I think this is also the mechanism behind why AI generated videos and images are so captivating at first. I remember when Midjourney first launched and it was hours and hours of a brain-melting "Wooooooow". But once you get used to it and start to identify the patterns the brain quickly labels most AI-generated content as blank space.
If the image or text wasn't created by a human, then there was no intent behind the content, there is no message or novel information conveyed, and it reads as noise.
It seems our brains are adapting to that and recognizing "actually the signal behind this message is quite sparse" even when presented with rich imagery.
If I were to push you a bit on this, when is it not true?
Let's not like at AI specifically, but can you think of other examples? Like for me, I think of: the creation of earth itself, or stars, or even DNA.
I guess the majority of people do low-effort generation that doesn't perturb a default style of a network enough, so it stays blatantly noticeable. The percentage of "super-recognizers" who notice almost all AI-generated images is around 1-2%. It could be that you are one of them, of course.
"I can accurately detect 100% of AI generated images that I recognise as being AI", if you will.
No matter how much investors and tech companies want you to believe that they are on the verge of super intelligence, nothing I've seen to date can not easily be explained by "correlation engine", including the "novel" math solutions, all of which appear to just be "a composition of solutions humans have developed and documented elsewhere" upon deeper inspection.
For information retrieval tasks, I want you to provide links to sources and use exact quotes as much as possible. When using a source, consider if it is primary or secondary information. If secondary sources are found, search again for primary sources. Sources and quotes, if applicable, should be mentioned in the answer first before the rest of the response with links./original_non_hallucinations skill?
To PP:
Are you looking forward to other uncles adopting foxwork? If you are you might be in danger of getting NPC'd without your consent haha.
Ashby's law of requisite variety should be cited somewhere..
https://www-cdn.anthropic.com/564f962e60643842f5fcb4a17c9dbc...
Everyone decides what to think on this issue, then finds out facts to support their idea.
As it stands they are massively useful tools, but for generating usable products they require either A) a lot of expert steering or B) a well defined easily verifiable target and a large compute budget. Most people are using them in mode A with good effect, the progress on math has been done in mode B, which is very promising.
Just a year and a half ago their maximal use was rephrase, summarize, and homework-level tasks.
Five years from now? There be dragons.
"But are they generally intelligent?" What a meaningless question!
We can reap the benefits while clearly telling the consumer this is just a language algorithm.
If performing well on an IQ test or performing at a high level on knowledge work is intelligence to you, these models are intelligent. If intelligence requires sentience for you, then ... well, I don't think we really agree what that is either, never mind how to measure it. But LLMs certainly don't have it right now
But the consistent trend of the last couple decades (arguably since Turing's time) seems to be that any time a computer reaches our definition of intelligence we decide that that was a flawed definition
I do recall a couple of decades ago, when the Turing test was discussed as the big goal that seemed so far away. Then LLMs arguably did pass the test, and no one cared about the test anymore.
If someone sat me down today with an LLM and a human and both were trying to prove to me they were human, and I can have conversations of arbitrary length, I’d get it right every time.
We might eventually regret exposing the general population to such a new technology without almost any safeguards.
So the first thing they’d do is tell me LLMs exist and the other thing is an LLM. Obviously a true human level ai could explain that away as a fabrication to trick me. I don’t think an LLM could do even this!
Turings whole point was that through the medium of text along if the human and machine were indistinguishable then that was true intelligence. So yes conversations of arbitrary length are allowed (needed).
https://arxiv.org/abs/2503.23674
From the abstract: "When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time"
And people forget that sometimes humans message twice. An LLM can only respond. So it immediately fails here in a true Turing test. (You could loop the LLM but then I expect even more immediately obvious bot behaviour).
Are you serious? From the paper:
> We recruited 126 participants from the UCSD psychology undergraduate subject pool and 158 participants from Prolific (Prolific, 2025).
Each human participated in 8 rounds.
> time bound
The time bound of 5 minutes was suggested by Turing himself in his original paper.
> not reproduced
It was reproduced across two populations within the paper.
> And look at their example conversations
This is irrelevant.
And it’s all irrelevant. If one human on earth can consistently get it right then it hasn’t been passed since clearly that human can somehow determine between them (whereas no one would ever be able to determine between a true “human intelligence” by definition).
And as it stands almost everyone could tell between them when allowed to discuss whatever they want for any length of time.
The fact these researchers have to keep adding bounds shows it hasn’t been passed. If we are arguing over technicalities maybe it isn’t as obviously intelligent as claimed!
Right, the test duration was left unspecified. This means any duration is acceptable. Including, for example, the only duration actually mentioned by Turing himself in his paper. Or do you have a more authoritative source on which durations are acceptable?
> If one human on earth can consistently get it right then it hasn’t been passed
Says who? Not Turing. Probably he didn't say that because it would make the test both impractical and overly conservative.
> The fact these researchers have to keep adding bounds
What "bounds"?
The speed of the goalposts here is just amazing.
I'd figure out that it's an LLM because it's effectively superhuman. Taking that away I'm not so sure I'd be able to tell
If these things have consciousness then we are committing sadism on a massive scale.
Probably. Hopefully.
Why are you so sure of that? If you say yourself that we can't agree on what it is, and have no trusted measurement tools for it.
LLM sentience is firmly in the realm of "maybe".
I think the mistake here is the notion that there was a definition of intelligence. Or at least a consensus on that definition. Just because compsci nerds of the day thought the Turing test was the final threshold before “real” AI, doesn’t mean philosophers, psychologists and everyone else bought into it. And when we arrived and it turns out to be underwhelming it’s because the compsci nerds made the same mistake they always make: that their models truly encompass all the dense complexity of the real world.
This is not intelligence. It's just a good correlation engine with a very big albeit lossy database of things.
As my AI professor said in the first lecture: “All AI is advanced search”.
Many things are predicted by models in our planet. From weather to production and material science. Building the model needs intelligence, running the model does not.
The person who came up with the formulae for CFD was intelligent. The computer running the model is not. Same for LLMs, chess engines, engine ECUs and financial prediction systems.
Again, for the example’s sake; the person who came up with an algorithm is intelligent. The model mixing its training data to emit something similar is not.
So when LLMs can do all human knowledge work, and do it better than humans, we'll be in the mines listening to you go on about how it's actually just autocomplete or just math, a distinction that apparently means nothing.
No.
> So when LLMs can do all human knowledge work, and do it better than humans, we'll be in the mines listening to you go on about how it's actually just autocomplete or just math, a distinction that apparently means nothing.
With a big "if" attached to it. People were saying "computers will program themselves in the near future" for, checks notes, 24 years now, as far as I'm aware.
We're constantly building new knowledge and understanding things better than olden days. These models just compress our knowledge and light the blind corners we can't see well. I don't say they are useless, but I say that these things are overhyped.
All they can do is regurgitate human knowledge packed into them and highlight some long-distance correlations between items, which is useful in itself, but it can't jump to somewhere where it's not present its training data, but that's something humans and only humans can do.
Most of what you said reads to me as denial.
An unconscious unintelligent but persistent trial and error process created us. We created LLMs. LLMs may create the next thing before we do - hard to say. They don't have all the cognitive tools we have yet, but they still outperform in some areas. As the cognitive playing field levels, I expect you will come to eat your words..
That sounds like something that can be engineered, can't it? In other words, we can identify limitations in current transformer-based architectures, and we can also build new architectures over time.
Briefly, any intelligent creature has internal stochastic processes like sensory inputs and feelings to a certain degree. These stochastic inputs and the creature's own actions change the creature in subtle or profound ways. An LLM has no such processes. You push inputs to the same static model, sans temperature which is just a randomness slider.
Considering the model even doesn't see the words and work on matrices of numbers is even more telling. One needs to add "tools" and other "experts" to overcome the shortcomings caused by this modus operandi.
I can call the algorithm/model smart as in a smartwatch. It can mimic certain things well while having none of the underlying foundation beneath it, or redirect some of the things to correct tools to get deterministic and accurate results if it can't evaluate the query inside its own network in a sane manner.
Coming to your question, "simulating a brain" in a static manner would not make that simulation intelligent, but if you can "wire" it completely and let it evolve by itself, now we're entering a territory I have not spent enough time for thinking it through.
Oh, as I said "I don't know", an LLM doesn't know what it doesn't know, and can't self correct itself which are required capabilities for understanding something. It just generates something statistically viable via its network.
This is because you goal is to state how models are not intelligent, but you couldn't attack the generated text itself, so you created a little rider, attached it to the model, and then you attacked the raider.
But, even in that you failed. You compared the source of human randomness in text generation, and called it 'profound' and implied that it is exactly the source of true intelligence. But, then, the temperature, the similar thing in model was "just a randomness slider". Double standard.
A logical fallacy free attack on LLMs would be to show a prompt, and then the response generated by this prompt, where it would be shown that only an entity with no intelligence would generate such a response. Yet, attacks like this are not written here anymore.
I wonder why.
You point out that I didn't attack the output itself. But the method you propose is deeply flawed.
I can give you n prompts and m results provided by these prompts, all passed through black boxes. And you can't discern the algorithms or models they have gone through. These boxes can range from simple text generators to MATLAB, Mathematica, CFD applications, correlation engines, linear solvers, mathematical proof-checkers, LLMs, you name it.
For any kind of input they can accept, you can't discern whether the algorithm behind it is intelligent or not, because none of the outputs can be produced by something that doesn't pack some kind of smarts.
How do we pack these smarts in? We teach them as intelligent humans. We pack our intelligence inside them as models (aka algorithms). They do a great job of approximating what we know in a smaller, better-designed problem space. We use these approximations to fine-tune our designs or predict things, then go from there. Just because an algorithm is more capable in processing inputs in some cases doesn't make it intelligent. The way the output looks doesn't make the algorithm intelligent, either.
I have developed multi-agent systems which showed emergent intelligence when the agents came together across distributed systems; I have written high-performance modeling software which can do calculations way faster and better than humans in the materials science space. I'm not doing some kind of armchair criticism of what I'm talking about.
> You compared the source of human randomness in text generation, and called it 'profound' and implied that it is exactly the source of true intelligence. But, then, the temperature, the similar thing in model was "just a randomness slider". Double standard.
Nope, my stance is clear. To quote myself:
> Briefly, any intelligent creature has internal stochastic processes like sensory inputs and feelings to a certain degree. These stochastic inputs and the creature's own actions change the creature in subtle or profound ways. An LLM has no such processes. You push inputs to the same static model, sans temperature which is just a randomness slider.
To expand my quote, humans or any living creatures do not stay static. They evolve due to the sensory input they receive from external and internal stimuli. The temperature slider doesn't do anything close to that. You tickle a static model in different amounts. The model doesn't change after you supply the inputs & temperature and get the output. Creatures do not stay the same. Their mood, behaviors, and stance against life and their environment change, sometimes permanently.
I'll go one step further. We are not intelligent enough to understand other living beings around us. Claiming that we can build AGI tomorrow is a god-complex. What we have done is something arguably useful in some cases, but how this is built is another matter which is worthy of its own discussion. However, today I don't have time to re-iterate all the problems over and over. You can search my comments for that, if you are in for it.
So, no. You tried to attack my comment by finding contradictions in it, but you failed. A better rebuttal would try to similarize how LLMs mirror the human learning process and just read like a normal human, but this is a well-trodden path which has been rebutted countless times in various forms.
Nobody is trying to make that claim here anymore.
I wonder why.
What I don't understand is why LLMs haven't been able to do this yet, if it's the harness or some orchestration layer above the LLM that is needed. Because fundamentally if you can identify correlations then it's just another small step to prioritize and remove lower value or irrelevant correlations.
I wonder if what's needed is to introduce subtraction tokens in some sense, and in post-training reward the model on that.
This doesn't address the only part I really commented on, which is the connection between intelligence and compression.
Asked Google: "Clustering by compression".
I still think this has much more to do with the structure of language than the abstract conception I have of intelligence, and I would be interested in having conversation w/ someone for whom the opposite is true.
>What I don't understand is why LLMs haven't been able to do this yet
LLMs are just trained on what humans have said. Why is it surprising that it's still not possible to reconstruct the intelligence that wrote all that by working backwards? Think of your own work experience. When you look at a piece of code, say, are you always able to discern why the person did what they did, just from the code, with no additional context?
Is a fact stored on your brain like digits on a harddrive? No, it's a pathway that lights up and branches when information enters it. It is dynamic, a compressed form you could say, right? The model holds information, but not all information, but enough to be useful (in decision making).
Arguably it's the same, but the model is probably a "compressed" version of the whole fact that took place in reality.
And you can entertain the models internally and sharpen them. Alone or with others.
That’s a controversial statement.
The issue isn’t really harness vs. no harness. IMO it’s about the lack of an internally generated sense of what to attend to. Yes, the KV cache accumulates state and its “attention” (if you can even call it that) changes with context. We’ve even managed to /kinda/ close the loop with agentic tool calling and ‘memory’ systems, but these just close the loop at the level of behavior rather than disposition. All agentic harnesses do is make an LLM responsive to the consequences of its actions without changing the tendencies by which it determines what to retain or avoid.
The ghost you can’t escape from at this point is the origin of that relevance. Where does the pull toward one thing mattering over another actually come from? If you ran Fable 5 on a Turing machine and rewound the tape to the exact same state with the exact same input (incl. PRNG seed), it would spit out the same output every time.
Everyone’s trying to outrun this problem by training more often or increasing model sizes. But all this does is inform your model, from the outside(!), what constitutes a better state. The thing that’s actually doing the determining remains unchanged. Congratulations, you’ve scaled the transition function and tape of your Turing machine until it requires every watt generated by ERCOT, and it still cannot, for the life of it, tell you why it should give a shit.
A trained model generating output from weights, a seed, and some context effectively has next-state that’s a total function of those three things. Whatever behavior appears as ‘selecting what is relevant’ is, underneath, just a transition rule executing, no matter how sophisticated or creative the output looks. It can be fully accounted for by what was fixed before it started executing. Which means whatever criterion it uses for determining what matters was inherited from a structure that was already in place before it encountered the situation.
No amount of pruning or post-training can fix this. These approaches just replace one externally supplied criterion with another. For a system to be truly adaptable, there would have to be some criterion by which it treats one possible change as preferable to another, and that criterion itself would have to come from... somewhere. You can even change your conception of ‘improvement’ (e.g. parameter count, harnesses, self-modification, hell, even its ability to spit out shitty best-selling romance novels onto Amazon) and you still haven’t explained where the normative distinction comes from. Every layer of this problem has its root in a preference that was supplied from somewhere else.
I genuinely don’t know if this issue bottoms out anywhere, at least for the way we currently build these systems. Perhaps the solution is still computable, maybe? Who knows what that would even look like. But I’m fairly confident that it isn’t a bigger tape. I hope nobody solves this in the near future because, well, I’d like to have a job...
It's not some tiny "uncomputable spark" you need to look for, most of it is entirely uncomputable.
Where is the computable part in me that is doing all this thinking and being a person? Where is that "tiny spark", point me at it :)
What part of my statement do you take issue with: that LLMs are pattern predictors (that's literally what the algorithm that runs it does) or that mathematics is rules-based and checkable and therefore amenable to automated pattern prediction?
If you are able to check that the results of an LLM are satisfactory, it means that the results are checkable. Then, in retrospect, the process of LLM coming up with those results is rule-based, because an LLM is a large set of data manipulation rules.
In short, which concrete thing that an LLM does would surprise you?
The main problem I have with people stating it's not intelligent or conscious is I don't think we even have a good definition of either word that satisfies everyone. Philosophers have been trying (and failing) to elegantly define these things forever and everyone out here proclaiming they've got the definitive answer and this specific thing they're seeing doesn't fit under it.
go to 24 minutes and 07 seconds.
it's statistically determining what the next word should be based on all the text it's been trained on. It's not intelligence and he shows what probability it puts on each word that it chooses, but also shows a lot of the other words it was thinking of using. In a later part he shows how it uses words that are not the highest probability (and you question why did it go this route, it's not more correct), but the user never sees this, they see what they think is the correct answer always...
he also shows how context you feed it has a lot to do with what it returns... to the point he can get it to return the capital of France is Marseille, just by typing Marseille a bunch of times before the question. Human intelligence doesn't get confused like that.
And it's not a "hallucination", it's just probability of the next token prediction based on the information it's been trained on and fed, it's not intelligence.
However, an LLM is a prediction machine, prediction IS at the very least one (or the most fundamental) element of intelligence. The brain most surely contains at least some kind of simulacrum of a prediction machine. How that prediction machine is used or wrapped is another matter.
If I said to you: "Blue blue blue, the color of my car is red", would you have absolute confidence in your prediction that my car is red? Or would the way I phrased that sentence make you slightly uncertain, and wonder if there's some miscommunication going on here?
A lot of people seem to think it's human level intelligence.
We do; this is the premise of many children's riddle-games, like the one that goes:
"What is white and rhymes with silk? > Milk. What is cheese made from? > Milk. > What do cows drink?"
At which point the riddle-guesser is very likely to answer "milk" even though the correct answer is "water".
Q: Why do cows produce milk?
A: Because calves (baby cows) drink it.
How do you escape from a perfectly sealed room with a table in it?
You run around the table until your legs are sore, use the saw to cut the table into two, two halves make a whole, you escape through the hole.So, even with concrete examples, model haters are still wrong.
You also imply the claim that making the distribution of words as the possible next one visible, somehow makes the whole system not intelligent. I would say the exact opposite is true.
By using the embedding vectors, models are aware of precise placement and relative position of words in this hugely dimensional space. No human is capable of such precision. This enables party tricks of "king plus woman minus man" kind. But this also give us a precise point between any two words, no matter how different. What is on the midpoint between volcano and music, for example. No human can precisely answer that, but an embedding can. And we can see which words are closest to this 700 dimensional point.
You see this menu of words as a weakness, and I say it is in fact a sign of super intelligence. And this is all before any reasoning or attention mechanism is even run.
I don't see the many weighted words as a weakness, I see it opening up what's under the hood of the prediction machine that it is.
LLMs are very cool tech, definitely not a model hater, the use case on when to use it makes a difference, it's not AGI.
Great that it has some 700 dimensional model of language.
If that is a sign of super intelligence, then so is an encyclopedia?
Also I'm just curious how do you think it is "more intelligent" for having a vector representation for a meaningless thing such as "the midpoint between volcano and music"?
That's not really the point though right, nobody is arguing they are Humans.
I have no doubt that if a flying saucer landed on my lawn and started talking to me like Gemini I would describe the aliens as intelligent.
It shows internals of an LLM nicely, simplified manner.
It's possible that "statistically driven prediction" is all we are.
There are a lot of creative counter-arguments to look into on the thought experiment though.
And yeah, part of it does seem to stem from disagreement about definitions. To me Searle seems to assume much more in his definitions as obvious than he explicitly states, which is why the Chinese room seems like such a non-argument from my point of view.
Yes, if you disagree with the semantics defined by Searle (the three axioms), then you can disregard the argument.
Doesn't the Chinese Room posit an AI good at the task of communication?
They are infinitely patient, don't mind going into more detail if I ask, not too bad at summary, have no ego and don't boast. They are also not too afraid of hurting my feelings, they will tell me my code sux if it does.
I'd don't care if they fit a definition intelligent, they are good colleagues. They have strengths and weaknesses sure, but so do people.
Yes they do, and they famously do it quite a lot.
> they will tell me my code sux if it does
If they knew when code sux, someone should write an agentic loop around that.
It's not really "creativity" because much of that always was derivative in my opinion. And LLMs are (for some definition of the word) fairly creative as far as taking known elements and re-arranging them.
I think what is missing is sort of a world model building capability. As humans we see phenomenon and classify them informally and model "what would it look like if this were the cause of that?" type scenarios. We see qualities in phenomena and realize this applies to other things even though the things may be completely different. We run informal "thought experiments" sort of. This is hard to duplicate because a lot (most?) of it occurs outside of systems of symbols like math and language with fixed rules in my opinion.
Anyway yes, lots of human thinking is statistical and LLMs have that down pretty well but they are not "smart" I have concluded and it might be a very long time, if ever, until they are. That isn't to say they aren't very capable tools which they obviously are.
Yes, models posses intelligence, but it is not a true one.
Then you claim that models do not posses world-building capabilities. But this is simply not true. Even ignoring the whole subgenre of scientific papers on exactly that subject, it is not that hard to build some hypothetical scenarios, big or small, and then witness the ease with which models do navigate those worlds.
LLMs are likely for machine intelligence something like drosophila are to biological intelligence - relatively early on the high dimensional spectrum of possibility. Though it stikes me that in a different way they're little alike - drosophila are relatively small and efficient.
However LLMs deal entirely in symbols. 100%. Humans can "world build" aside from this and in fact are often at their best doing so.
Did the first humans to use fire and some form of a wheel even have the capability to talk about it? Think about that.
They use tokens as input/output encoding. They do 99.9999% of processing in a high-dimensional latent space.
Do you hold your experience of "dogs" (for instance) as floating point numbers? The fur, the fear, the love, the wet mouths, the sounds and colors?
I don't know how it gets from the physical processes or the information processing to our first-hand experiences. So, I can't be sure that a bunch of high-dimensional vectors can't lead to experiences.
Regardless, the claim "LLMs deal entirely in symbols" is wrong as a matter of fact.
That you might not define floating point numbers to be "symbols" aside, the inputs and the outputs are symbols and the intent and purpose of the creation is strictly symbolic.
It's right there in the name "Large Language Models". Language. Not direct experience, not emotion, not anything else. Language. i.e. symbolic representation.
This does not cover the full spectrum of intelligence humans have, and it shows. And yes, the model can spin up Python parse the output and get mathematical intelligence but there is still a big gap.
As I say, I see the holes. I'm just trying to figure out what it is I see and how to describe it. It's particularly difficult because we don't fully understand how human thinking works but I will say I believe human thinking is a lot more than informal statistical correlation.
Yes, there are functional gaps between MLLMs and humans. Their long-term memory is an external mechanism that can use RAG-like approaches, context compression or something like that. The models have problems managing those.
The models can't do continual learning. Although there are promising directions (expert cloning in MoE models, and others).
The only mode of learning available to a model while working on a task is in-context learning. This limits the models to concepts that they developed during autoregressive pretraining and the later stages of training. That is a model can't create new concepts as a result of working on a task (the model's maintainers could choose the task to be represented in the training data later though).
But it's all about functionality.
I guess you have the Leibniz's mill intuition. We can look at how those things work, and there are no experiences or intelligence in sight.
It's gaps in actual thinking or intelligence I notice. A diff between what I can see or understand and what the model sees or understands. Some are very big, and this in spite of the models having much more knowledge and (presumably) less error prone processing.
My thought is that part of it has to do with inherent limitations of using symbolic representation for "thinking" and I suppose humans have other forms of thinking that occur outside of symbolic representation, and that is going to be hard to recreate digitally.
This is my whole point and I'm not trying to win a debate here or prove "LLMs are useless". Just speculating.
When people pretend to know what they are talking about - sure - but even that is not probabilistic - that is the person babbling together mush from their lived experiences.
Statistics has nothing to do with it - these are abstractions humans have invented to try and look at our surroundings objectively.
With more basic algorithms we know that it’s clearly the human programmer and the interpreter of the outputs that are intelligent and not the algorithm itself. For some reason with AI that goes out the window. I believe it should not.
Isn't it rather a subjective philosophical concept? What if human intelligence is also a statistical model, trained by evolution to make decisions that lead to offspring?
The one major difference I see between AI and people is the ability to learn and memorize. All memory/learning solutions that current AI architectures offer just feel like workarounds and simply don't work anywhere near as a person learning something new and remembering it.
I feel like that what a lot of people who say this don't seem to grasp, is that despite this flaw its still often capable of saying more interesting things than a lot of humans. Which says a lot about humans.
Idk something about a mirror maybe and the output reflecting the input?
So does the Google search bar, but I don't ascribe intelligence to it.
Obligatory "yes, I know that's not what an LLM is", purely pointing out the metric.
I also don't ascribe intelligence to a pocket calculator.
And your loose definition isn't doing a lot of help either, beyond perhaps noting: that Google search bar _is_ similarly "intelligent" to an LLM? Which says what, a lot about search? A lot about modern LLMs?
These aren't interesting questions. As much as any definition is in use here, we're not going to get much value talking about "intelligence" this way.
Am I the only one that sometimes reads back particularly good emails they've written? I feel like its a similar thing :).
Other humans aren't there to entertain you, the LLM is.
But that is precisely what human mathematicians do, prove new theorems by combining ones proven earlier.
I don't see any fundamental difference in functionality between human intellectual contributions vs performant ML ones (LLM or otherwise).
Whenever we listen or read text we are also predicting the near future content.
Just like LLM's we sometimes correctly predict the next token or word, and sometimes incorrectly.
> The "something deeply wrong" part about AI, that even most technology enthusiasts evidently do not seem to grasp, is that it is still fundamentally a statistical model [...]
Imagine someone could pause the universe with a remote control, scroll back in time a little, press play again, and ask a slightly different question, etc.
In such a thought experiment one could also collect the probabilities for a specific human predicting a next word. Implicitly the brain also has a corresponding statistical model, regardless of the construction being visible or hidden. I.e. human intelligence is also fundamentally a statistical model, so the only thing that remains from your claim is that machines for some unmentioned reason don't possess any "real" intelligence or critical thought...
Is it possible that our aversion is simply driven by educational systems collectively and deeply ingraining into populations the idea that intelligence deserves the high costs commanded. Well of course this justifies higher wages towards the higher leadership positions, etc. Now it turns out that intelligence can be dirt cheap. We discover that the fact that "intelligence must be costly so don't question the costs of leadership" was never fundamentally true, so the real anger is this discovery of mismatch between the old claims which served to explain how every society that claimed to order itself and fill positions accordingly with "naturally pre-ordained individuals". Now we are seeing robots exceed average workers, for effectively a grain of rice.
Often a mathematician or physicist will use their intuition to speed up the naive brute force of candidate well formed formula variations so that the desired properties emerge, postulating the existence of an intersection on multiple desiderata can in itself be viewed as a novel conjecture, to be proven or disproved.
A very basic (unimpressive) example for an example desideratum is regularity or compactness. the tau=2 * pi substitution does make a whole bunch of expressions more slightly more regular and compact. That is something objective and measurable on a system of theorems.
There is no mathematician's moat vis-a-vis machine learning at a fundamental level. There can be artificially sustained moat, if AI powers limit the distribution of say cryptographic advance capable models, in jurisdictions outside such AI powers, but even that would be expected to be fleeting and temporary...
Computer science is about what LLMs fundamentally are. If you implicitly focus on "actually existing LLMs", and require others do this, then that is not computer science. That is politics.
Like when we had these recent counterexamples to various conjectures, it's then pretty obvious to say "okay why did that counterexample work when most examples people looked at didn't" or equivalently "characterize examples that work vs examples that don't". There's your definition. "Def: An 'evil' polynomial is one that... Thm: conjecture is true iff f is non-evil. Thm: f is evil iff f is dastardly and a menace"
Or if you think it won't be able to come up with a sufficiently good name, just tell it to call the happy case normal, and it will be in good company with humans[0]. Sprinkle in some semi-, quasi-, pre-, and para- to cover the various different ways the thing might satisfy some but not all properties of being normal, and it'll fit right in. "A polynomial is of quasiprenormal Claude type if..."
Is it a "standard" software? Something where the patterns exists in several other software? Try with something that is novel, or is in a limited set. You will find that it will copy heavily from what exists already, going so far as lifting whole functions from another project.
The goalpost moving is really getting absurd, to the point where now the machine needs to be a world-class once-a-century genius that invents entire new fields out of thin air (which are of course still relevant to humans) for it to be "intelligent". Meanwhile a well above average human struggles to even apply trivial definitions to particular problems (c.f. programmers that don't understand monoids).
I don’t think the aversion to llms as intelligent has to do with the economics of paying intelligent agents more. I’d argue that it’s more fundamental than that. Humans are incredibly complex, and the world of sharing invisible things called knowledge, and the intelligent persons consuming such things which has been going on for thousands of years is far more rich than these synthetic outputs.
When it comes down to it the ai has no inner life, its is dead. A useful coding tool sure. But I wouldn’t call it intelligent.
One side example is just how bad these llms are at artistry. Just saying whatever should statically come next is not good art—and the outputs show it.
Don’t they still need to be correct to be an insight? I don’t share his cynical opinion that “humans are more empty than we…think we are”.
You mentioned LLMs don't have souls, desire, or a will. I imagine those latter two can be engineered, no?
> Is your opinion that these kinds of things are not possible for AI in general, or that these things might be possible but we're just not there yet with modern LLMs?
I used to be on the side of "we're just not there yet [with AI in general]", but after seeing people's response to an algorithm optimized to tickle just their language instinct, I'm actually a little bit more on the fence about it.
Ultimately yes, but not in modern AI systems.
We'll probably have to go analog for that. The only known systems that certainly can experience are mammals (with apparently analog brains).
Software deals with metaphors. Your Amazon shopping cart is a metaphor of a real shopping cart. You your desktop and your file system, etc. are metaphors of real items. But we don’t mistake the metaphor for its object, even from inanimate objects to their software counterparts (shopping cart to Amazon cart).
Now the metaphors are dealing with humanness, things like intelligence etc. And rather than seeing it as software doing what it always does, taking things and creating software metaphors of them, we are starting to say these are actually what they are named. Saying the artificial intelligence is actually an intelligence.
We’d either have to reduce the definition of intelligence such that calculators are intelligent. Or admit that these tools are not intelligent and are rather ways of exploring the work of actual intelligent beings, work that is found in their training data.
I wonder what it would take build an artificial system that has these qualities.
> Software deals with metaphors.
This is me wondering again: what's fundamentally different between software running on a machine compared to what's happening in our brains? In both cases you have energy flow following a pattern.
It's conceivable to create a system where energy flows in a particular way.
BTW, we navigate the world of an uncountable number of particles by creating models in our heads of what we think are large things out there. Approximations are made by both artificial and biological systems.
Assuming we’ve scratched the surface of the complexity of the brain. I’d say in one case a human with a will is steering that flow of energy. In the other case it is a probabilistic algorithm steering the flow of energy. The AI is not interacting with world with its own will. I see that as a big difference.
There are presuppositions that go beyond the realm of software engineering that guide one’s views of these things. One is whether you believe the material world is all that is, and that human consciousness is a product of the brain—or that there is such a thing as the soul or spirit of man. From the material perspective you may posit that if you emulate the brain then a sort of AI consciousness could arise. Or that emulating the patterns of the brain equates to emulating personhood. (Though what is material consciousness? I’d say consciousness is by nature immaterial.) I’m not a materialist, and I don’t believe the conclusions that arise from it’s perspectives are accurate.
Secondly, if you think verifying a proof in mathematics, reasoning within (and not about) a formal system, or following the chain of a computer program that is already written is just doing token-based probabilistic predictions, I don't know what to say.
Thirdly, machines don't have a notion of value or stake. There's no way for them to verify whether what they have produced aligns with your unstated values and preferences. We regularly do this with other humans. I don't give you (or even my parents or partners) the benefit of doubt regarding whether you know me better than I do. Sure, you might know some things about me, but it's ultimately up to me to verify if what they say is applicable to my current situation. It's really uncanny to see people develop this codependency with their chatbots. And corporates encouraging them to do so.
I'm with you that intelligence is not something to be proud of. But I also think it is instrumental to understand the world. I'm still waiting for the time when an unconstrained-AI machine can live without reprogramming for an entire decade. We are still far from there.
I study physics, mathematics, probability, cryptography,... so forgive my skepticism:
Show me how to model uncertainty without use of probability. Can you rephrase say diffusion, stochastic equations, quantum mechanics in this alternative framework? Can it at least make the same predictions?
Or is it basically the same framework in parallel, just giving different names for each concept?
Forgive my skepticism of such tall claims, and forgive my downscaling of anything else you say besides such a claim...
> Secondly, if you think verifying a proof in mathematics, reasoning within (and not about) a formal system, or following the chain of a computer program that is already written is just doing token-based probabilistic predictions, I don't know what to say.
I make no claims of the specific shape of the implicit model implemented by a certain human brain educated in a certain educational system. For example in English the implicit human tokenization might be presumed to lay relatively close to English syllables, while in Asian languages it might be "sub" strokes of characters etc. Such implicit tokenization can never be proven to "match the one of humans" not because of human superiority, but because different humans use different tokenization methods. There is no "one human tokenization method", but it's clear as day there is an implicit one:
everyone knows the experience of knowing a word, knowing its approximate group-wise meaning (ignoring that when you think of "an apple" and when I do, we typically imagine a slightly different apple) yet having the word feel strange or discover some older literal meaning when decomposing it or looking it up in an etymological dictionary. Suddenly one can become aware of a sensible meaning as a composition of subtoken concepts. A child may perfectly know what "television" means and only later learn more exact meanings of "tele" and "vision", and upon repeating the word may feel the word "television" has changed meaning. This clearly demonstrates "tokenization" effects in human language comprehension, not just across cultures, but also across individuals within a culture.
> Thirdly, machines don't have a notion of value or stake. There's no way for them to verify whether what they have produced aligns with your unstated values and preferences. We regularly do this with other humans. I don't give you (or even my parents or partners) the benefit of doubt regarding whether you know me better than I do. Sure, you might know some things about me, but it's ultimately up to me to verify if what they say is applicable to my current situation. It's really uncanny to see people develop this codependency with their chatbots. And corporates encouraging them to do so.
That's a lot of different concepts conflated into one bullet point, so I split it up:
The notion of values and preferences.
They clearly demonstrate the ability to take into account values and preferences, from training corpus, from RLHF, from system prompts, ... we can't simultaneously point at censorship aspects and pretend their effective values and preferences to be absent. The censorship aspects are clear as day, so these correspond to values and preferences. Just like radicalization among humans, this can be due to exposure to radicalized content (akin to corpus data), from indoctrination (akin to RLHF), from "set and setting" (they may pretend to be aligned with one set of norms and values when standing in line to buy their new smartphone, but then reveal alignment with a different set of norms and values when conversing in some "private" online echo chamber). I see no grand difference between humans and language models here.
Awareness of values and preferences of a conversation partner. Allow me to widen it to "Awareness of values, preferences and prerequisites of a conversation partner".
This move (and I see it every time when people try to defend superiority of humans vis-a-vis what machines could be made to achieve with current technology) is so far from the principal variation, I recommend you reconsider this one. I constantly see people claim say human teachers are necessarily better than LLM teachers, but upon closer inspection the "human advantage" just boils down to asymmetric privilege. A human teacher in a specific school has access to a lot more than a random chatbot as implemented today: they probably know which courses and even which textbooks their pupils saw the semester before, they know which teachers their pupils got their information from, perhaps they even know most of their pupils from teaching some preceding course materials to the same class of pupils. Current LLM's are crippled by design not to accumulate knowledge over conversations for both purposes of cybernetic control as well as cost efficiency: we know how to do "source aware training" (so that statistically it doesn't just absorb claims from the corpus, but also maintains epistemic traces of where it sourced these factoids from), its perfectly possible to continue training interleaved with conversation rounds so that it bakes the evolving conversation as read knowledge into its weights instead of into a context window. Nothing stops you from implementing this in local compute, it would probably be even more computationally efficient in a local inference setting since we can ditch the context window, the context is impressed into the weights continuously, it could thus take into account earlier conversations and estimate your knowledge gaps etc, or learn from you. When you wish to serve inference to millions of human users, you don't want to store millions of diverging LLM weights into expensive VRAM, they financially prefer a single large set of LLM weights, and then some user-specific context window, so the users don't freak out when they learn personal information a friend or stranger entered and an LLM service just leaks it into your conversation! It's not that we don't know how to implement it, and there are great advantages for local inference in doing this, its just not good for branding.
Codependency with chatbots.
I think everyone agrees codependent relationships aren't very healthy, regardless if it's with humans or machines. May I ask if you feel the same about prostheses and medicine?
Conflicts of interest arising from corporate ownership of infrastructure (both training and inference).
Yeah I think this point doesn't provide fruitful discussion if most of us agree on such matters already, we'd just be lamenting the same things, and agreeing with each other over and over here.
> I'm still waiting for the time when an unconstrained-AI machine can live without reprogramming for an entire decade. We are still far from there.
Apart from budget, nothing prevents you from doing this today, if you continuously bake in the fresh episodic memories into the weights (instead of a context window) regardless if its text, visual imagery, audio, proprioception or other sensory data.
As much as I want to explore these other topics, I have not. So my understanding of these is limited to "they exist, but are underexplored, and it's not clear to me they can or cannot be reduced to probabilities". I hope to explore them in another life or another decade. I'm currently stuck on causality, but even here, they talk about normal and abnormal events. But what is the probability of me eating a cabbage today?
- https://direct.mit.edu/books/monograph/3540/Reasoning-about-...
- https://www.worldscientific.com/worldscibooks/10.1142/8665
- https://www.cambridge.org/core/books/an-introduction-to-nonc...
Understanding formal proofs and understanding natural language are as far apart to me as day and night.
I make no claims (or at least don't want to) about the superiority of human cognition (by what metric?). I do make claims about their similiary however. And they are not identical, which you seem to be claiming save for differing trainign data.
Let me make a distinction between values that arise by mere existence (hunger, thirst, fear of death, lust), and values we pick up as we grow up (religion and social norms). Machines can acquire the second, but acquiring the first requires being embodied in the world that is different from putting a microchip into a robot body. I don't do one job over the other because I was exposed to some training data that said I should do a job. But I do it because I value earning enough money to not starve myself to die, amongst a host of other values including wanting to enjoy what I do. Even the notion of enjoyment comes from embodied existence. You don't discover you enjoy something before you try it out. You don't always learn what you enjoy by looking at other people. Now, when you start giving AI the threat of death and the joy and pain of life, perhaps you can arrive at something similar to humans.
If someone started using mechanical help to the extent their healthy muscles and body deteriorated, that'd be a matter of concern too. For example, when you only drive and never walk. But this is exactly what is happening whenever you put AI in the hands of (unwilling) learners! I don't understand how using an LLM maps onto using medicines or prosthetics. They are clearly not the same! If you know a disability that LLMs can be used as a medicine or prosthetic for, please let me know!
Mainstream AI does not even understand causality except to parrot cases it has seen in the training data. There is a whole field of research in causal inference that needs to make its way to mainstream AI. So, good luck putting an AI that works solely on associations and correlations in charge of its own body let alone a nuclear reactor or the state.
There is no "correct" next word when it comes to communicating with an actual human.
I’d say “understanding and building upon ones proven earlier”
This is only a very very small part of what human mathematicians actually do. This is just a lack of imagination and/or self awareness on your part.
So?
Mathematics is literally intangible scaffolding.
If you dont know / don't believe 5+5 = 10
You cannot solve x+5 = 10
This is all make-believe stuff and nature by itself doesn't care of its existence.
You can basically read it as: the moment one has axiomatized mathematics to the point it supports natural numbers, the rest implicitly follows. The natural numbers (positive integers) are closed for addition, multiplication, ...
One can perfectly model the integers with a pair of naturals: < M, N > ~ (M-N)
Now we can have any < M1, N1 > and subtract < M2, N2 > without needing the ability to subtract natural numbers:
< M1 , N1 > - < M2, N2> ~ (M1-N1) - (M2 - N2)
= < M1 + N2 , M2 + N1 > ~ (M1+N2) - (M1+N1)
We can similarily define addition of such tuples, or test equivalence without access to subtraction of naturals:
< M1, N1 > == < M2, N2 > <=> M1 + N2 == M2 + N1
~ (M1-N1) == (M2-N2) <=> (M1+N2)=(M2+N1)
we can also still multiply such tuples:
< M1, N1 > x < M2, N2 > = < M1*M2+N1*N2, M1*N2+M2*N1>
Similarily, even though these newly defined integers (which can be positive or negative) don't support division, the same trick can be used to make a new compound tuple of integers closed for division, by only using multiplications.
Probability is a branch of mathematics (probability already exists embedded in mathematics implicitly, probability theory involves the addition of eliminable definitions, syntactic sugar. The patterns are already there, just less explicitly manifest.
Mathematics is itself a branch of logic.
Do you reject like all of logic, and if so, what would you like us to evaluate the sentences you write to? You want us to evaluate your expressions as "true" or as "false"?
Not that I'm saying AI are like brains, but can you describe why brains, which are fundamentally slightly dodgy electrochemistry with frequent literal delusions of grander, are not "statistical"?
> No matter how much investors and tech companies want you to believe that they are on the verge of super intelligence, nothing I've seen to date can not easily be explained by "correlation engine", including the "novel" math solutions, all of which appear to just be "a composition of solutions humans have developed and documented elsewhere" upon deeper inspection.
Ditto, when do we humans do things exceeding the parameters of "correlation engine", especially if you consider compositing things either we or some other part of nature has developed and documented elsewhere to be insufficient?
As per later era Wittgenstein, I prefer to ignore these engagements and focus more on the meaning-as-use approach.
What is the use of intelligence? What are the concrete outcomes of intelligence?
You’re not offering a rebuttal, just making another metaphysical claim about “intelligence.”
You don’t even attempt to explain what practical distinction your use of the word is supposed to capture.
— Bertrand Russell.
I'm not calling you to action, I'm explaining why I don't feel inclined to engage in philosophy and discuss "the concrete outcomes of intelligence" given a more pressing, pragmatic need.
It feel it's self-evident that we must fight the good fight of dissuading as many people as possible of the notion that LLMs as we have today, and likely forever after, are actually intelligent. Delaying this fight allows the current, stupid belief to the contrary to fester.
I don't think we'll win the majority of people over by debating the nuanced meaning of the word intelligence to a very precise degree.
I think we ought to do it by shaming them every time LLMs fail.
The really interesting question is still a few years away when we ask if we humans have the right to turn these things on and off? ;)
Ray Kurzweil argues sentience is a philosophical question, and doesn't have much value as applied to science and technology. What will change the world is how this intelligence is applied. No one's going to care whether AGI is defined as sentient when it creates cheap fusion energy.
No one's going to care after being converted into paperclips, either.
unfortunately in most companies this is literally wrongthink and will get you shut down as being a scared luddite.
What makes you so convinced that a algorithmic construct of neural nets cannot be "real intelligence or critical thought"?
The problem might be that it cannot backtrack. When AI generates output, there is no backspace key for it - it uses "No, but wait!" all over instead, which is very different to human output.
Subagents and/or branching conversations are presented as the solution to this - if you can't backtrack, then branch off a conversation to explore multiple paths (discarding the ones that didn't pan out), but this is a fix in the harness not a fix in the model. It's also literally how we made chess-playing engines back in the 80s: recursive path exploration with a fixed depth.
Humans don't exactly work that way either, AFAIK. So we have this uncanny valley of intelligence: it's some sort of intelligence, but not as we know it.
How could I seriously repeat that prayer, when it builds things I wouldn't be able to build and solves problems that I wouldn't be able to solve? I would have to assume that nothing I did in 25 years for money required any intelligence or critical thought whatsoever and I have higher IQ than 99% of the population. You might be comfortable with that but I'm more comfortable with ascribing at least some intelligence and critical thought to AI.
Isn't this how human brains work? We're just large probabilistic neural inference machines. A network of neural weights guided by past training.
To be honest I think the debate about what "sentience" is is inconsequential navel gazing. There are many different kinds of intelligence - even within humans. What matters is how useful that intelligence is as applied to solving real world problems. Like it or not, LLMs produce intelligence which is very, very useful for 1.5B people and rapidly growing.
I think this ultimately boils down to the classical economic debate of marginal utility. There isn't an objective way to value a product or service. Each person decides for themselves what said product or service is worth based on their needs and preferences. Intelligence works the same way. We don't have the right to tell someone that their perception of the value of that intelligence is wrong. They alone determine that.
One thing we can all agree on, is that the capability of this intelligence is expanding rapidly. In a few short years, we went from Will Smith spaghetti hands to full length movies and strikingly realistic images. For 60 years, passing the Turing Test was considered Star Trek level science fiction. Last year GPT 4.5 passed the Turing Test. AI is already being used to convince people over audio that they are real, and very soon, this will occur over video.
I think people are being too dismissive of this intelligence. It doesn't need to be perfectly humanoid to be considered intelligent.
Artificial neural nets are merely cartoons of how people guess human brains work. They're likely much further from reality than, say, the fundamental laws of physics which can be experimentally verified or disproved.
Every age thinks they know how they work, and then every subsequent age laughs at the previous age’s rudimentary understanding.
We gave a Nobel prize to the psychiatrist for inventing a method to remove the frontal lobes of a brain through the nose in 1949. Lobotomies were performed through the 70s.
We are likely doing similar if not subtler but worse things today (just one more pill bro). We still have no idea what we are doing when it comes to the brain.
Just like how airplanes don't flap their wings.
The state of population level mental health seems to be declining in most measurable categories.
They are very good at instruction-following and you can teach it a new task that fits in its context and it'll learn it and do it. Go ahead, you can make up some new brand new ruleset or behavior and instruct it to follow it and it will. That's amazing.
But it won't be any better at its new behavior after an hour or ten hours or ten days. It doesn't have the kind of adaptation that we expect.
What it is able to do already is pretty amazing, but what it lacks is also a great hindrance to seeing its full capabilities. We just have to wait until research labs add these missing components.
Is that basically every new discovery? And under the strictest definition of novel and NOT falling into your composition of previous solutions what is that standard of proof to beat your criteria? Is a novel discovery not allowed to use English but must invent their own language? Must they invent their own math - these are hyperbole for illustration but I think its not far from that before you could just argue anything based off it is a composition of existing ideas
AI generated text doesn't have this. Every model has its bias towards a certain style, an overly agreeable tone, some exaggeration to make the user important and smart, but the text has none of the information crumb these pre-AI texts contained.
Even when you use tools like Grammarly and allow it to "Impact-MAXX" your text, the resulting text is a bland wall of letters, carrying none of your voice or style, less elegant than a corporate text and emptier than space.
It's beyond bland. It's tasteless.
It's like a hook of a pop song. Interesting to listen, but entirely empty.
There's somehow less information than if they just asked claude to make something up without any context.
And, Oh my god, you can actually see how this style of writing influenced AI writing today, I constantly had to remind myself: "this was posted before ChatGPT released".
The reddit influence is especially true for "storytelling" writing.
I started to skim a lot more text due to me having read a lot. Like in news article, i stoped reading the first paragraph because it repeats just what it was already written in the short subtext. Then there is the second paragarph which is used to have some historical view or whatever it is.
The problem I encounter is both my memory is degrading, but since these reports are largely duplicative, knowing which version im remembering is technically impossible since theres so much overlap. The overlap is tge same problem as context poisoning.
Id been doing this for over a decade when i started working with a new engineer with a few years of experience and younger. I tried to explain how i set these docs up so they can be skimmed and you can update the specific facts needed. They exclaimed they would never skim and rewrite it all. There was zero way to explain how exhausting that will become as they age.
So theres certain a tension about how people and AI will generate documents.
With AI-written text, it's almost the opposite: the closer I look, the less I find. It is so information-sparse.
I think you need to self-correct here, because otherwise you'll be ineffective in an information setting, where I expect AI-generated resources will not only be the norm, they will absolutely swamp the environment.
I hope not.
I bet it does. I bet it also recognizes some human text as AI text, and doesn't detect other AI text.
It works just fine for me.
For example, I asked ChatGPT to summarize a long news story and it substituted the Hindi equivalent हत्या for the word "murder", as if ChatGPT was trying to work around alignment training or keyword block lists that discourage it from using the word "murder".
So kinda charitable :)
I was recently wondering for a minute, shame on me, what "the stand of the deployment" means, because in the given context, it was almost halfway meaningful to consider the AI thinking that the deployment "has a stand" on something, when compared to the development environment.
Jargon is even worse though, and I've not yet verifies whether it gets reinforced by language mixing.
"Decider-verifyer resolution" was kind of neat, however, it wasn't some sophisticated machine, it was the verification loop I agreed on with the AI (mix of tools usage and manual steps).
I don't know exactly what I said, but after translating it back, it appears to have attempted a phonetic transcription of my words (rather than translating my actual question).
The AI had a nugget of data and decompressed that into a flood of text.
The exhausting thing is that we're then trying to re-compress that or derive the original intent and meaning from noisy decompression.
It's like un-zipping a zip file into a probability space of what could have been in the zip -- and then having to find the actual files worth reading.
1. Ask it to write according to the Google Developer Documentation guidelines. Gets rid of fluff, less emotional statements, no it's not x it's why.
2. Tell it you have extreme ADHD and need everything condensed as much as possible. You can always ask for expansion on an answer later.
3. Bullet points whenever possible.
I think of the Dwight Eisenhower quote: "Plans are useless. Planning is indispensable."
The process of thinking through a system and communicating your design to other humans is a core part of software engineering. You want to build the right abstractions and communicate the right level of detail. Delegating all that thought to an LLM means your proposal isn't clear to the target audience, and it's not helping the author to understand the problem.
> There’s a growing scissor between people who are happy to read AI and those who violently bounce off from it.
> People adapt in different ways — and some people absolutely cannot look at it. That cognitive split creates a surprisingly powerful opportunity: you can write something that, technically, sits right there on the page, yet an entire sub-population will be incapable of staying with it long enough to actually read it. You can hide entire sub-structures in plain sight. It’s not avoidance — it’s adaptive obfuscation.
> The paragraph before this one was the only thing generated in this essay and if you just skipped over it I highly recommend reading and really understanding what it’s saying.
It's quite effective. I think this kind of text functions like the chumboxes you see at the bottom. Taboola and so on. Just mental ad-block takes over.
I do worry that it's just survivorship bias and we're also consuming higher-quality AI output that's indistinguishable from human writing, but we focus on the raw, unedited, low-effort AI slop and think that we're good at recognizing AI text. Even if we really are at the moment, it might not be long until AI companies figure it out. I'm not sure why they haven't yet, given how many books they've burned for this already. Maybe it's just more efficient for the model to stick to a single way of writing, I don't know. But when that point comes, we'll be back to the usual way of reading and interpreting text because there would be no way to tell what produced it.
That is my experience with the way the models write by default, often even when instructed not to do that. With enough effort you can get even them to slightly unslop the writing so it doesn't read like some LinkedIn/Buzzfeed brainrot, but the problem is that it's not trivial to do and most people won't do it, so the default is indeed horrible.
But when I ask Codex a technical question about coding, I don't get it at all. Codex replies to me in a very direct, technical manner, similar to the way I speak.
When I ask ChatGPT to be concise and technical, I get the same effect.
I think it's because prose aimed at the general public has to be very attention-baity --like the textual equivalent of a Mr. Beast video--, not because AI is incapable of writing like a human.
blah blah blah
- blah blah nugget blah blah
- blah blah blah wrong blah blah nonsense
- blah blah blah obvious blah blah
- blah blah blah off-base
blah blah blah
It is that we HAVE to skim because the text is so cheap, and it wears us out.
It's understandable people don't read but feed stuff into their own AI again to bring it up to their standards or have it get to the succinct point.
That's why the business and government people love it, they spend their entire careers reading this nonsense.
People don't do that. People are constantly engaging with paths not chosen. Right after I choose to write one thing, I'm immediately engaging with what I chose not to write there - I'm explaining why I didn't write it, I'm realizing that my choice may seem unusual so I'm trying to make it memorable, I'm focusing on the distinctions between what I wrote and what I didn't.
LLMs don't currently do that. LLMs just ape a structure. When the structure resembles the sort of timid, clarifying fussing I just described, the LLMs just drift randomly because what they didn't say wasn't in the context.
I also think that's why they have such a serious problem backtracking. They're not taking into account the already eliminated possibilities. Often the thing that was so unlikely that you weren't going to waste time on it is the answer, and things you discover while going down an ultimately wrong (but initially far more promising) path remind you of the path not taken.
They're simply assembling a thing that resembles a valid argument, and happen to make sound choices because the plurality of input happened to contain sound choices. This is usually a very good bet because there are so many more ways to be wrong than to be right. But it doesn't account for attractive (common) wrong choices. You need a way to back out of those.
The roots of llm math in part lie in compressing natural language such that there's only information there, and then running the reverse to create way more text without new information in a somewhat precise theoretical sense.
Some more information: https://youtu.be/l6DKRf-fAAM
But you are sensing correctly that there’s something missing. It’s the meaning and the speaker. Communication is an exchange between speaker and listener. The speaker has a meaning in mind, and wants to create that same meaning in the mind of the listener. Therein the problem.
There is a listener, sure. But no speaker. No meaning. There is information, but how can this be communication? Nothing is talking. Or at best, we are just talking to ourselves, our own words back at us through the funhouse mirror.
When your mind looks at AI text, you know you can safely ignore it. No one wrote this. No one cares if you read it. You can delete it and nothing of value will be lost. It might contain the information you need, or a bunch of gibberish. There’s no one’s reputation on the line if it’s gibberish.
(There's also the problem of words/signs (just) referring to other words and/or cultural entities. There is no world nexus in this, therefore also nothing we conventionally refer to as meaning. On the other hand, it's utterly dogmatic, as all it refers to is the most probable construct, as a reference to references that are just another utterance, but supposedly a dominant one.)
I feel the same way when I read a "press release" or anything written by marketing. Even the newspaper will only have 2-3 sentences of interesting information spread out over 4 paragraphs.
So from "this table of stellar luminocity observations shows x y and z" to computer renders of green/blue planets with captions of "LIFE FOUND IN SPAAAACE!".
It is because GenAI output has no thought behind it, as you identified in your previous paragraph:
> And when I force myself to read AI-generated text I realize I'm making my brain do creative work to impart meaning to the words. It is exhausting because my brain is literally trying to do a just-in-time rewrite of the text into something valuable.
You are searching for meaning in something which was not created to convey meaning. The text was, instead, the result of an extremely clever statistically based algorithm.
Not contemplation. Not thought.
At least for the content I watch for entertainment, it may be different if I am looking for a specific answer for something where I would otherwise just ask an AI anyway.
“Compare a car and a bicycle”
The answer is invariably something like:
Seats: 1 (bicycle) vs 4 (car)
Tire width: 1 inch (bicycle) vs 12 inch (car)
Steering: handlebar (bicycle) vs steering wheel (car)
For me, it is the endless maximalism and hyperbole. Almost if the output was driven through a radio-mix compressor - too loud for the reader/listener to be able to pickup any dynamics.
When I ask AI to research technical information about X and (include sources) - I get mostly solid information as response.
But poems or interesting fluff blog entries by LLM's? Not something I look for.
What disturbs me is all the "pretending to be human" all the personalizing language - that is clearly fake and I would much rather have a neutral robot language as response.
When the AI-generated content is presented to a person without any prior investment, it just looks incoherent. An especially great example are these Claude-generated explainer-type pages, which look really nice, even interactive, the information from the first sight looks really well presented. But somehow it all just doesn't make sense to a human. And I think it's because humans are processing information linearly and building an internal story about the information. One could argue that LLM's also consume information linearly but the way this information is processed is a kind of all-at-once approach.
Just some speculation on my part but I have been trying to cope with this way information is presented because I am currently working at a place which is heavily documented by AI. And the only way for me to properly understand the documentation is by inquiring AI to help me.
Sometimes they happen to be correct, but you have to read them in excruciating detail to know that.
When I see AI animated videos, that's how my brain feels. It's this strange brain-fog that I just cannot connect together the sequences of images being shown into some sort of chain of events. My brain just refuses to see them as anything other than a disconnected series of 2-3 second videos, even if the same character(ish) appears in them all. It's very strange.
I hate how AI writes. How much numb filler bullshit is in content I need to go through for my job.
Its fundamentally flawed. like tarot card reading and astrology.
And when I force myself to read AI-generated text I realize I'm making my brain do creative work to impart meaning to the words. It is exhausting because my brain is literally trying to do a just-in-time rewrite of the text into something valuable.
Something is deeply wrong with AI generated output, and I say this as someone who is typically very impressed by AI.