281–290 of 310 posts
The multimesh is interesting. Still, I bet the Fourier Neural Operator approach will prove superior. Members of the same team (Sanchez-Gonzales, Battaglia) have already published multiple variations of this model, applied to other physical scenarios and lots of them proved to be dead ends. My money is on the FNO approach, anyway, which for some reason is only given a brief reference. To their credit DeepMind usually…
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 ).
why is hiring phds a measure?
They're also good at prioritizing outcomes, rather than other stuff.
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…
AI/ML's bitter lesson [1] applies again. In this case, the AI model may have learned a more practical model than the one human researchers painstakingly came up with by applying piles and piles of physics research. [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
You mean by remembering piles and piles of example data and interpolating between it.
I've been really impressed at how much better weather forecasting has become already. I remember weather forecasts feeling like a total crapshoot as recently as 15 years ago or so.
Isn't that highly subjective to where you live? Because I moved to Scandinavia and the forecast here is so incredibly bad, compared to central europe.
Ten years ago, the weather forecast was so unreliable that I just assumed anything could happen on a given day, no matter the season. Frequently it would be unable to even tell you whether it was currently raining, and my heuristic for next day forecast instead was to just assume the weather would be the same as today.
Nowadays I find the next day forecasts are nearly always accurate and hourly precipitation forecasts are good enough that I can plan my cycles and walks around them.
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…
Earlier quoted context omitted.
You assuming OpenAI's models are AGI tells more about you than anything else.
If Alan Turing says ChatGPT is an AGI, it's good enough for me.
> "Can machines think?" I believe to be too meaningless to deserve discussion.
So I don't think he would've appreciated such a fuzzy concept as AGI.
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…
Shortcuts 100% exist. Imagine another physical problem. Simulating a sand grain and how it bounces off other sand grains or lodges against them. If you wanted to simulate a sand mountain, you could use a massive amount of compute and predict the location and behaviour of every single grain. Or, you could take a bunch of well-known shortcuts and just know that sand sits in a heap at the angle-of-repose. That angle dec…
The example you gave does not really explain anything.
The multimesh is interesting. Still, I bet the Fourier Neural Operator approach will prove superior. Members of the same team (Sanchez-Gonzales, Battaglia) have already published multiple variations of this model, applied to other physical scenarios and lots of them proved to be dead ends. My money is on the FNO approach, anyway, which for some reason is only given a brief reference. To their credit DeepMind usually…
Yandex claims to be using AI-based weather forecasting for a good part of a decade and claims it as a success. It is quite good. https://meteum.ai/
Earlier quoted context omitted.
IIRC the MetNet announcement a few weeks ago said that their model is now used when you literally Google your local weather. I don't think it's available yet to any API that third party weather apps pull from, so you'll have to keep searching "weather in Seattle" to see it.
Any idea why it is still showing the "weather.com" link next to the forecast?