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I think this is the most valuable part of the article. It's the writing process itself, which isn't valued in schools.
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See System Reply, the Chinese Room is a pseudo problem begging the question rooted in nothing more than human exceptionalism. If you start with the assumption that humans are the only thing in the universe able to "understand" (whatever that means), then of course the room can't understand (except for every reasonable definition of "understanding" it does).
And System Reply, too, ignores the central problem: the man in the room does not understand Chinese.
To make it a little easier to understand:
* go read about the x86 instruction
* take an .exe file
* manually execute it with pen&paper
Do you think you understand what the .exe does? Do you think understanding the .exe is required to execute it?
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> LLMs are token predictors. And neural nets are "just" matrix multiplications, human brains are "just" chemical reactions
I don't know why you're quoting, or stressing, a word I didn't use. I once got into an argument with a date about the existence of God and the soul. She asked whether I really think we're "just" physical stuff. I told her, "no, I'm not saying we're 'just' physical stuff. I'm saying we're physical stuff." That 'just' is simply a statement of how she feels about the idea, not a criticism of what I was claiming. I don't…
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I don't know why you're quoting, or stressing, a word I didn't use. I once got into an argument with a date about the existence of God and the soul. She asked whether I really think we're "just" physical stuff. I told her, "no, I'm not saying we're 'just' physical stuff. I'm saying we're physical stuff." That 'just' is simply a statement of how she feels about the idea, not a criticism of what I was claiming. I don't…
“That’s all they do” and “just” are interchangeable. All you do is transferring electric charges across synapses, and all molecules do is bouncing off each other, but this reduction doesn’t explain neither thinking nor flying. In this case you’re saying “That’s all they do”, which means “just” to others. Don’t even try to argue, cause all I do is typing letters and you’ll only get more letters in response ;)
> “That’s all they do” and “just” are interchangeable
That's a valid point, thanks. I could have stated what I was saying more effectively. I just (edit: heh, "just.") meant that, definitionally, LLMs are token predictors, and saying so doesn't belittle them any more than it avoids some unpleasant reality; it is the reality, to the best of my understanding.
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> LLMs are token predictors. That's all they do. That's a disingenuous statement, since it implies there is a limit to what LLMs can do, when in reality an LLM is just a form of Universal Turing Machine[1] that can compute everything that is commutable. The "all they do" is literately everything we know to be doable. [1] Memory limits do apply as with any other form of real world computation.
I'll ignore the silly claim that I'm somehow dishonest or insincere. I like the way Pon-a put it elsewhere in this thread: > LLMs are a language calculator, yes, but don't share much with their analog. Natural language isn't a translation from input to output, it's a manifestation of thought. LLMs translate input to output. They are, indeed, calculators. If you don't already see that that's different from having a th…
And that's relevant exactly how? Do you think "thought and expression" are somehow uncomputable? Please throw science at that and collect your Nobel prize.
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Congratulations, you've successfully solved the Chinese Room problem by paving over and ignoring it.
I think the Chinese room is actually correct. The CUDA cores running a model don't understand anything, the neuron cells in our brain don't understand anything either. Where inteligence actually lies is in the process itself, the interactions of the entire chaotic system brought all together to create something more than the sum of its parts. Humans get continuous consciousness given our analog hardware, digital only…
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And System Reply, too, ignores the central problem: the man in the room does not understand Chinese.
That's only a "problem" if you assume human exceptionalism and begging the question. It's completely irrelevant to the actual problem. The human is just a cog in the machine, there is no reason to assume they would ever gain any understanding, as they are not the entity that is generating Chinese. To make it a little easier to understand: * go read about the x86 instruction * take an .exe file * manually execute it w…
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Welcome on hackernews, where thousands years old philosophical problems are solved through plain assertions!
Turns out we have technologies and experiences with technology which weren't possible until very recently. Some things just look very different in hindsight.
Philosophy of mind hasn't started nor ended with Dennett, and definitely not with AI hype manufacturers.
"I cannot emphasize this enough: ChatGPT is not generating meaning. It is arranging word patterns. I could tell GPT to add in an anomaly for the 1970s - like the girl looking at Billy’s Instagram - and it would introduce it into the text without a comment about being anomalous." I asked ChatGPT to introduce the girl looking at Billy's instagram. The response: "Instagram didn't exist in the 1970s. Do you want to keep…
It’s always baffling how people take a technology that wasn’t even thought as feasible a decade ago and try to dismiss it as trivial and stagnant. It’s pretty clear that LLMs have improved rapidly and have successfully become better writers than the majority of people alive. To try and present this as just random pattern matching seems as just a way to assuage fears of being replaced. It’s also amusing that people mi…
I’m conflicted about your comment. On one hand, I agree, useless reductions are boring. But on the other, we are living in the “overselling all up in your ears” epoch, which is known to sell pffts as badabooms. So it isn’t baffling to me that a new tech gets old quickly, because it’s not really what was advertised. Our decades-old ideas of AI weren’t feasible a decade ago, but neither are these now. Those who believe…
With that in mind, I still don't get the dismissal. LLMs are broadly accessible - ever since the first ChatGPT, anyone could easily get access to a SOTA LLM and evaluate it for free; even the limited number of requests on free tiers were then, and now are, sufficient to throw your own personal and professional problems at models and see how they do. Everyone can see for themselves this is not hot air - this is an unexpected technological breakthrough that's already overturning way people approach work, research and living, and it's not slowing down.
I'd say: ignore what the companies are selling you - especially those who are just building products on top of LLMs and promising pie in the sky. At this point in time, they aren't doing anything you couldn't do for yourself with ChatGPT or Claude access[0]. We are also beginning to map out the possibilities - two years since the field exploded is very little time. So in short, anything a business does, you could hack yourself - and any speculative idea for AI applications you can imagine, there's likely some research team working on it too. The field is moving both absurdly fast and absurdly slow[1]. So your own personal experience over applying LLMs to your own problems, and watching people around you do the same, is really all you need to tell whether LLMs are hot air or not.
My own perspective from doing that: it's not hot air. The layer of hype is thin, and in some areas the hype is downplaying the impact.
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[0] - Yes, obviously a bunch of full-time professionals are doing much more work than you or me over couple evenings of playing with ChatGPT. But they're building a marketable product, and 99% of work that goes into that is something you do not need to do, if you just want to replicate the core functionality for yourself.
[1] - I mean, Anthropic just published a report on how exposing "thinking" capability to the model in form of a tool call leads to improvement of performance. On the one hand, kudos to them for testing this properly and publishing. On the other hand, that this was something to do was stupidly obvious ever since 1) OpenAI introduced function calling and 2) people figured out "Let's think step by step" improves model performance - which was back in 2022[2]. It's as clear example as ever that both hype and productization lag behind what anyone paying attention can do themselves at home.