> It’s not hard to imagine a neural net learning a more efficient way to encode and forecast the underlying physical patterns.
And that is where your understanding breaks down.
What makes weather prediction difficult is the same thing that make fluid-dynamics difficult: the non-linearity of the equations involved.
With experience and understanding of the problem at hand, you can make some pretty good non-linear predictions on the response of your system. Until you cannot. And the beauty of the non-linear response is that your botched prediction will be way, way off.
It's the same for AI. It will see some nicely hidden pattern based on the data it is fed, and will generate some prediction based on it. Until it hits one of those critical moments when there is no substitute to solving the actual equations, and it will produce absolute rubbish.
And that problem will only get compounded by the increasing turbulence level in the atmosphere due to global warming, which is breaking down the long-term, fairly stable, seasonal trends.
And that is where your understanding breaks down.
What makes weather prediction difficult is the same thing that make fluid-dynamics difficult: the non-linearity of the equations involved.
With experience and understanding of the problem at hand, you can make some pretty good non-linear predictions on the response of your system. Until you cannot. And the beauty of the non-linear response is that your botched prediction will be way, way off.
It's the same for AI. It will see some nicely hidden pattern based on the data it is fed, and will generate some prediction based on it. Until it hits one of those critical moments when there is no substitute to solving the actual equations, and it will produce absolute rubbish.
And that problem will only get compounded by the increasing turbulence level in the atmosphere due to global warming, which is breaking down the long-term, fairly stable, seasonal trends.