather data.
Inputting newer weather condition data is different than changing the model. The model is the machine that does the computing, the weather data is just inputting variables. As an analogy it’s like a computer - the hardware itself doesn’t change, but if you do different clicks and typing input then the computer will output different things on screen. The ai model itself only changes when you train it differently.
There’s a difference between the real-ish-time weather data continuously fed in to output predictions, and the decades of weather data used to build the model. The continuous feed of data is more than likely part of what Google alleges is saving significant energy.
Its the training on decades of information, and occasional updates to those trained models that take a significant amount of resources, but hopefully for relatively short bursts.