You know, I've been following a rule where if I open any article and there's meme pictures in it, I instantly close it and don't bother. I feel like this has been a pretty solid rule of thumb for weeding out stuff I shouldn't waste my time on.
The Singularity will occur on a Tuesday
501–510 of 800 posts
Re: The Singularity will occur on a Tuesday
#502It's worth remembering that this is all happening because of video games ! It is highly unlikely that the hardware which makes LLMs possible would have been developed otherwise. Isn't that amazing ? Just like internet grew because of p*rn, AI grew because of video games. Of course, that's just a funny angle. The way I see it, AI isn't accidental. Its inception has been in the first chips, the Internet, Open Source, G…
Google DeepMind can trace part of it's evolution back to a playtester for the video game Syndicate who saw an opportunity to improve the AI of game NPCs.
Re: The Singularity will occur on a Tuesday
#503Earlier quoted context omitted.
> We really have no idea how did ability to have a conversation emerge from predicting the next token. Uh yes, we do. It works in precisely the same way that you can walk from "here" to "there" by taking a step towards "there", and then repeating. The cognitive dissonance comes when we conflate this way of "having a conversation" (two people converse) and assume that the fact that they produce similar outputs means t…
> It works in precisely the same way that you can walk from "here" to "there" by taking a step towards "there", and then repeating. It's funny how, in order to explain one complex phenomenon, you took an even more complex phenomenon as if it somehow simplifies it.
Sorry, can't tell if that's sarcasm or not.
I wasn't referring to the biomechanical process of walking, I was referring to the process of gradient descent, which is well understood and yes, quite simple.
Re: The Singularity will occur on a Tuesday
#504Earlier quoted context omitted.
>It's pattern matching, likely from typography texts and descriptions of umbrellas. "Pattern matching" is not an explanation of anything, nor does it answer the question I posed. You basically hand waved the problem away in conveniently vague and non-descriptive phrase. Do you think you could publish that in a paper for ext ? >Why am I confident that it's not actually doing spatial reasoning? At least in the case of…
>Do you think you could publish that in a paper for ext ? You seem to think it's not 'just' tensor arithmetic. Have you read any of the seminal papers on neutral networks, say? It's [complex] pattern matching as the parent said. If you want models to draw composite shapes based on letter forms and typography then you need to train them (or at least fine-tune them) to do that. I still get opposite (antonym) confusion…
>You seem to think it's not 'just' tensor arithmetic.
If I asked you to explain how a car works and you responded with a lecture on metallic bonding in steel, you wouldn’t be saying anything false, but you also wouldn’t be explaining how a car works. You’d be describing an implementation substrate, not a mechanism at the level the question lives at.
Likewise, “it’s tensor arithmetic” is a statement about what the computer physically does, not what computation the model has learned (or how that computation is organized) that makes it behave as it does. It sheds essentially zero light on why the system answers addition correctly, fails on antonyms, hallucinates, generalizes, or forms internal abstractions.
So no: “tensor arithmetic” is not an explanation of LLM behavior in any useful sense. It’s the equivalent of saying “cars move because atoms.”
>It's [complex] pattern matching as the parent said
“Pattern matching”, whether you add [complex] to it or not is not an explanation. It gestures vaguely at “something statistical” without specifying what is matched to what, where, and by what mechanism. If you wrote “it’s complex pattern matching” in the Methods section of a paper, you’d be laughed out of review. It’s a god-of-the-gaps phrase: whenever we don’t know or understand the mechanism, we say “pattern matching” and move on, but make no mistake, it's utterly meaningless and you've managed to say absolutely nothing at all.
And note what this conveniently ignores: modern interpretability work has repeatedly shown that next-token prediction can produce structured internal state that is not well-described as “pattern matching strings”.
- Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (https://openreview.net/forum?id=DeG07_TcZvT) and Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models (https://openreview.net/forum?id=PPTrmvEnpW&referrer=%5Bthe%2...
Transformers trained on Othello or Chess games (same next token prediction) were demonstrated to have developed internal representations of the rules of the game. When a model predicted the next move in Othello, it wasn't just "pattern matching strings", it had constructed an internal map of the board state you could alter and probe. For Chess, it had even found a way to estimate a player's skill to better predict the next move.
There are other interpretability papers even more interesting than those. Read them, and perhaps you'll understand how little we know.
On the Biology of a Large Language Model - https://transformer-circuits.pub/2025/attribution-graphs/bio...
Emergent Introspective Awareness in Large Language Models - https://transformer-circuits.pub/2025/introspection/index.ht...
>That said, you claim the parent is wrong. How would you describe LLM models, or generative "AI" models in the confines of a forum post, that demonstrates their error? Happy for you to make reference to academic papers that can aid understanding your position.
Nobody understands LLMs anywhere near enough to propose a complete theory that explains all their behaviors and failure modes. The people who think they do are the ones who understand them the least.
What we can say:
- LLMs are trained via next-token prediction and, in doing so, are incentivized to discover algorithms, heuristics, and internal world models that compress training data efficiently.
- These learned algorithms are not hand-coded; they are discovered during training in high-dimensional weight space and because of this, they are largely unknown to us.
- Interpretability research shows these models learn task-specific circuits and representations, some interpretable, many not.
- We do not have a unified theory of what algorithms a given model has learned for most tasks, nor do we fully understand how these algorithms compose or interfere.
Re: The Singularity will occur on a Tuesday
#505[dead]
Re: The Singularity will occur on a Tuesday
#506Earlier quoted context omitted.
You're putting a bunch of words in the parent commenter's mouth, and arguing against a strawman. In this context, "here’s how LLMs actually work" is what allows someone to have an informed opinion on whether a singularity is coming or not. If you don't understand how they work, then any company trying to sell their AI, or any random person on the Internet, can easily convince you that a singularity is coming without…
The problem is, there's two groups: One says "well, it was built as a bunch of pieces, so it can only do the thing the pieces can do", which is reasonably dismissed by noting that basically the only people predicting current LLM capabilities are the ones who are remarkably worried about a singularity occurring. The other says "we can evaluate capabilities and notice that LLMs keep gaining new features at an exponenti…
False dichotomy. One can believe that LLMs are capable of more than their constituent parts without necessarily believing that their real-world utility is growing at a hyperbolic rate.
Re: The Singularity will occur on a Tuesday
#507Great article, super fun. > In 2025, 1.1 million layoffs were announced. Only the sixth time that threshold has been breached since 1993. Over 55,000 explicitly cited AI. But HBR found that companies are cutting based on AI's potential, not its performance. The displacement is anticipatory. You have to wonder if this was coming regardless of what technological or economic event triggered it. It is baffling to me that…
I don’t think a lot of people here have been in the typists room or hung out with the secretaries. There were a lot of people taking care of all the things going and this has been downloaded and further downloaded. There was a time I didn’t have to do my expenses. I had someone just know where I was and who I was working for and and took care of it. We talked when there was something that didn’t make sense. Thanks to…
My first boss couldn't type. At all. He would dictate things to his secretary, who would then type them up as memorandums, and distribute to whoever needed them (on paper), and/or post them on noticeboards for everyone to read.
Then we got email, and he retired. His successor can type and the secretary position was made redundant.
Re: The Singularity will occur on a Tuesday
#508Earlier quoted context omitted.
Russell's chicken (or turkey) would like a word. https://en.wikipedia.org/wiki/Turkey_illusion
I love that you brought this up. Chickens are killed ALL the time. It’s a recurring mass event. If you were a smart chicken you could see that pattern and put it into a formula. In contrast, the end of Humanity would be a singular event. It’s even in the name… And that is fiction / speculation in comparison. It’s not backed by any data. Human survival over 300,000 years by contrast is. I mean it’s fine to dream thing…
One thing I've wondered about is:
Suppose a civilization (but not species) ending event happens.
The industrial revolution was fueled (literally) by easy-to-extract fossil fuels. Do we have enough of those left to repeat the revolution and bootstrap exploitation of other energy sources?
Re: The Singularity will occur on a Tuesday
#509Earlier quoted context omitted.
Honestly, you are just confused. With LLMs, The "knowing" you're describing is trivial and doesn't really constitute knowing at all. It's just the physics of the substrate. When people say LLMs are a black box, they aren't talking about the hardware or the fact that it's "math all the way down." They are talking about interpretability. If I hand you a 175-billion parameter tensor, your 'knowledge' of logic gates does…
It sounds like you're looking for something more than the simple reality that the math is what's going on. It's a complex system that can't simply be debugged through[1], but that doesn't mean it isn't "understood". This reminds me of Searle's insipid Chinese Room; the rebuttal (which he never had an answer for) is that "the room understands Chinese". It's just not satisfying to someone steeped in cultural traditions…
>It sounds like you're looking for something more than the simple reality that the math is what's going on.
Train a tiny transformer on addition pairs (i.e i.e '38393 + 79628 = 118021') and it will learn an algorithm for addition to minimize next token error. This is not immediately obvious. You won't be able to just look at the matrix multiplications and see what addition implementation it subscribes to but we know this from tedious interpretability research on the features of the model. See, this addition transformer is an example of a model we do understand.
So those inscrutable matrix multiplications do have underlying meaning and multiple interpretability papers have alluded as much, even if we don't understand it 99% of the time.
I'm very fine with simply saying 'LLMs understand Language' and calling it a day. I don't care for Searle's Chinese Room either. What I'm not going to tell you is that we understand how LLMs understand language.
Re: The Singularity will occur on a Tuesday
#510Earlier quoted context omitted.
> We really have no idea how did ability to have a conversation emerge from predicting the next token. Maybe you don't. To be clear, this is benefiting massively from hindsight, just as how if I didn't know that combustion engines worked, I probably wouldn't have dreamed up how to make one, but the emergent conversational capabilities from LLMs are pretty obvious. In a massive dataset of human writing, the answer to…
If such a simplistic explanation was true, LLM's would only be able to answer things that had been asked before, and where at least a 'fuzzy' textual question/answer match was available. This is clearly not the case. In practice you can prompt the LLM with such a large number of constraints, so large that the combinatorial explosion ensures no one asked that before. And you will still get a relevant answer combining…
I think you're confusing OP for the people who claim that there is zero functional difference between an LLM and a search engine that just parrots stuff already in it. But they never made such a claim. Here, let me try: the simplest explanation for how next token estimation leads to a model that often produces true answers is that for most inputs, the most likely next token is true. Given their size and the way they're trained, LLMs obviously don't just ingest training data like a big archive, they contain something like an abstract representation of tokens and concepts. While not exactly like human knowledge, the network is large and deep enough that LLMs are capable of predicting true statements based on preceding text. This also enables them to answer questions not in their training dataset, although accuracy obviously suffers the further you deviate from known topics. The most likely next token to any question is the true answer, so they essentially ended up being trained to estimate truth.
I'm not saying this is bad or underwhelming, by the way. It's incredible how far people were able to push machine learning with just the knowledge we have now, and how they're still making process. I'm just saying it's not magic. It's not something like an unsolved problem in mathematics.