this post was submitted on 04 Dec 2024
106 points (90.8% liked)

Technology

60102 readers
2171 users here now

This is a most excellent place for technology news and articles.

Our Rules

  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 2 years ago
MODERATORS
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
106
DeepMind's GenCast AI is really good at forecasting the weather (www.engadget.com)
submitted 3 weeks ago by [email protected] to c/[email protected]
39 comments fedilink hide all child comments
 

Pretty much the only thing I think AI could be useful for - forecasting the weather based off tracking massive amounts of data. I look forward to seeing how this particular field of study is improved.

Bonus points, AI weather modeling, for once, saves energy relative to physics models. Pair it with some sort of light weight physical model to keep the hallucinations at bay, and you've got a good combo.

you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 3 points 3 weeks ago* (last edited 3 weeks ago) (1 children)

About 4 years ago, this video showed that a ML model can be used to cut costs on physics simulations. It’s about time we did that with weather too.

[–] [email protected] 2 points 3 weeks ago (2 children)

It's not just about cutting costs, but also improving accuracy. Physical simulations factor in a dozen or so weather conditions to predict outcomes. Machine learning can track thousands of conditions, drawing connections not realized in physical models, leading to much more accurate statistical models.

[–] [email protected] 1 points 3 weeks ago (1 children)

Yeah, that’s pretty impressive. I wonder if you could apply the same philosophy in other areas too. Instead of training the model with data produced in a simulation, you could just feed it real world data instead. Like, if you gave a bunch of stress-strain data to a model, could you make better predictions about the behavior of physical structures, such as bridges and towers.

[–] [email protected] 3 points 3 weeks ago (1 children)

There are already non-AI physical modeling programs that do that.

[–] [email protected] 0 points 3 weeks ago

Yes there are, but would it be possible to replace them with ML and get more accurate predictions?

[–] [email protected] 0 points 3 weeks ago

Physical simulations factor in a dozen or so weather conditions to predict outcomes.

Many more parameters than that.

Machine learning can track thousands of conditions

Scientists already know which ones are relevant. You're not going find any big surprises there with an AI. Shotgun-style factor analysis has already been done to death. The price of baked beans doesn't impact the wind direction in the Persian Gulf. It's OK to not consider it.

drawing connections not realized in physical models

Again, it's possible but unlikely. And you'd need an AI that could be queried to tell you what factors it considered, and most of them don't work that way right now.

Statistical models don't become more accurate because you throw irrelevant parameters at them. But that's how ML systems work.