this post was submitted on 14 Apr 2025
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[–] Dirt_Owl@hexbear.net 40 points 4 days ago (9 children)

Pretty sure people in tech hate it too outside of those trying to sell it

[–] Cadende@hexbear.net 20 points 4 days ago* (last edited 4 days ago) (1 children)

There's a contingent that like it. For some, they don't have to even pretend to have social skills since they can outsource writing to AI. They are also increasingly using it in place of google/copy-pasting from stackoverflow/etc to get "quick fix" solutions to their problems. It's not particularly good at those tasks IMO, but I genuinely think for some people the dopamine hit of copy-pasting something directly from chatgpt and not having to so much as lift a finger and it working first try, is addictive, and even though they usually have to troubleshoot it and re-prompt and then make changes by hand, they just keep trying for that sweet no-effort fix. For some of them they seem to treat it like a junior coworker you can offload all your work onto, forever.

In my experience (I've literally never used it but had coworkers try to feed its answers to me when we're working together on something, or giving what it spit out to me to fix for them), it tends to do okay for common use-cases, ones that you can almost always just look up in documentation or stackoverflow anyhow, but in more niche problems, it will often hallucinate that there's a magic parameter that does exactly what you want. It will never tell you "Nope, can't be done, you have to restructure around doing it this other way", unless you basically figure it out yourself and prompt it into doing so.

[–] 30_to_50_Feral_PAWGs@hexbear.net 5 points 4 days ago (2 children)

in more niche problems, it will often hallucinate that there's a magic parameter that does exactly what you want. It will never tell you "Nope, can't be done, you have to restructure around doing it this other way"

This was why, in spite of it all, I had a brief glimmer of hope for DeepSeek -- it's designed to reveal both its sources and the process by which it reaches its regurgitated conclusions, since it was meant to be an open-source research aid rather than a proprietary black-box chatbot.

[–] Cadende@hexbear.net 6 points 4 days ago (1 children)

I have been meaning to try deepseek for a chuckle/to see what the hype is about. I have pretty much no drive to use AI instead of learning or doing the work myself, but I am willing to accept that, free of the shackles of capitalism, it might be useful and non-destructive technology for some applications in the future, and maybe it's a tiny glimpse towards that

[–] semioticbreakdown@hexbear.net 2 points 3 days ago

make weird prompts and get it to do weird outputs, it's kind of fun. I put in such a strange prompt that I got it to say something suuuuper reddity like "le bacon is epic xD" completely unprompted. I think my prompt involved trying to make the chatbot's role like an unhinged quirky catgirl or something. unfortunately this tends to break the writing model pretty quickly and it starts injecting structural tokens back into the stream but its very funny

[–] AtmosphericRiversCuomo@hexbear.net 3 points 4 days ago (1 children)

Anthropic's latest research shows the chain of thought reasoning shown isn't trustworthy anyway. It's for our benefit, and doesn't match the actual reasoning used internally 1:1.

[–] semioticbreakdown@hexbear.net 5 points 3 days ago (1 children)

thank you anthropic for letting everyone know your product is hot garbage

crank theoryI dont even think LLMs are reasoning in chain-of-thought, because they arent a cognitive model at all, just a writing model. By forcing the model to write out all the intermediate steps of a computation the model's sign-interpretation ability allows it to probabilistically choose a more correct result without any sort of cognition or actual "reasoning" as we would think of it happening. This is the reason CoT is both untrustworthy and easily added to existing models of LLMs by CoT prompting. Its using the CoT examples of the prompt to produce output in such a way that significantly reduces the chances that it will hallucinate wrongly. Its non-cognitive and still suffers from the ability to hallucinate, and this will happen as long as it isnt meta-aware of what its doing. I think LLMs are basically a solved problem. Great job. You made a really good model of language itself. Maybe move on??

[–] AtmosphericRiversCuomo@hexbear.net 2 points 3 days ago (1 children)

As you say, hallucinating can be solved by adding meta-awareness. Seems likely to me we'll be able to patch the problem eventually. We're just starting to understand why these models hallucinate in the first place.

[–] semioticbreakdown@hexbear.net 2 points 3 days ago

I dont think hallucination is that poorly understood, tbh. Its related to the grounding problem to an extent, but also a result of the fact that its a non-cognitive generative model. Youre just sampling from a distribution. Its considered "wrong output" because to us, truth is obvious. But if you look at generative models beyond language models, the universality of this behavior is obvious. You cannot have the ability to make a picture of minion JD vance without LLMs hallucinating (or the ability to have creative writing, for a same-domain analogy). You can see it in humans too in things like wrong words or word salads/verbal diarrhea/certain aphasias. Language function is also preserved in instances even when logical ability damaged. With no way to re-examine and make judgements about its output, and also no relation to reality (or some version of it), the unconstrained output of the generative process is inherently untrustworthy. That is to say - all LLM output is hallucination, and only has relation to the real when interpreted by the user. Massive amounts of training data are used to "bake" the model such that the likelihood of producing text that we would consider "True" is better than random (or pretty high in some cases). This extends to the math realm too, and is likely why CoT improves apparent reasoning so dramatically (And also likely why CoT reasoning only works when a model is of sufficient size). They are just dreams, and only gain meaning through our own interpretation. They do not reflect reality.

REALLY crank thoughtsits more than just a patch tbh, its a radical difference in structure as compared to LLMs. the language model becomes a language module. Also, it doesnt solve the grounding problem - the only thing that can do that is multi-modality, the network needs to have a coherent representational system that is also related to the real in some way. further question: if such a system of meta-awareness is responsible for p-consciousness in humans, would incorporating it into an AI system also provide p-consciousness? Is it inherently impossible to create the desired artificial intelligence systems without imbuing them with subjective experience? I'm beginning to suspect that might be the case

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