Right, but that’s kind of like saying “I don’t kill babies” while you use a product made from murdered baby souls. Yes you weren’t the one who did it, but your continued use of it caused the babies too be killed.
There is no ethical consumption under capitalism and all that, but I feel like here is a line were crossing. This fruit is hanging so low it’s brushing the grass.
I’m interpreting your statement as “the damage is done so we might as well use it”
And I’m saying that using it causes them to train more AIs, which causes more damage.
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.
It still smells like an apologist argument to be like “yeah but using it doesn’t actually use a lot of power”.
I’m actually not really sure I believe that argument either, through. I’m pretty sure that inference is hella expensive. When people talk about training, they don’t talk about the cost to train on a single input, they talk about the cost for the entire training. So why are we talking about the cost to infer on a single input?
What’s the cost of running training, per hour? What’s the cost of inference, per hour, on a similarly sized inference farm, running at maximum capacity?
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.
You’re way overcomplicating how it could be done. The argument is that training takes more energy:
Typically if you have a single cost associated with a service, then you amortize that cost over the life of the service: so you take the total energy consumption of training and divide it by the total number of user-hours spent doing inference, and compare that to the cost of a single user running inference for an hour (which they can estimate by the number of user-hours in an hour divided by their global inference energy consumption for that hour).
If these are “apples to orange” comparisons, then why do people defending AI usage (and you) keep making the comparison?
But even if it was true that training is significantly more expensive that inference, or that they’re inherently incomparable, that doesn’t actually change the underlying observation that inference is still quite energy intensive, and the implicit value statement that the energy spent isn’t worth the affect on society
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).
You’re absolutely right that the environmental impact depends on the source of the energy, and less obviously, by the displaced demand that now has to seek energy from less clean sources. Ideally we should have lots of clean energy, but unfortunately we often don’t, and even when AI uses clean sources, they’re often just forcing preexisting load elsewhere. If we can start investing in power infrastructure projects at the national (or state/province level) then maybe it wouldn’t be so bad, but it never happens at a scale that we need.
I think the argument isn’t the environmental impact alone, it’s the judgement about the net benefit of both the environmental impact and the product produced. I think the statement is “we spent all this power, and for what? Some cats with tits and an absolutely destroyed labour market. Not worth the cost” Especially because it’s a cost that the users of AI are forcing everyone to pay. Privatize profits, socialize losses, and all that.
Right, but that’s kind of like saying “I don’t kill babies” while you use a product made from murdered baby souls. Yes you weren’t the one who did it, but your continued use of it caused the babies too be killed.
There is no ethical consumption under capitalism and all that, but I feel like here is a line were crossing. This fruit is hanging so low it’s brushing the grass.
“The plane is flying, anyway.”
Are you interpreting my statement as being in favour of training AIs?
I’m interpreting your statement as “the damage is done so we might as well use it”
And I’m saying that using it causes them to train more AIs, which causes more damage.
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.
I guess.
It still smells like an apologist argument to be like “yeah but using it doesn’t actually use a lot of power”.
I’m actually not really sure I believe that argument either, through. I’m pretty sure that inference is hella expensive. When people talk about training, they don’t talk about the cost to train on a single input, they talk about the cost for the entire training. So why are we talking about the cost to infer on a single input?
What’s the cost of running training, per hour? What’s the cost of inference, per hour, on a similarly sized inference farm, running at maximum capacity?
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.
You’re way overcomplicating how it could be done. The argument is that training takes more energy:
Typically if you have a single cost associated with a service, then you amortize that cost over the life of the service: so you take the total energy consumption of training and divide it by the total number of user-hours spent doing inference, and compare that to the cost of a single user running inference for an hour (which they can estimate by the number of user-hours in an hour divided by their global inference energy consumption for that hour).
If these are “apples to orange” comparisons, then why do people defending AI usage (and you) keep making the comparison?
But even if it was true that training is significantly more expensive that inference, or that they’re inherently incomparable, that doesn’t actually change the underlying observation that inference is still quite energy intensive, and the implicit value statement that the energy spent isn’t worth the affect on society
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).
You’re absolutely right that the environmental impact depends on the source of the energy, and less obviously, by the displaced demand that now has to seek energy from less clean sources. Ideally we should have lots of clean energy, but unfortunately we often don’t, and even when AI uses clean sources, they’re often just forcing preexisting load elsewhere. If we can start investing in power infrastructure projects at the national (or state/province level) then maybe it wouldn’t be so bad, but it never happens at a scale that we need.
I think the argument isn’t the environmental impact alone, it’s the judgement about the net benefit of both the environmental impact and the product produced. I think the statement is “we spent all this power, and for what? Some cats with tits and an absolutely destroyed labour market. Not worth the cost”
Especially because it’s a cost that the users of AI are forcing everyone to pay. Privatize profits, socialize losses, and all that.