Technology

59168 readers

2011 users here now

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

Our Rules

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

Approved Bots

founded 1 year ago

MODERATORS

[email protected]

345

AI Computing on Pace to Consume More Energy Than India, Arm Says (news.bloomberglaw.com)

submitted 6 months ago by [email protected] to c/[email protected]

60 comments fedilink hide all child comments

AI’s voracious need for computing power is threatening to overwhelm energy sources, requiring the industry to change its approach to the technology, according to Arm Holdings Plc Chief Executive Officer Rene Haas.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 2 points 6 months ago (1 children)

Because they're not actually pulling too much from the grid to cause damage to others or even the grid itself.

Any musings about curtailing AI due to power consumption is just bullshit for clicks. We'll improve efficiency and increase productivity, but we won't reduce usage.

[–] [email protected] 1 points 6 months ago* (last edited 6 months ago)

Improving the models doesn't seem to work: https://arxiv.org/abs/2404.04125?

We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance, following a sample inefficient log-linear scaling trend.

It's taking exponentially more data to get better results, and therefore, exponentially more energy. Even if something like analog training chips reduce energy usage ten fold, the exponential curve will just catch up again, and very quickly with results only marginally improved. Not only that, but you have to gather that much more data, and while the Internet is a vast datastore, the AI models have already absorbed much of it.

The implication is that the models are about as good as they will be without more fundamental breakthroughs. The thing about breakthroughs like that is that they could happen tomorrow, they could happen in 10 years, they could happen in 1000 years, or they could happen never.

Fermat's Last Theorem remained an open problem for 358 years. Squaring the Circle remained open for over 2000 years. The Riemann Hypothesis has remained unsolved after more than 150 years. These things sometimes sit there for a long, long time, and not for lack of smart people trying to solve them.