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 Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects.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.
They're taking about emissions, not energy use. You have a reading comprehension issue. The emissions are from the energy production. It's logical to say that a, largely pointless, technology using high amounts of electricity cause emissions through the generation of electricity to power the pointless AI tech.
AI tech isn't pointless though. It's not just about trying to replace artists or whatever. It significantly speeds up things like programming. It's also used by scientists to mine data to find patterns and make predictions. For Pete's sake I am pretty sure climate modeling relies on AI and other forms of HPC.
Scientists analyze data using statistics. I don't see how and LLM helps with that. And it barely helps with programming, not to the extent that it is worth the impact.
I wasn't just talking about LLMs. Lots of modern data analysis techniques rely on machine learning.
Although LLMs are also used by scientists to help with things like programming that not all scientists are necessarily good at or properly trained in.
Yeah, seriously. Did the person you were replying to think the energy that's powering datacenters was all clean?
I'm also referring to emissions, just redirecting focus about HOW electricity is produced. Also, AI is not pointless, that's a bad claim. You have a comprehension issue.
I didn't miscomprehend, you just disagree with my reasonable assertion that AI is pointless and sucks. Hope this helps!
I didn’t miscomprehend, you just disagree with my reasonable assertion that AI isn't pointless and sucks. Hope this helps!
That is not a reasonable assertion at all. AI is being used in more ways than what is being described in your rage-bait media diet. "AI is pointless and it sucks" is a blatantly ignorant statement.
It's marginal utility is not worth the energy expenditures.
You just don't know what it is beyond memes.
No reason for this to have been removed
Source: Implement trained cnn and gnn models in hardware for real-time particle identification and tracking for high-energy physics.