We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are.
LLM-based “AI” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credibility massively when your LLM-based “AI” gets the probabilities wrong at best, or in this case, claims a line of code generates a vulnerability when it is, in fact, a code comment.
LLMs are text-prediction engines. They are not Artificial Intelligence, and shouldn’t not be treated in any form or fashion as if they possess intelligence. What bothers me about this entire situation is that presumably the folks that relied on the LLM-based “AI” to generate these vulnerabilities knew (or should have known) enough about their tool to know this would happen, but did not.
Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this usage of “AI”.
A human must verify everything an LLM presents as fact. Everything. If you don’t, we all pay the price. LLMs do not remove the onus of responsibility on the human being, if anything they amplify it because LLMs can generate lots more output more quickly that needs to be verified than humans can.
Many people with no skills are taking advantage of the LLM craze to artificially inflate their own value. I see it every day on LinkedIn.
People that previously have barely any experience in tech, now being hired in AI startups because they are good bullshitters.
Countless directors and managers are now cosplaying as engineers. I've seen so many myself and that's just my tiny slice of this engineering world
It is pretty funny to see the shoe on the other foot, since it's usually software engineers with unearned arrogance about other fields.
What fields do you see devs think they know about? I’ve never personally seen this with other devs I work with but obviously small sample size…
In my experience, product. There’s a pervasive view that product is easy, that anyone working in it is a dunderhead who gets in the way of engineers doing the really worthwhile work. In reality, it would just mean they rewrite the software five times and have zero customers.
Oh, design too. I’ve met many engineers who simply don’t see any value in design.
This. I'm a former teacher. I left to get a PhD in educational psychology, and now I'm a data scientist for a large school division. The number of ed tech companies I interact with who clearly have little grasp of research-based best practices in education is staggering. But they can all build slick-looking websites and dashboards.
find any econ related post on hn et voila
I don't think it is much of a problem that dabblers get into Internet arguments about things they are not experts in. After all the easiest way to get a correct answer/learn something is to say something wrong.
Of course this should be differentiated from a professional setting.
And under no circumstances should we put that stuff into the knowledge bases we base our "AI" on without making sure it takes into account the context.
There have always been people with no tech experience building startups generating spaghetti code. It's just that now it looks a lot more polished.
> It's just that now it looks a lot more polished.
Yes, and that is the problem. It used to be if a product looked polished it was fairly polished engineering wise as well if we compare to todays AI slop. You can see that on steam, before AI slop games a game that looked polished mostly worked. But today you can get a game that looks like they put in a lot of effort, but its all AI slop and everything is flaky and broken, I've had way more such experiences the last few months than before that.
A human coder that was capable of coding a complex game typically also was good enough and tested things to make most things work. There were bugs etc, but at least buttons did things.
Engineers cosplay as physicists and mathematicians every day. What's your point? Think of it pragmatically. If they can do the job they can do the role.
> If they can do the job they can do the role.
Obviously.
Can they do the job? Because right now, government decisions are based on AI generated code, which was verified by nobody who can do that. So the cost of an unsatisfactory answer is quite high.
To be fair, "people with no skills inflating their own value" is what LinkedIn has always been like. But I guess LLMs are uniquely well positioned for that task.
LLMs have brought it to a new level. I have multiple people in my feed posting several page dissertations on a daily basis. More often than not, these posts are about the benefits of AI...
People who live and create on LinkedIn? absolutely. For a huge group of people LI was just the most popular job board, though.
I heard the term "Sloperator" a few times. Quite fitting...
> People that previously have barely any experience in tech, now being hired in AI startups because they are good bullshitters.
I like the symmetry of people being unable to detect "skill" in people who are great at manipulating language and in LLMs who are also great at manipulating language.
This isn't new due to AI, just amplified.
> A human must verify everything an LLM presents as fact. Everything. If you don’t, we all pay the price. LLMs do not remove the onus of responsibility on the human being, if anything they amplify it because LLMs can generate lots more output more quickly that needs to be verified than humans can.
The sort of person that's going to offload their thinking to AI is the exact sort of person that is not going to verify anything because they've already offloaded their thinking to AI.
For a long time I was anti-licensure in tech; now with the bar being lowered to next to nothing, it seems as if licensure is more important than ever — not to protect this trade (though it will do that, and that is a benefit), but because the sheer amount of irresponsibility in the usage of LLMs and “AI” in general begs for licensure and adoption of a regulatory body for software in general.
This is unfortunately a feeling I share. It wasn't until LLMs have become nearly ubiquitous at this point, and there has been zero realistic technological response to the dangers they present. Not to mention I suspect there may be some psychological element to being exposed to interactions with AI models and their nonsense for hours a day. Not all of it is nonsense....but you won't ever know for sure.
Unfortunately people sometimes get defensive against this take. But I think treating the LLM as you described can make you a better LLM user and help get better output. It helps understand the failure modes better, and moderate one’s reliance on them. Just like how we should do for every tool we work with.
Yes, I've found that reminding yourself of how they actually work helps keep you on guard against LLM-patterned mistakes. Especially things like carefully considering what parts of the current task likely fall outside the distribution of corpus + RL data (as much as that can be guessed).
> ...Unfortunately people sometimes get defensive against this take. But I think treating the LLM as you described can make you a better LLM user and help get better output. It helps understand the failure modes better, and moderate one’s reliance on them. Just like how we should do for every tool we work with.
B...b...but the Anthropic trainer said we'd get the best results if we don't think of it as a tool, but instead give it a name and think of it as our brilliant coworker!
Why should I trust you, internet rando over a stormtrooper-level salesman? /s
> Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this usage of “AI”.
Brandolini's principle in action. It takes 10 times more energy to refute BS than to generate it. A related analogy to computing: it is easy to generate propositions, but hard to test if a given proposition is satisfiable or not, which curiously ties to P vs NP.
> Brandolini's principle
I much prefer the alternative name: the Bullshit Asymmetry Principle.
Seems like most of it is covered by: Entropy increases.
Apophatic intelligence? "We don't know what intelligence is, but LLMs with CoT are certainly not it despite being Turing-complete."
Watching for unexpected failure modes is surely worth it.
Turing-completeness is a necessary pre-requisite for being able to fulfill the requirements of a Turing machine, nothing more. In the same way that cell division is a necessary condition for life, but cell division does not mean a given life form itself is sentient.
Intelligent life-forms can generate probabilistic outputs based on inputs, but being able to generate probabilistic outputs based on inputs is not what makes us intelligent.
OK. A more pointed question. What do you know about intelligence that allows you to exclude LLMs with CoT from the category of intelligent systems with certainty?
How do you know there's not a teapot orbiting the sun?
There is no reason to think that teapots were sent into orbit or spontaneously formed there.
Likewise, there is no reason to think the brain employs super-Turing or quantum computations that cannot be approximated by LLMs.
The point is that you're asking someone to prove a negative.
Right, it follows naturally that one can never meaningfully discuss that which can only be settled by proving negatives. Hence the total lack of debate on religious topics, as everyone just gave up 1500 years ago and decided that God indeed may or may not exist and that's that.
It's a negative in your worldview where "intelligence can't be a computation" is an axiom or something close. To me this worldview is sloppy thinking.
I did not express my opinion or "worldview." I was simply responding to this:
> exclude LLMs with CoT from the category of intelligent systems with certainty
Why do you think that intelligence of a Turing-complete system trained on human data can be likened to a Russell's teapot?
At least, don't you think that the recent mathematical results of LLMs are a bit like a glimpse of something teapot-shaped in the orbit? (which makes it not a Russell's teapot, which. by definition, can't be observed).
To me, it's an expected progression of ANNs' approximation of human cognitive processes. The universal approximation theorem guaranties the existence of such ANNs barring the super-Turing or quantum superiority of the brain.
> Why do you think that intelligence of a Turing-complete system trained on human data can be likened to a Russell's teapot?
I don't? You are presenting opinions as if they are mine, but they are not.
To my knowledge there are many millions of teapots orbiting the sun.
No, they're all orbiting the Earth very badly. :p
You’re right to point that out. The load-bearing seam of your logical retort is the hidden assumption that the teapot is in outer space, not at peace on a kitchen stovetop here on earth. I am sorry I had not considered this fact. \s
They were modeled after us, which almost certainly dooms them to stupidity.
They could have been great, if trained on datasets from a more sensible species.
> but being able to generate probabilistic outputs based on inputs is not what makes us intelligent.
???
Of course it is. The brain is mechanically not capable of doing anything other than that.
Do you believe the brain is something other than a bundle of probabilistic physical interactions? Or are brains not the source of what we call intelligence?
May I suggest the excellent & recent book: "The brain. In theory" by Romain Brette?
This is going to elevate your thinking on this no end, if you're interested.
Looks interesting (added to my list, thank you for the recommendation), but not actually relevant to the topic at hand.
We know that the brain is a probabilistic input → output machine because the universe is a probabilistic input → output machine. The brain is made of universe. There are deterministic relationships (which at high sensitivity or complexity become easier to describe as probabilistic), and quantum relationships. That's it. The brain, like every other thing comprised of "universe" is comprised of those two types of relationships.
If Romain's book provides evidence of relationships in the brain that are neither quantum (therefore random) NOR classical (therefore deterministic), then 1) he would have already won at least one Nobel prize, and 2) anyone in this thread would be able to at least gesture toward what relationship that is.
You're a fast reader :-D
No quantum bullshittery in there I promise.
Equating "classical" with "deterministic" is however pushing it a bit too far, when no one and nothing can ever do even a very small fraction of said determination...
Quantum is not bullshit and yes, classical is deterministic.
That's why the brain cannot possibly be anything other than an input → output machine, which is functionally deterministic (with maybe some fully random components), but is easiest to describe as probabilistic.
In the same way that LLMs are functionally deterministic, but easiest to describe as probabilistic.
We don’t know
Of course we do. Unless the brain is an object unlike anything else in the known universe, then it's an input → output machine.
This deserves about as large of a "[citation needed]" as one could draw. Are you well-studied in neuroscience?
Nothing in the universe is anything other than a chain of reactions, amigo.
The brain is an object in the universe.
The universe has quantum behaviors (fully random, not a source of intelligence) and it has deterministic behaviors (fully non-random). Many of those deterministic behaviors are so complex that they're easier to analyze and describe as probabilistic, which is where most brain input → output relationships land.
Please point to any evidence whatsoever that the brain has some third type of interaction going on that has never been observed anywhere in the entire universe, then we can have a discussion about it.
Its baffling to me that people think the brain is somehow "outside" of the universe and somehow "magic" and not tied down by physics just like any other matter is. I don't understand how people come to this conclusion, except in the case of belief in magic or religion (which is belief in magic more or less).
Yeah, that's ultimately this whole conversation and people just don't have the courage to acknowledge their own beliefs.
The brain is a (very complex, incredible) input → output machine. That's it! It's incredible!
I don't understand why people are so afraid of this that they will believe otherwise with literally zero evidence whatsoever.
Why is the brain probabilistic instead of deterministic?
Probabilistic and deterministic are not opposites.
The brain is deterministic at the level of specific interactions, which process inputs in a highly chaotic (but still deterministic) manner into probabilistic outcomes.
The opposite of deterministic is random, i.e. in the quantum sense of truly no relationship between input and output.
There are probably some quantum effects in the brain here and there, but the vast majority of it is just traditional deterministic interactions networked together in such a complex system that the resulting behavior is much, much easier to predict in probabilistic terms than otherwise.
Ok right, but then I don't see the original point. Or like, you don't seem to be appealing to a hard science/soft science distinction here if the question revolves around what conceptual model is most "useful" to us. Useful for what? For predicting future actions? That makes sense, but does actually that speak to op's (possibly still fraught) metaphysical/semantic point here? It just feels like you are speaking over it, right? They could just as easily counter that a probabilistic model of the physical brain is not a very useful description of human intelligence, so why use it? You can't appeal to materialism here because you are already practicing some implied pragmatism in your commitments. You pretty much have as much ground to stand on as they do!
I am saying that there is no categorical distinction to be made on functional grounds between artificial intelligence and brains.
You cannot say an AI model cannot be intelligent because it's a probability machine, when all available evidence points toward natural intelligence also being generated by probability machines (much more complex ones, called brains).
But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence.
So, LLM are just next token predictors, brains are next token predictors + many other things in addition, and that makes people still feel LLM are dumb even when they solve a lot of problems using tokens.
> brains are next token predictors
Of the vast uncertainties and philosophical exercises that we must face to bridge the chasm between where we are now, and where we will be when we understand intelligence, I can take comfort in claiming, with 100% accuracy, that our biology is not based on technology invented by Google in 2017.
> But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence.
Like what?
What specific biological structure in the brain could be doing anything other than producing output as a function of 1) current electrical/chemical/thermal inputs and 2) previous electrical/chemical/thermal inputs?
LLM doesn't just produce output as a function, they are much more specific: they predict text based on text they have been trained on.
Yes that is the item (2) parameter described above.
No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't.
The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn.
That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.
Okay, then what part of the brain is doing anything other than transforming an input into an output based on what inputs that part of the brain has previously been exposed to?
> transforming an input into an output based on what inputs that part of the brain has previously been exposed to
You changed the definition there, for it to be like an LLM it should be:
> transforming an input into an output trying to mimic inputs that part of the brain has previously been exposed to
Anyone can see how that limits you a lot, and why that makes it so much harder for LLM to learn things properly than it is for humans.
Well no, you changed the definition. LLMs don't just mimic what they've been trained on. They mimic what they're rewarded for. Like a brain!
Pre-training is just direct mimicry. A pre-trained LLM is very stupid and mostly useless. To become useful they are post-trained with a reward function.
Right, the CoT training step does work a bit more like normal training. But those do produce some very weird results, if you look at the "thoughts" CoT training leads to, I wouldn't say that CoT adds general intelligence, it teaches it to solve specific problems just like how any regular neural net training works.
The only general part of this training is the text prediction. All the other steps add specific skills to it and try to not hurt the generality as they do so, but we have no way to add general skill in the second step. So the general part is still the text predictor part. We know that training a neural net on specific tasks doesn't produce generality, we have tried that before, the text predictor is what let us get a glimpse of generality.
So then the text predictor try to solve problems by moving into the different specific skills there.
But yeah, you are right, without the CoT the text predictor wouldn't have any reliable skills to map to, so its still an important part of it. But the CoT doesn't add to the generality of the model, it just adds special skills it can utilize.
But, I agree what I am arguing now is a weaker statement. But I still believe its true, but I probably wont convince you of it.
This seems fine but things being the same "on functional grounds" is just one way things can be the same. Why should we privilege this framing as something definitive versus another conceivable one?
On functional grounds my bike has not a lot of distinction from a horse, but just, like, saying that doesn't tell me much about either. Or at least, it seems to leave out a lot of otherwise crucial details and differences..
What does it mean to you, this point of view? Are you truly coming from like a 20th century pragmatism point of view? Where what is most useful is what is right? Or are your trying to make a larger claim about nature? I think being clear about that would help focus your critique here.
Yours is a controversial view. It is lazy and selfish to try to get other people to explain their case that it is not exclusively that, when saying that it is exclusively that is the weaker case, and you back it up with nothing but a snarky proclamation.
Are newly born babies reacting due to statistical probabilities that they have derived, or are they using something other than their brains?
> Are newly born babies reacting due to statistical probabilities that they have derived, or are they using something other than their brains?
The answer is obviously yes lol.
The creature is an assemblage of electrical, chemical, and kinetic relationships.
Watching a baby develop is exactly what you'd expect from a system that's predominantly electrical noise triggering behaviors and then gradually refining denoising the relationship between inputs and outputs, with the goal function of achieving more desirable inputs.
Surely you can at least gesture toward one thing in the brain that appears not to be a probabilistic relationship between input and output?
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Every day I wake up and open HN.
“LLM has made legitimate mathematical discoveries” —> Wow the rate of progress is amazing. Highly upvoted.
“LLM does something not good” -> Does everyone else not realize LLMs are just dumb next token predictors? Highly upvoted.
So tired of this discourse and this site.
The rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand.
These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.
Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?
They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that.
For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new skills to them, so they will continue to get more useful.
In all fairness humans can also be considered next token predictors. It could be said that’s how we communicate with one another today. Presently LLMs lack other things, like physical presence in the world and continuity of input sensory data.
Humans learn to be a next token predictor as a kid when they learn to speak, an LLM cannot learn to be a next token predictor or anything of the sort, we have no clue how you could have an LLM learn human language just based on a thousands conversations with a human.
You don't see how that is very different? For an LLM to be as smart as a human it has to be able to learn like a human. Like you don't evaluate how smart a human is based on how much he knows, you evaluate it based on how fast he learns. And LLM are so bad at learning its ridiculous, they lack that part of the brain that lets humans be smart and learn so fast and easily.
> For an LLM to be as smart as a human it has to be able to learn like a human.
"For a plane to fly as well as birds it has to be able to flap its wings".
"For a submarine to swim as well as a fish it has to be as light as fish".
Somewhat.
Yes, but not because they are useful.
It can be a token predictor and still tell me exactly how my life will proceed from now until the indefinite future, or be the most intelligent conversational entity you have ever witnessed.
The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time.
To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.
The "dumb" part comes from how it behaves in contexts where it lacks a lot of data, or where the data is skewed. Since they are tuned to give a prediction anyway and just make something up since sometimes those made up things are useful they will produce dumb results.
So people call them dumb since like dumb people they make strong statements about things they don't understand. And it doesn't matter how much smart things you encode them with, they will keep making strong statements about things they don't understand until they are fundamentally changed.
But since LLM are very smart about things where they have extensive data they can still be used to reliable solve many problems and probably in the future where we understand that better almost completely replace most lawyer and doctors work etc, because a lot of what a frontline doctor or basis lawyer work is very repetitive and can be encoded with billions of examples and decision paths into an expert system framework the LLM will follow.
So people say LLM are dumb since LLM will always keep making dumb statements. This is the same way we call Elon Musk dumb for making a lot of dumb statements, he is a smart guy but he makes dumb statements so her is dumb.
> they will keep making strong statements about things they don't understand until they are fundamentally changed
If ever there was a human quality.
Also, your explanation of "dumb" is really favoring the anti-llm side, and its a very generous interpretation. I suspect what is much more likely meant, is that token predictors cannot be smart, not now nor in the future after improvements, because they are token predictors and predicting tokens is not how intelligence works.
All of this is of course unfounded, and hidden behind the word "dumb".
> I suspect what is much more likely meant, is that token predictors cannot be smart, not now nor in the future after improvements, because they are token predictors and predicting tokens is not how intelligence works.
Why do you think that? LLM are used as expert systems today, in order to quickly navigate problems by breaking them down and iterating between different well known possible solutions and paths to check etc. That is how they work, they do that by using their next token predictions, and for things they aren't well trained on they will produce dumb results.
LLM has solved enough problems that almost nobody has the view you ridicule here, but there are still many who think LLM are thinking just like humans and that you can trust them just like humans. So its important to remind people these are just token predictors and lack many things humans do.
> If ever there was a human quality.
Humans can avoid doing that by using introspection, LLM can't. That some humans do it by not using introspection doesn't mean humans are incapable of it, we know humans are capable of it, which is why we can point out when the LLM is wrong with certainty, humans as a group make extremely good predictions.
Much of an LLM's capability comes from the structure encoded in its learned representations. The probabilistic outputs are primarily a way of expressing uncertainty and generating fluent text, while compression during training is what forces the model to discover that underlying structure.
> Much of an LLM's capability comes from the structure encoded in its learned representations
And thats encoded as a set of next token predictions. So the way to see how reliably it solves a problem is to look at the chain of predictions, and see where it is unreliable at finding the next spot, or where it always fails and you need to add that link to the dataset to train it.
This isn't magic, today we understand pretty well how to add new skills to LLM, and the better this is understood the faster progress will be.
This also means that if a context doesn't have any good predictions, it will produce a dumb prediction for that context. This results in these bad outcomes, because currently LLM doesn't have a map for where predictions are good or bad.
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Not sure what your point is? Those things can both be true.
Or should the discourse in a diverse community like HN only reflect the positions you personally hold?
Tell me how a 'nExT toKeN prEdIcTor' can make breakthroughs in math or play a game of chess. These activities aren't pure symbol manipulation, they require actual understanding at some level.
By predicting next tokens
Eh, I'm not going to litigate your claims.
My point is it's silly to whine that HN is a place where multiple points of view on the topic are aired out and discussed.
If you want a personal echo chamber where only your own beliefs are affirmed and anything else is flagged off or downvoted, I'm sure you can go find one or, worst case, vibe code one into existence.
Fair so let me be clear. I’m whining because the “next token predictor” reductionist point of view has been wrong and is only growing more wrong with time. Clearly these things can do things that actually matter. Do you disagree?
Even now you're engaging in this discussion as though I'm trying to litigate your point and that somehow forcing me to concede is, what, winning? I don't know.
I get the impression you want me to concede that the particular points of view you disagree with aren't worthy of representation here on HN.
I'm not going to do that.
I just prefer HN comments to be better reflections of reality. There is an unspoken expectation here that people here know what they’re talking about especially when it comes to technical matters. The rise of LLMs has given way to a HN branded populism that willingly denies reality as well. “Next token predictor” truthism is just so dumb and completely ignores the reality of what these tool are able to do. Smash that upvote button every time it feels good if you want but it’s just a meaningless take at this point. It won’t help you predict anything that’s coming.
Since we disagree on the present let’s informally do a “remind me 2 years” to this discussion and see what’s happened then.
> I just prefer HN comments to be better reflections of reality.
You mean your particular version of it.
It's interesting to see you consistently missing this point.
You've decided LLMs are clearly more than just complex but mindless statistical models.
You've decided that based on, it seems, the very impressive things these tools are capable of.
Therefore if anyone claims they're just mindless stastical models--with or without any attached judgement as to their actual utility or usefulness--then they are ipso facto wrong.
(And yes I just used endashes, damnit!)
That's on you.
It is in fact possible to simultaneously believe that LLMs are mindless token predictors and that they're enormously powerful.
These are entirely orthogonal beliefs.
Heck you could equally believe that LLMs represent true emerging AGI and that they still remain deeply flawed and are only an incremental step along the path of automation.
Or somewhere in between.
And discussing that space of possibilities is, I'd hope, precisely what HN is for.
It’s just a boring and unhelpful complaint that afaict largely serves to soothe the commenters ego rather than point at anything insightful that’s useful or predictive. Point me to your favorite “next token predictor” comment that was actually insightful or predictive. You have years of material to draw from.
https://news.ycombinator.com/item?id=49155075
"These models are probabilistic, you shouldn't blindly trust them in spaces where accuracy is really important" seems like pretty sound advice to me.
Would be nice to get high karma commenter votes count only ..
Not a dichotomy actually. Highly depends on the task.
Opinions differ. This is not news.
It's almost as if there were many people using this site, and there is no clear consensus on LLMs, so people from various camps upvote interesting stores to support their cause. And people who are still somewhat undecided upvote both, if they present good evidence.
I mean even perennially contentious topics will get this behavior.... some thing about emacs makes the front page, within a day or two there will be a vim post up there. Same with Rust is (good|bad), or if systemd creates an even more awesome tool, the haters will come along and recycle stories about bugs from over a decade ago.
There's a lot of people here. Not all of them read it every hour, and discussions like this among large groups often take a very long time with lots of repetition. Human group dynamics (aka politics) is slow.
> So tired of this discourse and this site.
You're welcome to leave if you don't like it. The site was like this long before you joined, and will like it long after you leave I'm sure.
It's also worth noting, that an awful lot of math discoveries are perfectly in line with dumb next token generators - they are finding a way to formally construct an argument and being surprised when it doesn't work, or surprised at the outcome of the grind. Not all of them are made by brilliant leaps of intuition.
Wow you just got us a complete nostalgia moment to the good old times when the computer who always beats us at chess became `not artificial intelligence`...
> LLMs are text-prediction engines. They are not Artificial Intelligence, and shouldn’t not be treated in any form or fashion as if they possess intelligence.
I agree that humans must verify LLM-produced facts, but strongly disagree with these kinds of "stochastic parrot therefore dumb" arguments.
Yes, an LLM is a "stochastic parrot". No, that doesn't imply that it is dumb. Enough to look at how Terence Tao asks ChatGPT to help him understand a solution that nobody had ever discussed before [1], or how a random guy asks ChatGPT in a handful of words to disprove a 30-year-old conjecture, with zero technical input [2].
If your parrot in a birdcage with internet access can finish the sentence, "The counterexample to the Dinitz–Garg–Goemans conjecture is...", then it's a pretty smart parrot, by all reasonable definitions of "smart". Just because someone bottled up the formula into matrix multiplications and added some random sampling to the outcome, that doesn't take away from the fact that the parrot said provably correct statements that the biggest experts in the field couldn't imagine.
And no, I'm not implying that the LLMs are correct all the time, or that their intelligence and reasoning works in any way like ours.
[1]: https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed... [2]: https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...
The best way to describe the LLM intelligence is "an expert system that works the way people thought expert systems would work". You can encode a massive amount of skills into an LLM, and then the LLM uses those to navigate problems.
But the LLM is still dumb where those skills doesn't have good coverage, since unlike the expert systems it maps fuzzily to its skills, and they are tuned to produce results over rejecting the request when its unclear if coverage is good. As long as that is true you have to treat them as dumb even if they sometimes produce brilliant results.
This is a conflation of issues, predicated on false understanding of what LLMs are.
This line of critique is pernicious because it is both technically correct, as description, and profoundly misleading.
Saying that outputs are a product of inputs is not interesting and to the point it is not explanatory.
What is interesting, is how they do what they do. What is the "statistically likely* next token? To answer that you can do exactly one thing, run the LLM. That's because what they are doing is interesting and not reducible.
What is more interesting is that in order to do what they do, given the architectures we apply and the training strategies we use and the harnesses we situate them in, LLM are recapitulating in their deep layers strategies observed in the animal brain. This is still suggestive, interpretibility is nascent: but it is also more than a little interesting. In some respects, for cognitive scientists interested in the manner in which mind merges from computational substrates, it is profoundly interesting.
One can incorporate this, and, still be viciously critical of bother the success and failure of LLM in the applications we have put them to, and of how we (as individuals and as institutions such as corporations) are integrating them into our work.
There is a lot to criticize!
But criticism can be taken more seriously when it is not obscured by misunderstanding or misrepresentation (intentional, or not) of what LLM are and why they are not remotely "parrots" in the pejorative sense.
The technology, as technology, at the scale we are architecting it, is doing things we did not imagine would be witnessed in our lifetime, if ever. Dismissing that and denying it because of the career, industry, society, and civilization challenges that technology brings are existential, is bad argumentation or bad faith.
Both can be true at once.
> We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are.
I see no credible corroboration. More likely its folks having no more care for what they are doing than the bots themselves.
> Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this usage of “AI”.
People said the same about email spam ... until they engaged spam filters. CVE report slop is simply spam. Complaints are better directed at the filters, not the filtered.
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> We are not going back, period.
I didn't get this at all from the parent. They're simply stating that LLMs aren't entirely trustworthy, and that the responsibility is ultimately ours, not the LLM's.
Okay.
This is the right conclusion for completely wrong reasons haha. I love hn. (aside from being entirely wrong as most models are now mixed modal so they are token prediction engines)
Each layer of attention can more through feature space “lit up weight clusters” in a way no other previous AI can. It can from that decode some rudimentary logic and world modeling and make deductions. Certainly better than any previous AI. Only a goof here would believe this wasn’t a serious advancement.
So don’t over sell it. But don’t sell it short with this “grrr in an engineer don’t threaten me with new tech” attitude.
This take is akin to teenage angsty takes and doesn’t really belong here.
Does a dog possess intelligence? Does a bird? Does a cricket? An amoeba?
I hate AI slop as much as the next guy but the amount of tribalism over AI is taking near-religious forms.
Nobody knows what intelligence is, therefore we don't know what does or does not possess it, therefore we don't know whether LLMs currently, or in the future, possess it.
Yes, LLMs can be stupid, guess what: so can I. That doesn't really change the argument at all.
I feel like I'm on a deja-vu from when DALL-E was released and everybody was fighting over whether AI can be creative yes or no. Same story, different words.
Intelligence, creativity: we have no idea what these words mean, and AI is helping us understand them better. That alone is an achievement of epic proportions. I am not joking here. Any computer scientist before 2015 would be absolutely blown away by what you can now do for 10 cents and an API call, yet somehow because of the tech-bro-iness of it all we get a tribal war over what is plainly visible in front of us:
LLMs are uncomfortably close to what we thought intelligent machines would look like
"Dumb next token predictor" keeps popping up since that is the core way they work. Since they aren't logic engines but prediction engines they will always return a result regardless what you ask it.
Some predictions might be the tokens "I don't know", but that is based on the model mapping your text to those tokens by having seen many similar "I don't know" responses to such contexts, it didn't do any introspective logic to produce that "I don't know", and its possible it actually does know if it followed another branch there so "I don't know" is often not even true.
If they had an introspective part that stops the prediction when its too unreliable it would no longer just be token prediction engine, and I believe we need such a part for them to become what I call smart. I don't think LLM will ever stop being dumb without such an introspective part to them.
And no, that introspective part is not a part of the token predictor. At least not in us humans, the feeling of certainty we have is not a prediction, it is bundled with our thoughts, so we get both "answer is a bear" and "certainty is low", we don't get just one of those as a "prediction".
Will LLM become smart as humans with such an introspective part? I don't know, but I think they will never become as smart as humans without one.
Note: The certainty score has to be per conclusion or response, not per token. You can't evaluate a responses validity by aggregating the weight of each token. Meaning its a logic engine, not token engine, that evaluates the certainty of a statement being correct or not instead of a token being correct or not. That is the level human thinking works at and seems to be dramatically more efficient.
I strongly suspect that if you dug deeply enough into how a human brain does reasoning, the answer would boil down to "it tries a bunch of pathways and picks the one that seems right". Pathways weighted by experience, but ultimately still guess-and-check. What would the alternative be?
Maybe our ability to reason is not as mystical and special as our ego might hope it to be, and discomfort over LLM's bringing that to light is the root of some people's urge to continuously downplay and discredit them.
(As far as I can tell as a non-neuroscientist, the literature on how our default mode network and prefrontal cortex interact agrees with my assessment - free association and logical verification respectively)
You have introspection, you can see a part of your thoughts, you know how that introspective part works since its what we call consciousness, you are conscious about it.
That introspection isn't an illusion, what your consciousness see of your thoughts obviously are tings that has been calculated and aggregated by the brain, so we know the brain calculates and aggregates those thoughts and feelings to produce its results. And we know LLM doesn't do that, it doesn't have a side system that does that sort of introspection.
It actually is an illusion though. The narrative the brain makes up after the fact for why we did something is not perfectly correlated with the actual reason. It's just very good at its job of spinning a convincing narrative, even to itself. This is demonstrated in split brain patients: they're given instructions to do something that are shown only to one eye, the one connected to the non-speaking hemisphere, and when asked why they did it, the speaking hemisphere confidently hallucinates a wrong answer that the person seems to fully believe.
> The narrative the brain makes up after the fact for why we did something is not perfectly correlated with the actual reason
But it does that introspection, we evolved to make it. If its not useful for anything we wouldn't have evolved it, it can't have been easy to evolve a consciousness so there has to be purpose for it.
Or do you think our consciousness is a magical ghost thing unrelated to the brains workings? I think its pretty obvious that our smarts in part depend on the computations that results in our conscious experience, you need a very strong argument as to why that wouldn't be the case. And broken brains being broken doesn't answer this.
> I don't think LLM will ever stop being dumb without such an introspective part to them.
The very second they add this part, it will "just be a dumb token predictor with introspection", mark my words.
If it keeps doing dumb things, yeah. But if that actually solves it then those opinions will quickly disappear when it replaces all human white collar work since it does it cheaper and better and faster.
AGI is fairly easy to detect for this reason. Does this system make a majority feel you don't have to hire people anymore? If not its not AGI.
> But if that actually solves it then
Solves what? Chess? No thats not AI, its just a chess bot. Turing test? No, thats not AI, its just a dumb token predictor.
Why would the goalposts stop moving at AGI? I am 100% convinced it will somehow still lack the "gusto" or the "taste" or the "timbre" of real intelligence. You can see it in coding right now, AI has bad "taste" in coding, because really, we can't do a better job critiquing that which is obviously (on occasion) just plain good.
> Solves what? Chess? No thats not AI, its just a chess bot. Turing test? No, thats not AI, its just a dumb token predictor.
You are moving the goalpost here if you think the chess AI was AGI. All those problems were evidence AI wasn't as smart as humans, not goalposts determining when AI is as smart as humans. The first turing test winner wasn't even an LLM, it was an expert system, so we already knew that test wasn't enough for AGI.
Necessary is not the same as sufficient.
If I want my ox-cart to fly, I need to add wings, but that's probably not all that needs changing.
> A human must verify everything an LLM presents as fact. Everything.
I've thought about this for quite some time now.
No. A human doesn't need to verify everything. And the argument is really simple: stochastic.
Think of self-driving cars: We can show today - based on evidence and real data - that self-driving cars are safer than human drivers. That's a fact and the consequences are clear, more self-driving cars, less human-driven cars, less accidents, less hurt people, less dead people.
Are the cars 100% safe and NEVER make a mistake? No. But they don't need to. Nothing is ever 100% (in the real world).
Now back to AI for software creation. "Review is the bottleneck because EVERYTHING must be judged by a human." No. It doesn't. We just need to build AI review systems, that will do reviews better than (or at least as good as) humans. The human review quality bar is far below 100%. Far far far. If we can show (likely in the next 12-24 months I think) that AI review quality is consistently above the human review quality - again, based on evidence, based on real data - then that's it, then there's no good reason to have humans review the code.
Yes, there will be another layer in the system, another level of abstraction that will/must end at the human boundary.
> Now back to AI for software creation. "Review is the bottleneck because EVERYTHING must be judged by a human." No. It doesn't. We just need to build AI review systems, that will do reviews better than (or at least as good as) humans. The human review quality bar is far below 100%. Far far far. If we can show (likely in the next 12-24 months I think) that AI review quality is consistently above the human review quality - again, based on evidence, based on real data - then that's it, then there's no good reason to have humans review the code.
Let me know once the majority of software engineering organizations start only checking in markdown files and let code be generated non-deterministically from these specs in CI. If this is not happening now, there’s clearly a sufficiently high level of distrust in blind LLM output (both code itself and reviews).
And even then, are you suggesting humans shouldn’t at least review the markdown specs? Why not have LLMs review the specs then? Is there, perhaps, some fundamental quality to human review process that is desirable here?
This reduction of everything to stochasticity is silly. Or, to put it differently: Do you accept a value with some error appearing in your bank account on salary day?
We have plenty of systems where complete accuracy is the only acceptable thing. Computers are great for such things. Until we all get caught up in a way of delusion and start writing those systems as natural prose passed through an improperly understood stochastic machine.
We can chalk this up as another example of over-exhuberance by what folks believe humans can accomplish vs. what they actually are.
Flesh-based “brain” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credibility massively when your flesh-based brain gets the probabilities wrong at best, or in this case, claims a line of code generates a vulnerability when it is, in fact, a code comment.
Humans are prediction engines. They are not Pure Intelligence, and shouldn’t not be treated in any form or fashion as if they possess pure intelligence. What bothers me about this entire situation is that presumably the folks that have relied on the flesh-based “brains” to generate these vulnerabilities knew (or should have known) enough about their "tool" to know this would happen, but did not: To err is to be human.
Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this over reliance on fallible “brains".
A human must verify everything another human presents as fact. Everything. If you don’t, we all pay the price. Using a human does not remove the onus of responsibility on the human being in charge, if anything they amplify it because humans work for peanuts in some countries, and can generate lots more output more quickly that needs to be verified by the humans in charge.
You are right with the analysis, but wrong with the conclusions. Yes, LLM „thinking process“ is kinda non-deterministic in a sense that it does not follow logical reasoning and will not produce logically correct results in 100% cases. It has an error margin.
However, error margins are in the center of any engineering discipline. We cannot produce things measured with 100% accuracy. This is accepted fact. The focus is always not on eliminating errors, but on reducing them to acceptable minimum. With LLMs we should not expect an ideal logical thinker, but a process that may error sometimes, and we must design quality controls instead that push LLM outputs within acceptable margins. And it can work.
Yes but the key here is doing proper risk assessment. "What is the consequence if the LLM gets this wrong?" "How do we verify the output?" "What are the legal ramifications for using the LLM in this way?" "Who is responsible when the LLM fails?" "Whats the expected accuracy here?" etc.
In the current AI mania, there's a lot of due diligence simply being ignored. Plenty of "Well humans make mistakes too!" going on here on HN too.
The due diligence not being done is people putting cats in microwaves. It‘s not the dangerous part. The real danger is risk assessments coming to wrong conclusions, because it is still terra incognita. Talented engineers were in this situation before, doing mistakes with cars, airplanes, buildings etc.
No, I'm sorry but I think thats a cop out. The fact that LLM are stochastic and can give incorrect answers is not particularly difficult to comprehend, and the risks that fall out of that are reasonably understandable. The issue is entirely down to bad choices by the people driving LLMs, because they are engaging with what they wish LLMs do instead of what they actually do.
So you say that it is easy to understand what LLMs are, yet the issue is that people don‘t understand it. Either you put too many people in the bucket labelled „idiots“ or you are blowing that problem out of proportion, at the same time seeing only the tip of the iceberg.
Risks are not binary. Most people are not idiots. They do understand that LLMs aren‘t thinking humanly or 100% logically. You are talking about some sort of faith, I think this is more about trust, which is built on observations. Statistically, the outcomes of LLM work may hit the goal quite a lot. More of that in the beginning of the journey than in the end. For many reasonable people that becomes a trap, where minor, acceptable deviations accumulate into a fireball. Still, saying that LLMs are failure with inevitable fireball at the end and giving up is a trivial and stupid solution. Staying within razor-thin distance from that fireball and managing to deliver a working solution is what everyone tries at the moment. This is how it always happened and always worked. It‘s the art of engineering.