281–290 of 683 posts
Earlier quoted context omitted.
The fact that certain specific data centres are being proposed or built in areas with water issues may be bad, but it does not imply that all AI data centres are water guzzling drain holes that are killing Earth, which is the point you were (semi-implicitly) making in the article.
What is it that you imagine happens to the water after it goes through the data center?
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They're not asking about telling the difference in collected data sets, data sets aren't weather. The question is can you tell the difference between the rain you see outside your window, and some representation of a simulated environment where the computer says "It's raining here in this simulated environment". The implied answer is of course, one is water falling from the sky and one is a machine.
You can't look at the "real weather" though. You can only look at the outputs. That's the constraint. Good luck and have fun. A human brain is a big pile of jellied meat spread. An LLM is a big pile of weights strung together by matrix math. Neither looks "intelligent". Neither is interpretable. The most reliable way we have to compare the two is by comparing the outputs. You can't drill a hole in one of those and se…
Tip for AI skeptics: skip the data center water usage argument. At this point I think it harms your credibility - numbers like "millions of liters of water annually" (from the linked article) sound scary when presented without context, but if you compare data centers to farmland or even golf courses they're minuscule. Other energy usage figures, air pollution, gas turbines, CO2 emissions etc are fine - but if you com…
Some time ago, I read the environmental impact assessment for a proposed natural gas thermal power plant, and in it they emphasized that their water usage was very low (to the point that it fit within the unused part of the water usage allowance for an already existing natural gas thermal power plant on the same site) because they used non-evaporative cooling. What prevents data centers from using non-evaporative coo…
In europe several power plants get shut down each summer because the heated water from those plants would have significant impact on the local wildlife.
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Growing almonds uses 1.3 trillion gallons of water annually in California alone. This is more than 4 times more than all data centers in the US combined, counting both cooling and the water used for generating their electricity. What has more utility: Californian almonds, or all IT infrastructure in the US times 4?
Depends on what the datacenters are used for. AI has no utility. Almonds make marzipan.
> As a technologist I want to solve problems effectively (by bringing about the desired, correct result), efficiently (with minimal waste) and without harm (to people or the environment). I agree with the first two points, but as others have commented the environmental claim here is just not compelling. Starting up your computer is technically creating environmental waste. By his metrics solving technical problems et…
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I think the point here is that objecting to AI data center water use and not to say, alfalfa farming in Arizona, reads as reactive rather than principled. But more importantly, there are vast, imminent social harms from AI that get crowded out by water use discourse. IMO, the environmental attack on AI is more a hangover from crypto than a thoughtful attempt to evaluate the costs and benefits of this new technology.
But if I say "I object to AI because and its water use", why would you assume that I don't also object to alfalfa farming in Arizona? Similarly, if I say "I object to the genocide in Gaza", would you assume that I don't also object to the Uyghur genocide? This is nothing but whataboutism. People are allowed to talk about the bad things AI does without adding a 3-page disclaimer explaining that they understand all the…
If you take a strong argument and through in an extra weak point, that just makes the whole argument less persuasive (even if that's not rational, it's how people think).
You wouldn't say the "Uyghur genocide is bad because of ... also the disposable plastic crap that those slave factories produce is terrible for the environment."
Plastic waste is bad but it's on such a different level from genocide that it's a terrible argument to make.
It is ultimately a hardware problem. To simplify it greatly, an LLM neuron is a single input single output function. A human brain neuron takes in thousands of inputs and produces thousands of outputs, to the point that some inputs start being processed before they even get inside the cell by structures on the outside of it. An LLM neuron is an approximation of this. We cannot manufacture a human level neuron to be s…
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Assuming you want to define the goal, "AGI", as something functionally equivalent to part (or all) of the human brain, there are two broad approaches to implement that. 1) Try to build a neuron-level brain simulator - something that is a far distant possibility, not because of compute, but because we don't have a clear enough idea of how the brain is wired, how neurons work, and what level of fidelity is needed to ca…
Or 3) Pour enough computation into a sufficiently capable search process and have it find a solution for us Which is what we're doing now. The bitter lesson was proven right once again. LLMs prove that you can build incredibly advanced AIs without "understanding" how they work.
A search or evolutionary process would also need an AGI-evaluator to guide the search, and this evaluator would then determine the characteristics of the solution found, so it rather smacks of benchmark gaming rather than the preferred approach of designing for generic capabilities rather than specific evaluations.
I wouldn't say we don't know how LLMs "work" - clearly we know how the transformer itself works, and it was designed intentionally with certain approach in mind - we just don't know all the details of what representations it has learnt from the data. I also wouldn't say LLMs/transformers represent a bitter lesson approach since the architecture is so specific - there is a lot of assumptions baked into it.
Tip for AI skeptics: skip the data center water usage argument. At this point I think it harms your credibility - numbers like "millions of liters of water annually" (from the linked article) sound scary when presented without context, but if you compare data centers to farmland or even golf courses they're minuscule. Other energy usage figures, air pollution, gas turbines, CO2 emissions etc are fine - but if you com…
Farmland, AI data centers, and golf courses do not provide the same utility for water used. You are not making an argument against the water usage problem, you are only dismissing it.
Of course water used up will eventually evaporate, and produce rainfall in the water cycle, but unfortunately at many places "fossil" water is used up, or more water used in an area then the watershed can sustainably support.
This is a constant source of miscommunication about water usage, and that of agriculture also. It is very different to talk about the water needs to raise a cow in eg. Colorado and in Scotish highlands, but this is usually removed from the picture.
The same context should be considered for datacenters.
Tip for AI skeptics: skip the data center water usage argument. At this point I think it harms your credibility - numbers like "millions of liters of water annually" (from the linked article) sound scary when presented without context, but if you compare data centers to farmland or even golf courses they're minuscule. Other energy usage figures, air pollution, gas turbines, CO2 emissions etc are fine - but if you com…
hmm why exactly? mineral content?