Despite its name, the infrastructure used by the “cloud” accounts for more global greenhouse emissions than commercial flights. In 2018, for instance, the 5bn YouTube hits for the viral song Despacito used the same amount of energy it would take to heat 40,000 US homes annually.
Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities.
Additionally, as these companies aim to reduce their reliance on fossil fuels, they may opt to base their datacentres in regions with cheaper electricity, such as the southern US, potentially exacerbating water consumption issues in drier parts of the world.
Furthermore, while minerals such as lithium and cobalt are most commonly associated with batteries in the motor sector, they are also crucial for the batteries used in datacentres. The extraction process often involves significant water usage and can lead to pollution, undermining water security. The extraction of these minerals are also often linked to human rights violations and poor labour standards. Trying to achieve one climate goal of limiting our dependence on fossil fuels can compromise another goal, of ensuring everyone has a safe and accessible water supply.
Moreover, when significant energy resources are allocated to tech-related endeavours, it can lead to energy shortages for essential needs such as residential power supply. Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects.
In other words, policy needs to be designed not to pick sectors or technologies as “winners”, but to pick the willing by providing support that is conditional on companies moving in the right direction. Making disclosure of environmental practices and impacts a condition for government support could ensure greater transparency and accountability.
if it’s not crypto miners with GPUs it’s AI, these narratives never really connect well with reality. /u/0ptimal wrote a great comment on this post: https://alexandrite.app/lemmy.world/comment/10355707
To no surprise, the other comments are full of laypeople that feel they understand the entire field they have never studied well enough to preach to others about just how useless and terrible it is, who also know nothing about the subject.
I know this is probably way off topic, but it made me think of Friendship is Optimal, especially the ending.
Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities.
This metric doesn’t say anything.
Do you mean it’s without context or comparison?
Im not being funny. I’m just stupid.
The whole article throws data without meaning.
Data is not information. Is this the amount of the water taken out of reach of farmers? Probably not. Is it the amount of energy used for cooling? Nope because liters is not an appropriate unit of energy. Is it the cost? Nope because that must be in dollars. So it’s data but not information. It can’t be compared to an hypothetically allegedly more efficient system.
So it takes 700,000 litres of water to cool a machine eh? Think about a water cooled PC, now, is that water cooled PC hooked up to your sink and continuously draining water? Or did you fill it up one time and then at the end when you’re done with it, dump the water back down the drain?
700,000 litres of water in a closed loop cooling system is not a problem in any way shape or form.
You’re technically correct, although there usually is some make-up that’s periodically necessary. You’ll want to blowdown some of that water from time to time to try and prevent scale/accumulation and even biomass buildup. It’s probably on the order of like 1-2%, and probably not continuously.
It would be better for the article to quantify this amount of water, that’s regularly leaving the system and needs to be replenished. 1% of 700,000 L is still a lot of water, but it’s very hard to measure the sustainability impact without knowing how often they happens. Once a year? Multiple times a year? Or once every few years?
Just for my credentials, I’m a chemical engineer by training and I used to do a little work with a closed loop steam generation and cooling system.
AI companies*
But it’s okay, because now we can get wrong answers faster than ever, and we’ve taken human creativity and joy out of art.
we’ve taken human creativity and joy out of art.
“As the photographic industry was the refuge of every would-be painter, every painter too ill-endowed or too lazy to complete his studies, this universal infatuation bore not only the mark of a blindness, an imbecility, but had also the air of a vengeance. … I am convinced that the ill-applied developments of photography, like all other purely material developments of progress, have contributed much to the impoverishment of the French artistic genius, which is already so scarce. … it is nonetheless obvious that this industry, by invading the territories of art, has become art’s most mortal enemy, and that the confusion of their several functions prevents any of them from being properly fulfilled. … If photography is allowed to supplement art in some of its functions, it will soon have supplanted or corrupted it altogether, thanks to the stupidity of the multitude which is its natural ally.”
So either something is EVERYTHING from the start or it’s not and thus not worth pursuing further.
Did I get your position right? The usefulness and applications for AI both now and in the future far exceeds what you’ve tried to boil it down to (thus destroying any nuance), your willful ignorance is showing.
but ai bad, and all that.
We can solve entire new classes of problems that we never could before.
Your problems are with capitalism and how we distribute our resources, not with advancements in automation.
Your problems are with capitalism and how we distribute our resources, not with advancements in automation.
This particularly story isn’t about wealth distribution though. It’s about environmental damage caused by this technology. So that’s a whole other class of problem. As for the other problems being about capitalism, I agree for sure that capitalism is a source of many many problems… but while we are in that system we should still try to minimise the problems. So if this technology has major problem when combined with capitalism, then we should either stop using capitalism, or stop using the technology - or both, until we make up our mind which we prefer to keep!
Every story is somewhat about wealth distribution. Your argument is fundamentally that AI is not worth it to spend the resources we are spending on it. If wealth was distributed more fairly, that would not be an argument since the money and carbon taxes spent on it would be an accurate representation of the will of the average person and its utility to them. That argument makes the most sense in the context of an inordinate amount of r sources being controlled and directed by the wealthy.
So if this technology has major problem when combined with capitalism, then we should either stop using capitalism, or stop using the technology - or both, until we make up our mind which we prefer to keep!
Except that it doesn’t. AI is no more frivolous and power hungry than any other industry. Video games consume far more power for instance and provide no economic value back.
