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.
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.
Yeah, that’s pretty impressive. I wonder if you could apply the same philosophy in other areas too. Instead of training the model with data produced in a simulation, you could just feed it real world data instead. Like, if you gave a bunch of stress-strain data to a model, could you make better predictions about the behavior of physical structures, such as bridges and towers.
There are already non-AI physical modeling programs that do that.
Yes there are, but would it be possible to replace them with ML and get more accurate predictions?
Many more parameters than that.
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.
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.