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"More accurate forecasts of risks of extreme weather can help officials safeguard more lives, avert damage, and save money," DeepMind said. Did the DeepMind AI say this?
It's incredible that we are able to predict anything this far into the future, but the complexity seems like it lends itself to this kind of black box approach.
*This is all speculation, so I'd be grateful if someone more knowledgeable could tell if if I'm mistaken about these assumptions. It's an interesting topic.
This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.
How many people can scrutinize the esoteric ensemble of climate models that existing climatologists use to generate weather predictions today?
Earlier quoted context omitted.
Totally different constraints. It’s like saying Nobel prize winning physicists can’t be trusted because they get basic arithmetic wrong Gemini is fast food - it’s not supposed to be the best.
Your analogy doesn't make sense. Nobel winning physicists can definitely perform arithmetic.
"Do not worry about your difficulties in mathematics, I assure you that mine are greater" - Albert Einstein
Based on the Gemini release it’s hard to take what Google claims at face value.
This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.
But most people just need to know if it’s going to be storming, hot or, going to rain on a given day and that is where this shines.
Earlier quoted context omitted.
>Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
Earlier quoted context omitted.
It's so new that it has no long term track record. I want to see how its record looks after say 1/3/5 years. Any 15 day forecast is just too far into the future for me to take seriously. Even the 3 day forecast is loose at best. By the time it reaches 7-10 days, some of the forecast is completely off when it reaches the 3 day window. "But DeepMind said GenCast surpassed the precision of the center's forecasts in more…
>Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
I've worked on several projects trying to use AI to model various CFD calculations. We could trivially get 90+ percent accuracy on a bunch of metrics, the problem is that its almost always the really important and critical cases that end up being wrong in the AI model.
Based on the Gemini release it’s hard to take what Google claims at face value.
Specifically just that the release was a bit awkward and had some problems? I've found the latest model releases to be very good compared to other frontier models.
This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.
How many people can scrutinize the esoteric ensemble of climate models that existing climatologists use to generate weather predictions today?