> But the "known laws" are too convolved with chaotic behavior, which is why making predictions from them is really hard. This canard always gets thrown out bu

> But the "known laws" are too convolved with chaotic behavior, which is why making predictions from them is really hard. This canard always gets thrown out but the application here is flawed in two very big ways. First, "chaotic behavior" does not mean "unpredictable behavior." Modern numerical weather forecasting already deals with this through ensemble modeling techniques and other approaches designed to capture the statistics of the evolution of the weather, not just a single deterministic state. Anyone selling you a deterministic weather forecast from a single model is robbing you. Second, DL will suffer the same challenges here because many AI-based weather forecasting tools are auto-regressive, where a model output is used to seed the next step of the forecast. So the AI approach doesn't actually escape this hypothetical limitation (in fact it might compound it badly).