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We actually understand AI quite well. It embeds questions and answers in a high dimensional space. Sometimes you get lucky and it splices together a good answer

We actually understand AI quite well. It embeds questions and answers in a high dimensional space. Sometimes you get lucky and it splices together a good answer to a math problem that no one’s seriously looked at in 20 years. Other times it starts talking about Goblins when you ask it about math. Comparing it to an alien intelligence is ridiculous. McKenna was right that things would get weird. I believe he compared it to a carnival circus. Well that’s exactly what we got.

jeremyjh 6mo ago
We understand the low level math quite well. We do not understand the source of emergent behavior. https://arxiv.org/html/2210.13382v5#abstract https://arxiv.org/html/2210.13382v5#abstract
- bondarchuk 6mo ago
  There's no end to arguing with someone who claims they don't understand something, they could always just keep repeating "nevertheless I don't understand it"... You could keep shifting the goalposts for "real understanding" until one is required to hold the effects of every training iteration on every single parameter in their minds simultaneously. Obviously "we" understand some things (both low level and high level) to varying degrees and don't understand some others. To claim there is nothing left to know is silly but to claim that nothing is understood about high-level emergence is silly as well.
  - jeremyjh 6mo ago
    Is there a book or paper where I can read a description of how high-level emergent behavior works? The papers I've seen are researchers trying to puzzle it out with probes, and their insights are very limited in scope and there is always a lot more research to be done.
- tremon 6mo ago
  There is no behaviour, there is only reflexes. Behaviour implies autonomous action; what we're seeing is only stimulus response.
forlorn_mammoth 6mo ago
Hey, about that high dimensional space, is it continuous or discrete? Also, I'm curious what you mean by "embed", the word implies a topographical mapping from "words" to some "high dimensional space". What are the topographical properties of words which are relevant for the task, and does the mapping preserve these? circling back to the first point, are words continuous or discrete? is the space of all words differentiatable?
- therobots927 6mo ago
  Discrete. But my understanding is that for all intents and purposes it is differentiable. None of this means that you can infer the input space (human brain) from the output space (language). You can approximate it. But you cannot replicate it no matter how many weights are in your model. Or how many rows you have in your dataset. And it’s an open question of how good that approximation actually is. The Turing test is a red herring, and has nothing to do with the fundamental question of AGI. Unless you have access to a Dyson sphere where you can simulate primate evolution. Existing datasets aren’t even close to that kind of training set.
libraryofbabel 6mo ago
I think this is a case of that mildly apocryphal Richard Feynman quote: "if you think you understand quantum mechanics, you don't understand quantum mechanics." I understand LLM architecture internals just fine. I can write you the attention mechanism on a whiteboard from memory. That doesn't mean I understand the emergent behaviors within SoTA LLMs at all. Go talk to a mechanistic interpretability researcher at Anthropic and you'll find they won't claim to understand it either, although we've all learned a lot over the last few years. Consider this: the math and architecture in the latest generation of LLMs (certainly the open weights ones, almost certainly the closed ones too) is not that different from GPT-2, which came out in 2019. The attention mechanism is the same. The general principle is the same: project tokens up into embedding space, pass through a bunch of layers of attention + feedforward, project down again, sample. (Sure, there's some new tricks bolted on: RoPE, MoE, but they don't change the architecture all that much.) But, and here's the crux - if you'd told me in 2019 that an LLM in 2026 would have the capabilities that Opus 4.7 or GPT 5.5 have now (in math, coding, etc), I would not have believed you. That is emergent behavior ("grown, not made", as the saying is) coming out of scaling up, larger datasets, and especially new RL and RLVR training methods. If you understand it, you should publish a paper in Nature right now, because nobody else really does.
- therobots927 6mo ago
  I wouldn’t use the phrase “emergent behavior” when talking about a model trained on a larger dataset. The model is designed to learn statistical patterns from that data - of course giving it more data allows it to learn higher level patterns of language and apparent “reasoning ability”. I don’t think there’s anything mysterious going on. That’s why I said we understand how LLMs work. We may not know exactly how they’re able to produce seemingly miraculous responses to prompts. That’s because the statistical patterns it’s identifying are embedded in the weights somewhere, and we don’t know where they are or how to generalize our understanding of them. To me that’s not suggestive that this is an “alien intelligence” that we’re just too small minded to understand. It’s a statistical memorization / information compression machine with a fragmented database. Nothing more. Nothing less.
  - 6mo ago

jeremyjh 6mo ago

We understand the low level math quite well. We do not understand the source of emergent behavior. https://arxiv.org/html/2210.13382v5#abstract https://arxiv.org/html/2210.13382v5#abstract

bondarchuk 6mo ago
There's no end to arguing with someone who claims they don't understand something, they could always just keep repeating "nevertheless I don't understand it"... You could keep shifting the goalposts for "real understanding" until one is required to hold the effects of every training iteration on every single parameter in their minds simultaneously. Obviously "we" understand some things (both low level and high level) to varying degrees and don't understand some others. To claim there is nothing left to know is silly but to claim that nothing is understood about high-level emergence is silly as well.
- jeremyjh 6mo ago
  Is there a book or paper where I can read a description of how high-level emergent behavior works? The papers I've seen are researchers trying to puzzle it out with probes, and their insights are very limited in scope and there is always a lot more research to be done.
tremon 6mo ago
There is no behaviour, there is only reflexes. Behaviour implies autonomous action; what we're seeing is only stimulus response.

forlorn_mammoth 6mo ago

Hey, about that high dimensional space, is it continuous or discrete? Also, I'm curious what you mean by "embed", the word implies a topographical mapping from "words" to some "high dimensional space". What are the topographical properties of words which are relevant for the task, and does the mapping preserve these? circling back to the first point, are words continuous or discrete? is the space of all words differentiatable?

therobots927 6mo ago
Discrete. But my understanding is that for all intents and purposes it is differentiable. None of this means that you can infer the input space (human brain) from the output space (language). You can approximate it. But you cannot replicate it no matter how many weights are in your model. Or how many rows you have in your dataset. And it’s an open question of how good that approximation actually is. The Turing test is a red herring, and has nothing to do with the fundamental question of AGI. Unless you have access to a Dyson sphere where you can simulate primate evolution. Existing datasets aren’t even close to that kind of training set.

libraryofbabel 6mo ago

I think this is a case of that mildly apocryphal Richard Feynman quote: "if you think you understand quantum mechanics, you don't understand quantum mechanics." I understand LLM architecture internals just fine. I can write you the attention mechanism on a whiteboard from memory. That doesn't mean I understand the emergent behaviors within SoTA LLMs at all. Go talk to a mechanistic interpretability researcher at Anthropic and you'll find they won't claim to understand it either, although we've all learned a lot over the last few years. Consider this: the math and architecture in the latest generation of LLMs (certainly the open weights ones, almost certainly the closed ones too) is not that different from GPT-2, which came out in 2019. The attention mechanism is the same. The general principle is the same: project tokens up into embedding space, pass through a bunch of layers of attention + feedforward, project down again, sample. (Sure, there's some new tricks bolted on: RoPE, MoE, but they don't change the architecture all that much.) But, and here's the crux - if you'd told me in 2019 that an LLM in 2026 would have the capabilities that Opus 4.7 or GPT 5.5 have now (in math, coding, etc), I would not have believed you. That is emergent behavior ("grown, not made", as the saying is) coming out of scaling up, larger datasets, and especially new RL and RLVR training methods. If you understand it, you should publish a paper in Nature right now, because nobody else really does.

therobots927 6mo ago
I wouldn’t use the phrase “emergent behavior” when talking about a model trained on a larger dataset. The model is designed to learn statistical patterns from that data - of course giving it more data allows it to learn higher level patterns of language and apparent “reasoning ability”. I don’t think there’s anything mysterious going on. That’s why I said we understand how LLMs work. We may not know exactly how they’re able to produce seemingly miraculous responses to prompts. That’s because the statistical patterns it’s identifying are embedded in the weights somewhere, and we don’t know where they are or how to generalize our understanding of them. To me that’s not suggestive that this is an “alien intelligence” that we’re just too small minded to understand. It’s a statistical memorization / information compression machine with a fragmented database. Nothing more. Nothing less.
- 6mo ago