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LLM Inevitabilism

(tomrenner.com)
1611 points SwoopsFromAbove | 7 comments | | HN request time: 0.001s | source | bottom
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lsy ◴[] No.44568114[source]
I think two things can be true simultaneously:

1. LLMs are a new technology and it's hard to put the genie back in the bottle with that. It's difficult to imagine a future where they don't continue to exist in some form, with all the timesaving benefits and social issues that come with them.

2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner), and several that seemed poised to displace both old tech and labor but have settled into specific use cases (the microwave oven). Given the lack of a sufficiently profitable business model, it feels as likely as not that LLMs settle somewhere a little less remarkable, and hopefully less annoying, than today's almost universally disliked attempts to cram it everywhere.

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fendy3002 ◴[] No.44568145[source]
LLMs need significant optimization or we get significant improvement on computing power while keeping the energy cost the same. It's similar with smartphone, when at the start it's not feasible because of computing power, and now we have one that can rival 2000s notebooks.

LLMs is too trivial to be expensive

EDIT: I presented the statement wrongly. What I mean is the use case for LLM are trivial things, it shouldn't be expensive to operate

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1. lblume ◴[] No.44568319[source]
Imagine telling a person from five years ago that the programs that would basically solve NLP, perform better than experts at many tasks and are hard not to anthropomorphize accidentally are actually "trivial". Good luck with that.
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2. jrflowers ◴[] No.44568547[source]
>programs that would basically solve NLP

There is a load-bearing “basically” in this statement about the chat bots that just told me that the number of dogs granted forklift certification in 2023 is 8,472.

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3. clarinificator ◴[] No.44568655[source]
Yeah it solved NLP about 50% of the time, and also mangles data badly and in often hard-to-detect ways.
4. lblume ◴[] No.44568800[source]
Sure, maybe solving NLP is too great a claim to make. It is still not at all ordinary that beforehand we could not solve referential questions algorithmically, that we could not extract information from plain text into custom schemas of structured data, and context-aware mechanical translation was really unheard of. Nowadays LLMs can do most of these tasks better than most humans in most scenarios. Many NLP questions at least I find interesting reduce to questions of the explanability of LLMs.
5. Applejinx ◴[] No.44569792[source]
"hard not to anthropomorphize accidentally' is a you problem.

I'm unhappy every time I look in my inbox, as it's a constant reminder there are people (increasingly, scripts and LLMs!) prepared to straight-up lie to me if it means they can take my money or get me to click on a link that's a trap.

Are you anthropomorphizing that, too? You're not gonna last a day.

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6. lblume ◴[] No.44570331[source]
I didn't mean typical chatbot output, these are luckily still fairly recognizable due to stylistic preferences learned during fine-tuning. I mean actual base model output. Take a SOTA base model and give it the first two paragraphs of some longer text you wrote, and I would bet on many people being unable to distinguish your continuation from the model's autoregressive guesses.
7. hyperbovine ◴[] No.44575415[source]
It still doesn't pass the Turing test, and is not close. Five years ago me would be impressed but still adamant that this is not AI, nor is it on the path to AI.