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PaperclipPerfector  8mo ago (text post)   29341 thread views

Culture War Roundup for the week of March 4, 2024

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We can simulate the weather for my town tomorrow with pretty damn good accuracy. Enough accuracy that it is VERY useful. Without building a computer the size of universe.

How about the weather in your town in a month? Or that same weather if you lived on the Moon where Navier-Stokes doesn't work because you're in a vaccum?

If your argument is that computational irreducibility is not relevant because we have found heuristics for certain problems, it seems to miss the point that the whole postulate is that a lot of important problems are not like the weather in your town tomorrow and do not have a convenient heuristic or general solution. And hence that throwing more compute at them won't really do anything.

I'm certain having more compute around will solve some problems, though we are sure to hit diminishing returns, but it seems weird to me to assume that it will solve all problems as you seem to imply.

Have you seen the new AI weather predictors? They can do what it took a supercomputer days to do in minutes on a laptop.

For inputs, GraphCast requires just two sets of data: the state of the weather 6 hours ago, and the current state of the weather. The model then predicts the weather 6 hours in the future. This process can then be rolled forward in 6-hour increments to provide state-of-the-art forecasts up to 10 days in advance.

https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/

This is just the first one...

Ultra extreme computational reducibility in fast action for yah' in both computational load and floating point elimination.

Have you seen the new AI weather predictors? They can do what it took a supercomputer days to do in minutes on a laptop.

No, I have not seen them. Can I download and run them locally?

Are you sure that https://github.com/google-deepmind/graphcast is runnable on laptop?

Yes.

For start:

GraphCast currently lacks the ability to marshal data for its own starting state, a process known as data assimilation. In traditional forecasts, this data is carefully placed into the simulation after thorough checks against physics and chemistry calculations to ensure accuracy and consistency. Currently, GraphCast needs to use starting states prepared in the same way by the ECMWF’s own tools.

“Google is not going to be running weather forecasts anytime soon, because they cannot do the data assimilation,” says Renfrew. “And the data assimilation is typically half to two-thirds of the computing time in these forecasting systems.”

https://www.newscientist.com/article/2402556-deepmind-ai-can-beat-the-best-weather-forecasts-but-there-is-a-catch/

Yes like all things you need the data in the first place, in this case you need very complex and assimilated data to run the forecast. But yeah you can then run graphcast on a laptop. 3 tone se-man-tics jingle, I would also question, do you think it will stop here due to CI?

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