81–90 of 105 posts
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[citation needed] Nuclear Power plants who use river water in their cooling cycle and pump the (now heated) water back to the river don't pay for that.
That's not the kind of water use the article is complaining about.
apparently, when water is used for purposes that annoying people dislike for ideological reasons, it disappears into another dimension, lost forever.
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TFA: > Many data centres use water-based systems to cool the plant with towers evaporating the heat, like a huge perspiration system, which means that the water is lost. > All of these companies have schemes to put water back into nature using projects that help river flow, capture rainwater, recharge aquifers and modify dams. They have all pledged to become “water positive” by 2030: returning more than they consume.…
Please don’t be that person quoting the rules when you don’t understand the arguments. This hit piece from someone whose job is directly endangered by LLMs is in the same absurd category as the similar articles about how AirPods contribute to e-waste. just look at them! They’re tiny! Or the breathless editorials blaming Elon for destroying the environment with the damage done to the launchpad by his rocket.. a one ti…
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That's a great argument! Assuming every word generated by ChatGPT is a word which would otherwise have been typed by a human writer. Is every word generated by ChatGPT a word which would otherwise have been typed by a human writer?
Why would that matter? Every system has inefficiencies. For example: Person sitting in office, trained for years, kept alive and breathing, being paid for by their employer — that is not writing that mail their boss is waiting for, but instead arguing about the resources used by ChatGPT.
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[citation needed] Nuclear Power plants who use river water in their cooling cycle and pump the (now heated) water back to the river don't pay for that.
That's not the kind of water use the article is complaining about.
Earlier quoted context omitted.
Please don’t be that person quoting the rules when you don’t understand the arguments. This hit piece from someone whose job is directly endangered by LLMs is in the same absurd category as the similar articles about how AirPods contribute to e-waste. just look at them! They’re tiny! Or the breathless editorials blaming Elon for destroying the environment with the damage done to the launchpad by his rocket.. a one ti…
What’s wrong with this perspective? Why are the scale of incumbents not part of these stories?
"Data centre uses water for cooling" is also not news, not in the slightest, in any way, shape or form. It's how it's done. It's how it's been done for decades, at a huge scale, but that scale is absolutely dwarfed by... checks notes... everything else. Literally just the leaks in municipal water pipes use more water than a data centre.
"ChatGPT uses a lot of water" is either a hit-piece, or a failed journalist desperately trying to make a non-story into something that'll get eyeballs.
I guess they succeeded.
PS: That, or they're simply innumerate. The general population is! Teraliters, gigaliters, megaliters, they all sound big, ya know?
PPS: Some numbers! Sydney Water lost 25,700 megaliters to leaks in that one city per year. A "hyperscaler" data centre uses up to 750 megaliters per year for cooling. Azure uses maybe 10% of their cooling capacity for ChatGPT, but I suspect it's a lot less. That's... what.. 75 megaliters per year per data centre? Water pipe leaks are wasting 340x as much as AI compute, which is a useful software product. Water leaks are pure waste. https://www.smh.com.au/national/nsw/nine-per-cent-of-sydney-... and https://dgtlinfra.com/data-center-water-usage/
1. ChatGPT is in the title purely for click-related reasons. This is about data center water usage, and applies to any application that uses a data center. 2. Seems like water should be more expensive in water-stressed areas and that data centers should pay a rate that includes the cost of the externality. Is it, and do they?
1. Yeah, let’s talk Microsoft Teams instead. I suspect that’s worse than AI training… 2. The article I’d write starts with incumbent uses of water. Residential use, especially in dry areas, has been pressured to progressively increase efficiency. It does take energy to treat and pump and treat water again. But some people always wonder if they’re saving water just so another part of town can add residents or sell wat…
If we assume that 25% of daily compute of 200 million people is spent on Electron and similar bloated software, and they're all using 50W computers (splitting the difference between laptops and desktops), that's about 7TWh per year: the annual electricity usage of Mongolia, or roughly 1% of Japan.
Which is actually less than I had thought, but it also doesn't include the server-side resources.
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Can be true, but it applies to Disneyland as well.
But if you're a conservative cynic about Disneyland, people will just label you as some old sad geezer with outdated opinions yelling at kids playing in the garden. You have to hate on something new and trendy to keep up your consumer focus group engagement metrics these days.
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My entire life trajectory has changed because of how LLMs have expanded my ability to write, think and reason. Have fun at Disneyland, though.
If you've experienced this life altering change and it's reproducible, you could show up a lot of AI critics with specifics. How have LLMs expanded your ability to reason, for example?
Meanwhile, a decent portion of my own peers echo my sentiment, mainly programmers. I don't have to argue with them. Instead I can analyze and improve methodologies with them, collaborate, and generally have positive, stimulating and thought-provoking conversations. You can imagine which of the two kinds of people I typically choose to engage with. Especially on a forum like HN, where meandering circular arguments are discouraged. Maybe the happy medium is just blogging about it. I really do want to blog and I'm trying to learn about it and make more time to write, but HN does already scratch a lot of that itch for me.
All in all... the only self-proclaimed AI critics left at this point are people who have either chosen to be deliberately ignorant about the benefits of this technology, or who lack the depth of skill or interest to find useful things for these models to do for them.
I've taken on increasingly ambitious projects because I now have little programmable assistants embedded into the UX of various tools that I work with. They're not perfect, sometimes we argue, sometimes I just have to turn them off for a few hours, roll up my sleeves and work through the muck. But at a high level, the benefits are huge. Modern adequately-trained LLMs are amazing at project scaffolding, architecture, design, documentation and generally being a backboard for my thoughts when I have no one else around to converse with. My typical day is highly varied and while I have specialist friends, none of them match the generalist abilities of some LLMs.
I am generally skilled enough with informatics to know when I need to corroborate or discard information (basic research skills learned independently of LLMS) and so "what if it's wrong / how will you know?" has always been an irrelevant, false paradox parroted by people lacking those basic skills which should have been taught in school. These same people already struggle with finding accurate information with search engines or libraries, it's no wonder they struggle with something like an LLM which can be very confidently wrong. With programming, it's easier. You can keep the model focused, break down tasks into tiny pieces, solve and write tests for them one by one. It encourages you to write clean, modular, self-documenting code.
With other topics like science, art and mathematics, I'm still consistently impressed by 4o and o1's capabilities, despite glaring shortcomings. I know you want very specific instances but I'm really ADHD and so I am not exaggerating when I say my conversations with LLMs are highly multidisciplinary and before a just a couple years ago, I had a hard time organizing some of these more esoteric and complex systems in my head. My typical conversations would probably be of little use to someone.
I'm now working in parallel on several scientific and mathematical inquiries, as well as a few ambitious engineering projects. My time spent researching has greatly reduced, as I can get up to speed very quickly by dumping some articles into an LLM conversation and asking it detailed questions, asking it to provide thought models, etc., getting maximal value out of the information by immediately dialing into areas of interest.
Essentially... I've always felt naked without a phone or computer around to google the random questions and ideas I have throughout the day, and I consider the internet and search engines integral to who I am and the knowledge and skill I've attained. Now, instead of a search engine, I feel naked if I don't have a well-trained chat model around. I have developed a similar dependence as I have to search engines (I panic when I have a question, slap my leg and realize my phone isn't on my person) but the tradeoff is that I feel my thinking and doing has been augmented.
And even if transformers/LLMs hit a dead end in our lifetime... after all, many of these techniques are rooted in ideas from the 60s-90s which simply "didn't work" at the time, only to become relevant again after sufficient compute is accessible... I am eager to see what the next 40-odd years of technology brings us.
Regarding reproducibility, I think it would be extremely beneficial for people to be shown how AI can help them with day-to-day things, but I have a hard time recommending LLMs to people who I know aren't equipped with enough research skill to avoid harming or hindering themselves from incorrect advice. And my experience with these models is mainly academic and related to engineering, so I just don't have much to offer normies, but I'm sure other people have made lots of ground on that.
Personally I think chat models in their current form just aren't natural or functional enough to appeal to the average person. I think the average person will see the biggest benefits from tooling built around modern models. I'm working on one right now, a grid-based component system which essentially lets you create tiny little tools that can speak with each other, and orchestrate them into one or more domain-specific UIs. Users can share and adapt apps or individual components, or create production-ready in-house creative or business tooling. Social productivity programs like this, as well as calendars, sheets, etc (all buildable within my app in minutes with a handful of stock components, with built-in multi-user support) will unlock the full power of advanced natural language models. Hoping to debut it sometime next year.