this post was submitted on 15 Jun 2024
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VCs, Very Smart (lemmy.world)
submitted 4 months ago by [email protected] to c/[email protected]
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[–] [email protected] 0 points 4 months ago* (last edited 4 months ago) (2 children)

https://en.wikipedia.org/wiki/Climate_model

Cloud-resolving climate models are nowadays run on high intensity super-computers which have a high power consumption and thus cause CO2 emissions.[44] They require exascale computing [..]. For example, the Frontier exascale supercomputer consumes 29 MW.[45] It can simulate a year’s worth of climate at cloud resolving scales in a day.[46]

Techniques that could lead to energy savings, include for example: "reducing floating point precision computation; developing machine learning algorithms to avoid unnecessary computations; and creating a new generation of scalable numerical algorithms that would enable higher throughput in terms of simulated years per wall clock day."[44]

[–] [email protected] 0 points 4 months ago (1 children)

Let me point a critical part that you seemed to have skipped:

... to avoid unnecessary computations...

Using ML algorithms to add more computations that weren't necessary doesn't help. Using it to improve computations can, if it's more efficient than not using it. ML can be a useful and good thing, but the extreme vast majority of what it's currently being used for is trying to come up with more places to shove it where it doesn't reduce computations and instead increases it.

[–] [email protected] 0 points 4 months ago

I just copied the Wikipedia part, because I thought it funny how AI in media is different from AI in science. I don't have a stance on the power consumption of climate models because without the models we'd be very unequipped for the storm we brew.

Sorry for creating the image of me criticising valuable science.