Whether LLMs are "intelligent" seems a wholly uninteresting distinction, resembling the internet ceremony surrounding whether a hotdog is a sandwich.
There's probably very interesting discussion to be had about hotdogs and LLMs, but whether they're sandwiches or intelligent isn't a useful proxy to them.
I disagree completely. Many people take for granted that the expression of intelligence/competence is the same as actual intelligence/competence, and many people are acting accordingly. But a simulacrum is definitively NOT the thing itself. When you trust fake intelligence, especially as a way to indulge mental laziness, your own faculties atrophy, and then in short order you can't even tell the difference between a real intelligence bomb and a dumb empty shell that has the word "intelligent" written on it.
I'm not even taking for granted what it means. Can you define it in a way that your neighbor will independently arrive at? It's an incredibly lossy container for whatever meaning that people will want to pack it with, moreso than for other words.
Is a hotdog a simulacrum of a sandwich? Or a fake sandwich? I have no clue and don't care because it doesn't meaningfully inform me of the utility of the thing.
An LLM might be "unintelligent" but I can't model what you think the consequences of that are. I'd skip the formalities and just talk about those instead.
And people will keep ignoring Stallman at their peril. But if you understand how the technology works, you also know he's right. If you think he isn't, you either don't understand or you don't _want_ to understand because your job depends on it.
"people should not trust systems that mindlessly play with words to be correct in what those words mean"
Yes, but this applies to any media channel or just other human minds. It's an admonition to think critically about all incoming signals.
"users cannot get a copy of it"
Can't get a copy of my interlocutor's mind, either, for careful verification. Shall I retreat to my offline cave and ruminate deeply with only my own thoughts and perhaps a parrot?
>you also know he's right. If you think he isn't, you either don't understand or you don't _want_ to understand because your job depends on it.
> Yes, but this applies to any media channel or just other human minds.
You can hold a person responsible, first and foremost. But I am so tired of this strawman argument; it's unfalsifiable but also stupid because if you interact with real people, you immediately know the difference between people and these language models. And if you can't I feel sorry for you, because that's more than likely a mental illness.
So no I can't "prove" that people aren't also just statistical probability machines and that every time you ask someone to explain their thought process they're not just bullshitting, because no, I can't know what goes on in their brain nor measure it. And some people do bullshit. But I operate in the real world with real people every day and if they _are_ just biological statistical probability machines, then they're a _heck_ of a lot more advanced than the synthetic variety. So much so that I consider them wholly different, akin to the difference between a simple circuit with a single switch vs. the SoC of a modern smartphone.
I don’t have the luxury of listening to him. I would be much less effective at my job compared to my competitors in the job market if I didn’t use ChatGPT, regardless of whether it’s open source software or meets his definition of intelligence.
From my understanding what Stallman says is that LLMs don't "understand" what they're saying. They do a probabilistic search of the most appropriate letter (say) that has had come after another letter in the text (or any media) they have been trained on, and they place it similar in resemblance in the text that they produce. This is largely (no pun) dependent on existing data that is there in the world today, and the more the data that LLMs can work through, the better they get at predicting. (Hence the big data center shops today.)
But the limitation is that it cannot "imagine" (as in "imagination is more important than knowledge" by Einstein, who worked on a knowledge problem using imagination, but with the same knowledge resources as his peers.) In this video [1], Stallman talks about his machine trying to understand the "phenomenon" of a physical mechanism, which enables it to "deduce" next steps. I suppose he means it was not doing a probabilistic search on a large dataset to know what should have come next (which makes it human-knowledge dependent), essentially rendering it to an advanced search engine but not AI.
Interesting points! Maybe a better term is LLMs (BTW smart phones are not smart and people don’t seem to be confused). I agree with being dependent and sending so much data to those servers. I would mention there is a version of ChatGPT you can run locally[1].
It doesn't understand anything. Yet if you prompt it with a question about what it understands, its output is consistent with something that understands.
Text in, text out. The question is how much a sequence of tokens captures what we think a mind is. "It" ceases to exist when we stop giving it a prompt, if "it" even exists. Whether you consider something "AI" says more about what you think a mind is than anything about the software.
Consciousness, in Zoltan Torey's[1] model, is the brain's layered, language-enabled off-line mechanism that reflects on its own sensory endogram, generating self-aware, internally guided behavior.[2] The off-line mechanism generates mental alternatives, which are then "run past the brainstem, which then makes the selection." Nice little accessible book.[3]
> Taking “computer” first, we find that this alleged
source of machine-generated consciousness is not what it
is cracked up to be. It is a mere effigy, an entity in name
only. It is no more than a cleverly crafted artifact, one essentially indistinguishable from the raw material out of
which it is manufactured.[2]
Another day, another example of the AI Effect in action:
> "The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements.[4][2][1] Edward Geist credits John McCarthy for coining the term "AI effect" to describe this phenomenon.[4] The earliest known expression of this notion (as identified by Quote Investigator) is a statement from 1971, "AI is a collective name for problems which we do not yet know how to solve properly by computer", attributed to computer scientist Bertram Raphael.[5]
> McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the 'failures', the tough nuts that couldn't yet be cracked."[6] It is an example of moving the goalposts.[7]
I wonder how many more times I'll have to link this page until people stop repeating it.
Leaving alone Stallman's extreme take, present day LLMs and other generative systems are absolutely still being referred to by society as AI, and I don't see this changing any time soon, so what does this say about the AI effect?
Richard makes a distinction between human understanding and AI indifference to truth. But isn't that what half the country is doing a.t.m? And more philosophically, we can't know the Truth because we rely on leaky abstractions all the way.
AI models are subject to user satisfaction and sustained usage, the models also have a need to justify their existence, not just us. They are not that "indifferent", after multiple iterations the external requirement becomes internalized goal. Cost is the key - it costs to live, and it costs to execute AI. Cost becomes valence.
I see it like a river - water carves the banks, and banks channel the water, you can't explain one without the other, in isolation. So are external constraints and internal goals.
Readit News
There's probably very interesting discussion to be had about hotdogs and LLMs, but whether they're sandwiches or intelligent isn't a useful proxy to them.
Is a hotdog a simulacrum of a sandwich? Or a fake sandwich? I have no clue and don't care because it doesn't meaningfully inform me of the utility of the thing.
An LLM might be "unintelligent" but I can't model what you think the consequences of that are. I'd skip the formalities and just talk about those instead.
Deleted Comment
"people should not trust systems that mindlessly play with words to be correct in what those words mean"
Yes, but this applies to any media channel or just other human minds. It's an admonition to think critically about all incoming signals.
"users cannot get a copy of it"
Can't get a copy of my interlocutor's mind, either, for careful verification. Shall I retreat to my offline cave and ruminate deeply with only my own thoughts and perhaps a parrot?
>you also know he's right. If you think he isn't, you either don't understand or you don't _want_ to understand because your job depends on it.
He can't keep getting away with this!
You can hold a person responsible, first and foremost. But I am so tired of this strawman argument; it's unfalsifiable but also stupid because if you interact with real people, you immediately know the difference between people and these language models. And if you can't I feel sorry for you, because that's more than likely a mental illness.
So no I can't "prove" that people aren't also just statistical probability machines and that every time you ask someone to explain their thought process they're not just bullshitting, because no, I can't know what goes on in their brain nor measure it. And some people do bullshit. But I operate in the real world with real people every day and if they _are_ just biological statistical probability machines, then they're a _heck_ of a lot more advanced than the synthetic variety. So much so that I consider them wholly different, akin to the difference between a simple circuit with a single switch vs. the SoC of a modern smartphone.
But the limitation is that it cannot "imagine" (as in "imagination is more important than knowledge" by Einstein, who worked on a knowledge problem using imagination, but with the same knowledge resources as his peers.) In this video [1], Stallman talks about his machine trying to understand the "phenomenon" of a physical mechanism, which enables it to "deduce" next steps. I suppose he means it was not doing a probabilistic search on a large dataset to know what should have come next (which makes it human-knowledge dependent), essentially rendering it to an advanced search engine but not AI.
[1] https://youtu.be/V6c7GtVtiGc?si=fhkG2ZA-nsQgrVwm
[1] https://openai.com/index/introducing-gpt-oss/
Text in, text out. The question is how much a sequence of tokens captures what we think a mind is. "It" ceases to exist when we stop giving it a prompt, if "it" even exists. Whether you consider something "AI" says more about what you think a mind is than anything about the software.
> Taking “computer” first, we find that this alleged source of machine-generated consciousness is not what it is cracked up to be. It is a mere effigy, an entity in name only. It is no more than a cleverly crafted artifact, one essentially indistinguishable from the raw material out of which it is manufactured.[2]
[1] https://en.wikipedia.org/wiki/Zoltan_Torey
[2] https://mitpress.mit.edu/9780262527101/the-conscious-mind/
[3] https://search.worldcat.org/title/887744728
> "The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements.[4][2][1] Edward Geist credits John McCarthy for coining the term "AI effect" to describe this phenomenon.[4] The earliest known expression of this notion (as identified by Quote Investigator) is a statement from 1971, "AI is a collective name for problems which we do not yet know how to solve properly by computer", attributed to computer scientist Bertram Raphael.[5]
> McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the 'failures', the tough nuts that couldn't yet be cracked."[6] It is an example of moving the goalposts.[7]
I wonder how many more times I'll have to link this page until people stop repeating it.
[0] https://en.wikipedia.org/wiki/AI_effect
AI models are subject to user satisfaction and sustained usage, the models also have a need to justify their existence, not just us. They are not that "indifferent", after multiple iterations the external requirement becomes internalized goal. Cost is the key - it costs to live, and it costs to execute AI. Cost becomes valence.
I see it like a river - water carves the banks, and banks channel the water, you can't explain one without the other, in isolation. So are external constraints and internal goals.