fair enough, my habit is to go for relatively neutral words such as “dislike” to encapsulate a broader spectrum of people.

It’s not brute-force to a better algorithm per se. It’s the same algorithm, exactly as “stupid,” just with more force (more numerous and powerful GPUs) running it.

Three are benchmarks to check if the model is “good” – for instance, how well the model does on standardized tests similar to SATs (researchers are very careful to ensure that the questions do not appear on the internet anywhere, so that the model can’t just memorize the answers.)

I think a different way to look at what you’ve brought up in the second paragraph is that people are angry and talking about the power usage because the dislike AI, not the other way around. It doesn’t really make sense for people to be angry about the power usage of AI if the power usage had no environmental impact.

The AIs can definitely get more advanced, sure, but with that should come some sort of efficiency.

This is what AI researchers/pundits believed until roughly 2020, when it was discovered you could brute force your way to have more advanced AIs (so-called “scaling laws”) just by massively scaling up existing algorithms. That’s essentially what tech companies have been doing ever since. Nobody knows what the limit on this is going to be, but as far as I know nobody has any good evidence to suggest that we’re near the limit of what’s going to be possible with scaling.

We’re also seemingly on the cusp of quantum computing, which I imagine would reduce power requirements.

Quantum computing is not faster than regular computers. Quantum computing has efficiency advantages for some particular algorithms, such as breaking certain types of encryption. As far as I’m aware, nobody is really looking to replace computers with quantum computers in general. Even if they did, I don’t think anyone has thought of a way to accelerate AI using quantum computing. Even if there were a way to, it would presumably require quantum computers like, 15 orders of magnitude more powerful than the ones we have today.

We have very, very real and very, very large environmental concerns that need addressing.

Yeah. I don’t think AI is really at the highest level of concern for environmental impact, especially since it is looking plausible it will lead to investing in nuclear power, which would be a net positive IMO. (Coolant could still be an issue though.)

That’s a good point. I rescind my argument that training is necessarily more expensive than sum-of-all-deployment.

I still think people overestimate the power draw of AI though, because they’re not dividing it by the overall usage of AI. If people started playing high-end video games at the same rate AI is being used, the power usage might be comparable, but it wouldn’t mean that an individual playing a video game is suddenly worse for the environment than it was before. However, it doesn’t really matter, since ultimately the environmental impact depends only on the total amount of power (and coolant) used, and where that power comes from (could be coal, could be nuclear, could be hydro).

If you are expecting AI to not have much impact and turn out to be a bubble, then I guess there isn’t much reason to believe it it will have much environmental impact. If you expect AI to not be a fad, then yeah it could have big environmental consequences if we can’t find renewable power and coolant. If AI is all it is hyped up to be, then it would dwarf the rest of humanity’s power consumption down to a footnote. So it really depends on how bullish you are about AI, or at least how bullish you expect the market to be going forward.

Regarding proof-of-work crypto, well, bitcoin is currently at its all-time high in terms of value, exceeding USD$100k/BTC. So I’m not sure I exactly buy the idea that it’s less popular, though perhaps people aren’t reporting on it as much. If the power consumption of crypto has levelled off, which I don’t know if it has, then it might be because it’s expensive to build a mining rig and the yield goes down over time as more bitcoin is mined. (It’s presumably true of other proof-of-work crypto, too, but as more BTC is mined, the marginal yield of mining more BTC decreases.)

How is it any worse than crypto farms, or streaming services?

These two things are so different.

Streaming services are extremely efficient; they tend to be encode-once and decode-on-user’s-device. Video was for a long time considered a tough thing to serve, so engineers put tons of effort into making it efficient.

Crypto currency is literally designed to be as wasteful as possible while still being feasible. “Proof-of-work” (how Bitcoin and many other currencies operate) literally means that crypto mining algorithms must waste as much computation as they can get away with doing pointless operations just to say they tried. It’s an abomination.

Maybe you should stop smelling text and try reading it instead. :P

Running an LLM in deployment can be done locally on one’s machine, on a single GPU, and in this case is like playing a video game for under a minute. OpenAI models are larger than by a factor of 10 or more, so it’s maybe like playing a video game for 15 minutes (obviously varies based on the response to the query.)

It makes sense to measure deployment usage marginally based on its queries for the same reason it makes sense to measure the environmental impact of a car in terms of hours or miles driven. There’s no natural way to do this for training though. You could divide training by the number of queries, to amortize it across its actual usage, which would make it seem significantly cheaper, but it comes with the unintuitive property that this amortization weight goes down as more queries are made, so it’s unclear exactly how much of the cost of training should be assigned to a given query. It might make more sense to talk in terms of expected number of total queries during the lifetime deployment of a model.

I agree with your second statement. You have misunderstood me. I am not saying the damage is done so we might as well use it. I am saying people don’t understand that it is the training of AIs which is directly power-draining.

I don’t understand why you think that my observation people are ignorant about how AIs work is somehow an endorsement that we should use AIs.

Halo: Reach > Halo 3

Are you interpreting my statement as being in favour of training AIs?

This is still orders of magnitude less than what it takes to run an EV, which are an eco-friendly form of carbrained transportation. Especially if you live in an area where the power source is renewable. On that note, it looks to me like AI is finally going to be the impetus to get the U.S. to invest in and switch to nuclear power – isn’t that altogether a good thing for the environment?

how about, fuck capitalism? Have you lost sight of the goal?

really depends. You can locally host an LLM on a typical gaming computer.

you are correct, and also not in any way disagreeing with me.

That’s how Canada got its first female PM, and it has not resulted in any more. I don’t really think it’s going to have the feminist effect you are hoping for.

My honest opinion is that both Clinton and Harris lost to Trump because people wanted Trump. Biden won despite being a paper bag because people realized they hated trump. Then they forgot about it.

there is so much rage today. why don’t we uh, destroy them with facts and logic

I know she’s exaggerating but this post yet again underscores how nobody understands that it is training AI which is computationally expensive. Deployment of an AI model is a comparable power draw to running a high-end videogame. How can people hope to fight back against things they don’t understand?

Gee, thanks, Einstein.

There are various romhacks available to improve the flaws of the NES and gameboy games, or make them more palettable to a modern audience. No need for save states.