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GraphCast: AI model for weather forecasting

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Re: GraphCast: AI model for weather forecasting

#221

Post by Someone »

DaiPlusPlus wrote: post #210
ArnoVW wrote: post #202

Earlier quoted context omitted.

It’s difficult to retain top talent if you do not allow them to publish.

How does Apple do it, if anyone knows? Apple is so loathe to keep their potential product plans hidden that AAPL employees aren’t even allowed to have GitHub accounts without mgr approval… but they have to be employing serious researchers, but they’ll never get to publish on volition.

> they have to be employing serious researchers, but they’ll never get to publish on volition.

That’s not true. I wouldn’t know how free they are to publish but they do publish stuf. See https://machinelearning.apple.com/

Re: GraphCast: AI model for weather forecasting

#223

Post by tony_cannistra »

lainga wrote: post #215
tony_cannistra wrote: post #214

Similar methodologies are being applied to climate modeling, too. The Allen Institute has worked on it for a while, and has hired quite a few PhDs ( https://allenai.org/climate-modeling ).

How long? The cloud microparameterisation looks really exciting, but 10-year stability for a GCM (and "nearly conserving" water) is not great

I'm not sure. NVIDIA is also working on it (with, interestingly, some of the original AI2 folks).

Similar to the DeepMind effort, the ACE ML model that AI2+others developed is really just looking for parity with physical models at this stage. It looks like they've almost achieved this, with similar massive improvements in compute time + resource needs.

Re: GraphCast: AI model for weather forecasting

#224

Post by sheepshear »

isaacfrond wrote: post #191

I find this quite surprising actually. You'd think predicting the weather is mostly a matter of fast computation. The physical rules are well understood, so to get a better estimate use a finer mesh in your finite element computation and use a smaller time scale in estimating your differential equations. Neural networks are notoriously bad at exact approximation. I mean you can never beat a calculator when the issue…

The accuracy improvement boils down to representing more salient features in the model. The humans got a head start figuring out what to model, but the machine figures it out faster, so it caught up and surpassed them. Now it models more important stuff.

The speed difference is a side effect of completely different implementations. One is a step-by-step simulator, the other is an input/output pattern matcher.

Re: GraphCast: AI model for weather forecasting

#226

Post by david-gpu »

acolderentity wrote: post #222

How could an ai, programmed with the bias of people that already suck at predicting the weather, even get close to being accurate?

You don't train the AI with the forecasts made by other systems. You train the AI with the actual weather that was measured hours/days later.

Re: GraphCast: AI model for weather forecasting

#227

Post by jvalencia »

acolderentity wrote: post #222

How could an ai, programmed with the bias of people that already suck at predicting the weather, even get close to being accurate?

Weather is a complex mix of many systems. The traditional approach is to understand all the systems and add them together. Since we don't understand them all fully, we get a lot of chaos.

The ML algorithm doesn't care about the science, the agendas, the theories, nothing. It just looks for patterns in the data. Instead of an exact calculation it's more akin to numerical analysis. Turns out that looking at the whole in this case, is better than the sum of the parts.

Re: GraphCast: AI model for weather forecasting

#228

Post by [deleted] »

isaacfrond wrote: post #191

I find this quite surprising actually. You'd think predicting the weather is mostly a matter of fast computation. The physical rules are well understood, so to get a better estimate use a finer mesh in your finite element computation and use a smaller time scale in estimating your differential equations. Neural networks are notoriously bad at exact approximation. I mean you can never beat a calculator when the issue…

[deleted]
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