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.
Yeah, I’ve long thought that weather forecasts are a perfect use case for AI. AI is great with complicated systems that are hard to model accurately but have lots of available data.
Current weather forecasts kinda suck. I try to schedule jobs around when it’s going to rain, and have to frequently reschedule because rain forecasts aren’t very accurate. I really hope we can see improvements.
what’s perhaps most striking about GenCast is that it requires significantly less computing power than traditional physics-based ensemble forecasts like ENS. According to Google, a single one of its TPU v5 tensor processing units can produce a 15-day GenCast forecast in eight minutes. By contrast, it can take a supercomputer with tens of thousands of processors hours to produce a physics-based forecast.
If true this is extremely impressive, but this is their own evaluation, so it may be biased.
About 4 years ago, this video showed that a ML model can be used to cut costs on physics simulations. It’s about time we did that with weather too.
It’s not just about cutting costs, but also improving accuracy. Physical simulations factor in a dozen or so weather conditions to predict outcomes. Machine learning can track thousands of conditions, drawing connections not realized in physical models, leading to much more accurate statistical models.
Physical simulations factor in a dozen or so weather conditions to predict outcomes.
Many more parameters than that.
Machine learning can track thousands of conditions
Scientists already know which ones are relevant. You’re not going find any big surprises there with an AI. Shotgun-style factor analysis has already been done to death. The price of baked beans doesn’t impact the wind direction in the Persian Gulf. It’s OK to not consider it.
drawing connections not realized in physical models
Again, it’s possible but unlikely. And you’d need an AI that could be queried to tell you what factors it considered, and most of them don’t work that way right now.
Statistical models don’t become more accurate because you throw irrelevant parameters at them. But that’s how ML systems work.
