> Yes, large language models (LLMs) are not actually AI in that they are not actually intelligent, but we’re going to use the common nomenclature here.
I'm sorry for the off-topic comment, but why do I keep seeing this? What am I missing here – is it that some people define intelligence as >= human, or that LLM are not intelligence because they're *just* statistical models?
It's a way for the author to distinguish himself as one who is neither a purveyor of, nor fooled by, the magic, grift, and cringy sci-fi fantasizing that currently comprises the majority of AI discussion.
Currently, most mentions of AI, outside of a proper technical discussion, are coming from crypto-tier grifters and starry-eyed suckers. Even further, a lot of discussions from otherwise technical people are sci-fi-tier fearmongering about some ostensible Skynet, or something, it's not quite clear, but it's clearly quite cringe. The latter is one of the many calibers of ammunition being used by AI incumbents to dig regulatory moats for themselves.
Anyway, I understand why the author is distinguishing himself with his LLM...AI disclaimer, given the above.
In my field it's accepted (by some) that you write "AI" for your grant proposal and say "ML" when you talk to colleagues and want to be taken seriously.
It feels a bit wrong to me, because as you say it's arguably a grift, in this case on the taxpayer who funds science grants. More charitably it might just be the applicant admitting that they have no idea what they are doing, and the funding agency seeing this as a good chance to explore the unknown.
Still, unless the field is AI research (mine isn't) it seems like funding agencies should giving money to people who understand their tools.
Most people outside of academia understand AI to include way more than just ML. People refer to the bots in video games as AI and they are probably a few hundred lines of straightforward code.
I don't think there is anything wrong with using the colloquial definition of the term when communicating with funding agencies/the public.
My assignments (different book) for Intro to AI class were:
Boolean algebra simplifier. Given a LISP expression - for example (AND A (OR C D)) write a function to return the variables needed to make the entire expression TRUE. Return NIL if the expression is a paradox such as (AND A (NOT A)). The expressions that we were to resolve had on the order of 100-200 operators and were deeply nested. I recall that I wrote a function as part of it that I called HAMLET-P that identified terms of the form (OR 2B (NOT 2B)) and rapidly simplified them to TRUE.
Not-brute-force job scheduler. The job-shop scheduling problem ( https://en.wikipedia.org/wiki/Job-shop_scheduling ) with in order processing of multiple tasks that had dependencies. Any worker could do any task but could only do one task at a time.
The third one I don't remember what it was. I know it was there since the class had four assignments... (digging... must have been something with Prolog)
The last assignment was written in any language (I did it in C++ having had enough of LISP and I had a good model for how to do it in my head in C++). A 19,19,5 game ( https://en.wikipedia.org/wiki/M,n,k-game ). Similar to go-maku or pente. This didn't have any constraints that go-maku has or captures that pente has. It was to use a two ply min-max tree with alpha beta pruning. It would beat me 7 out of 10 times. I could get a draw 2 out of 10 and win 1 out of 10. For fun I also learned ncurses and made it so that I could play the game with the arrow keys rather than as '10,9... oh crap, I meant 9,10'.
And I still consider all of those problems and homework assignments as "AI".
From the digging, I found a later year of the class that I took. They added a bit of neural nets in it, but other topics were still there.
I agree that using a colloquial definition is fine. And I don't mean to be too harsh on people who use buzzwords in their grant proposal: it's just sort of the sea you swim in.
But I only wish we could say that a few hundred lines of code was "AI": that would mean funding for a lot of desperately needed software infrastructure. Instead AI is taken as synonymous with ML, and more specifically deep neural networks, for the most part.
I think there’s nuance to be had here. Terms have been overloaded, and individuals aren’t necessarily acting in bad faith. ML can be considered to be a subset of AI.
That being said, ML is extremely boring to me, and I really do think a lot of the research is an enormous grift. Hop on the bandwagon, read a stats book, flagrantly plagiarize it, submit to CS journal that no statisticians read, publish and don’t perish, rinse, repeat.
It feels like society has spent billions of dollars on bad academics continuously reinventing applied statistics over and over again, but now with Big Data and a brand refresh! It’s like a whole generation of academics watched one too many terrible Hollywood remakes. It broke their brains, and now they’re only doing remakes too.
They ran out of statistics content to steal, so now the latest and greatest thing is plagiarizing classical AI works from the late 20th century and calling it “reinforcement learning.”
It’s all very frustrating. We could’ve funded a Manhattan project for fusion power, but instead thousands of our most brilliant people are wasting their time and humanity’s carbon budget to create the most powerful spambot ever.
I think you're entirely wrong about this. Using the term AI or artificial intelligence directly invokes several centuries of cultural baggage about golems, robots, Terminators, androids and cyborgs and Matrix-squid.
Saying "large language models" does not. Saying "giant correlation networks" does not. Not to be too Sapir-Whorfian, but the terminology we use influences our conversations: terrorists, guerillas, rebels, revolutionaries, freedom-fighters.
If you choose your words carelessly, you get unintended results.
Telling me about the AI in your HR system that hunts for the best candidates brings along the cultural context of stories about AI. Telling me about the rules engine that ranks incoming CVs does not.
"terrorists, guerillas, rebels, revolutionaries, freedom-fighters" are all the same group of people being referred to in different ways depending on how the speaker wants you to feel about them. Once you start using a particular word, you adopt the same viewpoint.
"AI" is too loaded with cultural contexts which will cause people to make mistakes.
If you pull up the TOC for an AI textbook, you'll find lots of things that aren't "intelligent". Machine learning is just a subset of it. I recall a professor in the AI department back in the 90s working on describing the shape of an object from a photograph (image to text) based on a number of tools (edge detection was one paper I recall).
Also in AI is writing a deductive first order logic solver is covered in there as are min-max trees and constraint satisfaction problems.
People are trying to put a box around "AI" to mean a particular thing - maybe they want AI to mean "artificial general intelligence" rather than all the things that are covered in the intro to AI class in college.
I ultimately believe that trying to use a term that has been very broad for decades to apply to only a small subset of the domain is going to end up being a fruitless Scotsman tilting at windmills.
Very clever people have located true intelligence in the gaps between what an machine can do and what a human can. Therefore, to show that you aren’t a starry-eyed rube you put a disclaimer that you aren’t really talking about intelligence, but something that just looks and acts like it.
True intelligence is, of course, definitionally the ability to do things like art or… err, wait, sorry, I haven’t checked recently, where have we put the goalposts nowadays?
I’m hesitant to even call this moving the goal posts. Intelligence has never been solidly defined even within humans (see: IQ debate; book smart vs street smart; idiot savants).
It’s unsurprising that creating machines that seem to do some stuff very intelligently and some other things not very intelligently at all is causing some discontent with regard to our language.
I see a whole lot more gnashing of teeth about goalposts moving than I do about people proposing actual solid goalposts.
> I’m hesitant to even call this moving the goal posts. Intelligence has never been solidly defined even within humans (see: IQ debate; book smart vs street smart; idiot savants).
> It’s unsurprising that creating machines that seem to do some stuff very intelligently and some other things not very intelligently at all is causing some discontent with regard to our language.
I think I agree about the language.
I don’t have a definition of intelligence. I don’t work in one of those fields that would need to define it, so my first attempt probably wouldn’t be very good, but I’d say intelligence isn’t a single thing, but a label we’ve arbitrarily applied to a bunch of behaviors that are loosely related at best. So, trying to say this thing is intelligent, this thing is not, is basically hopeless, especially when things that we don’t believe are intelligent are being made to exhibit those behaviors, one behavior at a time.
> I see a whole lot more gnashing of teeth about goalposts moving than I do about people proposing actual solid goalposts.
I might not see a ton of explicit “here are the goalpost” type statements. But, every time someone says “I’m using the term AI, but actually of course this isn’t intelligence,” the seem to me at least to be referencing some implicit goalposts. If there isn’t a way of classifying what is or isn’t intelligent, how can they say something isn’t it? I think the people making the distinction have the responsibility to tell us where they’ve made the cutoff.
Maybe I’m just quibbling. Now that I’ve written all that out, I’m beginning to wonder if I just don’t like the wording of the disclaimer. I’d probably be satisfied if instead of “this isn’t intelligence, but I’m going to call it AI,” people would say “Intelligence is too hard to define, so I’m going to call this AI, because why not?”
Conceptually Speaking you can reduce it down to Intelligence and strip out the Artificial Label.
So know the question is what is Intelligence. Our standardized testing Model tells us passing tests that Humans cannot would be considered intelligent.
Then add back in artificial to complete the equation.
Commercially the Term Ai Means nothing thanks to years of Machine Learning being labeled such. It's arbitrary and relays more to Group Think to avoid approaching that Intelligence is a Scalar Value and not a Binary Construct.
I say we take the word intelligence and throw it out the window. It's a bit like talking about the either before we discovered more about physics. We chose a word with an ethereal definition that may or may not apply depending on the context.
So what do we do instead? We define sets of capability and context and devise tests around that. If it turns out a test actually sucked or was not expansive enough, we don't get rid of that particular test. Instead we make a new more advanced test with better coverage. Under this domain no human would pass all the tests either. We could each individual sub test with ratings like 'far below human capability', 'average human capability', 'far beyond human capabilities'. These tests could be everywhere from emotional understanding and comprehension, to reasoning and logical ability, and even include embodiment tests.
Of course even then I see a day where some embodied robot beats the vast majority of emotional, intellectual, and physical tests and some human supremacist still comes back with "iTs n0t InTeLLigeNt"
Ooh, this is a rare one! A comment directly noting the similarities between AI art with photography, but insisting both aren't art. You're in very historical company: https://daily.jstor.org/when-photography-was-not-art/
>Photography couldn’t qualify as an art in its own right, the explanation went, because it lacked “something beyond mere mechanism at the bottom of it.”
That has nothing to do with the technology, that has everything to do with the quality.
Is it art if I take a picture with the cap on? No. Is it art if I take a picture of a tan colored wall? No.
Is it art if I set up something beautiful and take a picture. Its closer to art than the previous few examples.
If I write a prompt that says: "a green bedroom with art work on the walls", to be inspired, that still isnt trying to be art.
There's long been a divide between what people call hard vs soft AI, or strong vs weak AI, or narrow vs general. The definitions are a bit fuzzy, but generally a hard AI or strong AI would be able to think for itself, develop strategies and skills, maybe have a sense of self. Soft AI in contrast is a mere tool where you put something in and get something out.
Now some people don't like using the term AI for soft/weak/narrow AI, because it's a fleeting definition, mostly applied to things that are novel and that we didn't think computers were able to do. Playing chess used to be considered AI, but a short time after AI beat the human chess world master it was no longer considered AI. If you buy a chess computer capable of beating Magnus Carlsen today that's considered a clever algorithm, no longer AI. You see the same thing playing out in real time right now with LLMs, where they go from AI to "just algorithms" in record time.
Yeah this is exactly it. It’s interesting seeing a precision-oriented discipline (engineering) running into the inherently very, very muddy world of semantics.
“What do you mean it’s not intelligent?! It passed Test X!”
“Yes and now that tells us Test X was not a good test for whatever it is we refer to as ‘intelligence’”
Are you intelligent or just a bunch of cells? Given that I can query it for all sorts of information that I don’t know, I would consider LLMs to, at the very least, contain and present intelligence…artificially.
I can query Wikipedia or IMDB for all sorts of information I don't know. I wouldn't consider the search box of either site to be "intelligent", so I don't know "query it for all sorts of information" is a generally good rubric for intelligence.
How do we know that the brain is a statistical model of the world? It sounds like explaining an unknown phenomenon using the technology du jour - just 10/20 years ago, the brain was a computer.
This touches on a dichotomy that has fascinated me for decades, from the very beginning of my interest in AI.
One side of the dichotomy asserts that "if it walks like a duck..." that is, if a computer appears to be intelligent to us, then it must be intelligent. This is basically the Turing Test crowd (even though Turing himself didn't approve of the Turing Test as an actual test of AI).
On the other side, you have people who assert that the human mind is really just a super-complicated version of "X", where "X" is whatever the cool new tech of the day is.
I have no conclusions to draw from this sort of thing, aside from highlighting that we don't know what intelligence or consciousness actually are. I'm just fascinated by it.
The general notion is called "lumpers" and "splitters".
From the perspective of software, the lumpers are pretty much always wrong except for when they get a lucky guess. Think of a pointy-haired boss who weaponizes his wishful thinking with a brutal dismissal of all implementation details and imposes ignorantly firm deadlines, or an architecture astronaut who writes and forces upon everyone cruel interfaces and classes that are thoroughly out of touch with reality.
As they say: "it's more easy to lump splits than split lumps". The people who insist the statistical models have emergent behavior, or even worse, equate them with human brains are "lumpers" who lack imagination and have no desire to truly understand and model these things. They naively seek out oversimplifications and falsely believe they're applying Occam's Razor, but they're actually just morons. "Splitters" are by their very definition always technically correct, but create complex distinctions that either represent much deeper knowledge than necessary, or hallucination. Either way, both types are needed, and of course, society values the lumpers far more for essentially playing the lottery with their reputations by telling people what they want to hear.
I wouldn't say the brain is magic, just that we still don't know what consciousness and intelligence is. Could the complex emergent behaviour we call intelligence emerge from a statistical model? Maybe. Can we gain more insights on what intelligence is by studying these models? Definitely. On the other hand — Are there limits to large language models' capabilities that we haven't reached yet?
I don’t think we know that. The point of my comment is to poke a bit at human exceptionalism. I think we’re going to see something that’s hard to deny is intelligent come out of a combination of a world model and an RL agent within the next decade. But I’m sure some will try to keep moving the goalposts.
Its interesting to see what it thinks about some ideas, like I ask, what 5 companies are best at marketing. My goal here is to be hypercritical of the companies it says because they are masters at manipulation. GPT3.5 was awful and confused advertising and marketing. GPT4 was perfect (Apple, Nike, Coke, Amazon, P&G)
As much as chatgpt doesnt want to give you answers because the fuzziness, it has the ability to make judgements on things like "This is the best" or "This is the worst".
In this examples, it likely took that those companies are often praised about their marketing in the same sentence marketing is mentioned.
LLMs don't repeat text its seen before, it links words/tokens/phrases that are related. Its prediction, but the prediction isnt just copypasting a previous webpage.
Have you use chatgpt yet? I wouldn't delay. Heck you are here on HN, you basically have a responsibility to test it.
Well, one clear thing about GPT4 that isn't intelligent is that it doesn't learn in situ. Knowledge has to be added to it via an external process. The prompt does allow it to condition further output based on "new" information but that isn't learning. Another thing GPT4 has trouble with is generalizing knowledge. While it is certainly able to generalize to a degree (more or less it is able to apply patterns in the training data from one domain to other domains) if you ask it to generalize to things not well represented in the training data but nevertheless obvious from the conceptual underpinnings thereof it fails. I see this frequently with complicated functional/function level programming. GPT4 gets hopelessly confused when you ask it about non-trivial functions which return or manipulate other functions, even though conceptually there is nothing confusing about it and, in fact, if you ask it about functions as first class objects, it can answer with reasonable text.
Thus, GPT4 can appear to have knowledge in the sense of generating text indicating such, but fail to use that knowledge. This is the most compelling indication to me of limited or total lack of intelligence. I believe that the vast majority of GPT4's "capabilities" amount to memorization and permutation, not the formulation of accurate models of things.
> is it that some people define intelligence as >= human
I just want to say that this seems to be how many, if not most people define intelligence internally. If an LLM gets something wrong or doesn't know something, then it must be completely unintelligent. (as if humans never get anything wrong!)
Clearly the test isn’t >= as ChatGPT is already more coherent than large swaths of the population. The AI test for some is that its intelligence >>> human intelligence. Which is funny because by that point in time, their opinion will be more than worthless.
What's concerning about this is we are evaluating AI on a basis that humans are not subject to. LLMs in their current form are built on the knowledge of the internet, while humans have both the internet and realtime feedback from their own lives in the physical world. If a human brain could be trained the same way as an LLM, might it also connect seemingly unconnected ideas in a way that would appear as non-thought? Maybe, maybe not. LLMs seem to be biased heavily towards making best effort guesses on things it doesn't know about, whilst humans are far more modest in doing so. I just don't know if we're really at a point where we can conclusively decide that something isn't thinking just because it doesn't appear to be thinking by the standards we place upon ourselves.
AI's a very soft term, and there's long been a technical vs "casual" split in what it means. Five or ten years ago you'd say your photo was retouched with AI dust removal, say, and we'd all know what that means. And that there was a big gulf between that and the sci-fi "AI" of Blade Runner or Her or Star Wars, etc.
The user interface to Chat GPT and similar tools, though, has made a lot of people think that gap is gone, and that instead of thinking they are using an AI tool in the technical sense, they now think they're talking to a full-fledged other being in the sci-fi sense; that that idea has now come true.
So a lot of people are careful to distinguish the one from the other in their writing.
I say that large language models are not intelligent because of the way they fail to do things. In particular, they fail in such a way as to indicate they have no mental model of the things they parrot. If you give them a simple, but very unusual, coding problem, they will confidently give you an incorrect solution even though they seem to understand programming when dealing with things similar to their training data.
An intelligent thing should easily generalize in these situations but LLMs fail to. I use GPT4 every day and I frequently encounter this kind of thing.
Is there a definition of intelligence that rules out large language models, but that does not also rule out large portions of humanity? A lot of people would readily admit that they don't have programming aptitude and would probably end up just memorizing things. Do we say those people are not intelligent?
It seems to me that the perceived difference is mostly in being able to admit that you don't know something, rather than make up an answer -- but making up an answer is still something that humans do sometimes.
I have to admit this is a genuinely interesting question. Language models demonstrably do have some models of the world inside of them. And, I admit, what I say that they aren't intelligent, I mostly mean they are very stupid, rather than like a machine or algorithm. Artificial stupidity is progress.
Ok, so from your other comment, I think this is where our definition of intelligence is breaking down...
Biological agents have a consistent world model based on their capabilities because an inconsistent model would lead to lack of reproduction or death. We could call this environmental intelligence.
Meanwhile we have LLMs that have appear to have what I would consider 'micro' world models for some things, but not a large consistent world model. I'm guessing this is due to a few things, but for example not being culled for bad world models would be one, and another is they are only grounded in text and we've not really explored multi-modal grounding in models very far.
I guess what's going to be interesting is to see how multi-modal and embodied models do as they are trained in the environment and create a more consistent world model.
I believe that the best way to understand these large language models is that they have models of patterns of text. To the extent that patterns of text are congruent with patterns in the world, they appear to function well, but I think, in the end, they are statistical models of text, not of the world, and that substantially limits their capabilities.
I do think multi-modal models will be interesting, but text is a very special sort of thing. It is widely available, semantically rich, and informationally pretty dense. I'm not sure there is such a nice set of properties for other modes. Consider that we have already almost reached training data exhaustion with text and it is, by far, the most voluminous/dense training mode there is.
> is it that some people define intelligence as >= human
Just like some people define stupid as <= them. Aptitude is a multivariate spectra. It is already hard to come up with a cutoff on a single measure, way harder to do so for a bunch of different skills that for some reason happen to correlate in humans (and sometimes they diverge wildly as in the case of savant syndrome).
I'm sorry for the off-topic comment, but why do I keep seeing this? What am I missing here – is it that some people define intelligence as >= human, or that LLM are not intelligence because they're *just* statistical models?