• FireRetardant@lemmy.world
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    5 months ago

    I wonder how much energy google wastes on its AI service in the regular search just to give me a worse answer than the top results I was actually looking for.

    • frezik@midwest.social
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      5 months ago

      Unlike purchasing things for imaginary gods, carbon credits could work in theory. At least well enough to be part of the solution. That is, if they were properly regulated around strategies that actually absorb carbon and everyone is forced to be honest and transparent.

      Which none of them do, of course.

  • Kachajal@lemmy.ml
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    5 months ago

    Ehhh. I get that exploitative techbros and cryptobros have confused the issue by latching onto the AI bubble.

    But at the same time generalized artificial intelligence is very likely possible and will be an absolute game-changer if and when it happens. It’s easily of similar value to fusion technology.

    And it is already bringing truly impressive results into reality - protein folding and diagnostic medicine come to mind.

    • abbiistabbii@lemmy.blahaj.zone
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      5 months ago

      But at the same time generalized artificial intelligence is very likely possible and will be an absolute game-changer if and when it happens. It’s easily of similar value to fusion technology.

      The “AI” we have now is basically advanced Autocomplete.

      • Kachajal@lemmy.ml
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        5 months ago

        In the same way that computers are basically advanced abaci.

        Don’t confuse a simplification made to demonstrate the basic functioning to a layman with how things actually work.

        LLM’s are neural networks, which are based on a model of brain function. There’s little reason to believe that we cannot eventually reach similar levels of effectiveness as human brains.

        Hell - reaching the levels of pigeon brains would already be absurdly useful.

        • pelotron@midwest.socialOP
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          5 months ago

          The problem is they’re already talking about needing trillions of dollars worth of hardware to make it happen. It’s absurd.

        • chayleaf@lemmy.ml
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          5 months ago

          While I agree that LLMs can achieve human-tier efficiency at most tasks eventually (some architectural changes will be necessary, but the core approach seems sound), it’s wrong to say it’s modeled after the human brain. We have no idea how brains work as they’re super complex, we’re building artificial neural networks from the ground up. AI uses centuries’ worth of math, but with our current maths knowledge the code isn’t too complicated. Human brains aren’t like that, they can’t be summed up in a few lines of code because DNA is a huge mess that contains so much more than just “learning”, so many inactive or redundant bits and pieces. We’re building LLMs with knowledge of how languages work, not how brains work.

          • jsomae@lemmy.ml
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            5 months ago

            Transformers are not built with our knowledge of language. That’s a gross approximation – it would honestly be more accurate to say they’re modelled after the human brain than that they’re built with our understanding of language. A big problem is that the connection between AI and language is poorly understood – we can’t even understand what the word2vec axes are.

            • chayleaf@lemmy.ml
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              5 months ago

              i’m not talking about knowing about how humans perceive/learn languages, i’m talking about language structure. Perhaps it’s wrong to call it “how languages work”

              • jsomae@lemmy.ml
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                5 months ago

                That’s what I meant, yes. They’re not built based on any linguistic field

                • chayleaf@lemmy.ml
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                  5 months ago

                  different neural network types excel at different tasks - image recognition was invented way before LLMs, not only for lack of processing power, but also because the previous architectures didn’t work with languages. New architectures don’t appear out of thin air, they are created with a rough idea of what we could need to make the network do a certain task (e.g. NLP) better. Even tokenization isn’t blind codepoint separation but is based on an analysis of languages. But yes, natural languages aren’t “parsed” for neural networks, they don’t even have a formal grammar.

    • Lotarion@lemmy.world
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      5 months ago

      And it doesn’t even make sense

      99% of a modern office’s correspondence already goes on online, and only the most important stuff gets backed up on paper copies, often because of regulations that are there for a reason

    • dislocate_expansion@reddthat.comB
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      5 months ago

      Seriously. It isn’t helpful towards the environment if we are using so many resources to mine chips and metals and then push it along the internet to then be trained on said AI bots. Would be more sustainable using paper and planting trees smh

  • pieter91@sh.itjust.works
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    5 months ago

    The fundamental problem is that there’s money to be made by consuming more and more “sustainable” resources. The real solution is to reduce consumption on a global scale.

      • greyw0lv@lemmy.ml
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        5 months ago

        Not OC, but some ways to “reduce consumption” are reducing our usage of inefficient technology by replacing it with more energy/resource efficient means.

        Examples include replacing individual automobiles with mass transit, building more dense cities to reduce consumption of construction materials/ vehicle miles, and not training massively large language models in facilities that consume more energy than an entire small country.

        • NomenCumLitteris@lemmy.ml
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          5 months ago

          In real world application, increased efficiency doesn’t decrease energy usage nor decrease labor required to live. Tech has gotten more efficient since the industrial revolution, but demand for technology has increased exponentially, energy use is astronomical, and workers still work more hours.