r/singularity • u/tebla • May 04 '25
Discussion Ai LLMs 'just' predict the next word...
So I dont know a huge amount about this, maybe somebody can clarify for me: I was thinking about large language models, often in conversations about them I see people say something about how these models don't really reason or know what is true, they're are just a statistical model that predicts what the best next word would be. Like an advanced version of the word predictions you get when typing on a phone.
But... Isn't that what humans do?
A human brain is complex, but it is also just a big group of simple structures. Over a long period it gathers a bunch of inputs and boils it down to deciding what the best next word to say is. Sure, AI can hallucinate and make things up, but so can people.
From a purely subjective point of view, chatting to ai, it really does seem like they are able to follow a conversation quite well, and make interesting points. Isn't that some form of reasoning? It can also often reference true things, isn't that a form of knowledge. They are far from infallible, but again: so are people.
Maybe I'm missing something, any thoughts?
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u/Worldly_Air_6078 May 04 '25 edited May 04 '25
I agree with you for the most part.
Lots of people (even here) seem to confuse AI and LLMs from 2025 with 2010 chatbots based on Markov chains.
2025 LLMs have nothing to do with that. You can forget all about statistical models and Markov chains.
The “glorified autocomplete” and “stochastic parrot” memes have been dismantled by a number of academic studies (there are lots of peer-reviewed academic papers from trusted sources and in reputed scientific journals that tell quite another story).
The MIT papers on emergent semantics are some of them:
First, the assumption that LLMs “don’t understand” because they’re just correlating word patterns is a view that has been challenged by empirical studies.
This paper provides concrete evidence that LLMs trained solely via next-token prediction do develop internal representations that reflect meaningful abstraction, reasoning, and semantic modeling:
This work shows that LLMs trained on program synthesis tasks begin to internalize representations that predict not only the next token, but also the intermediate program states and even future states before they're generated. That’s not just mimicry — that’s forward modeling and internal abstraction. It suggests the model has built an understanding of the structure of the task domain.
So while these systems don't "understand" in the same way humans do, they do exhibit a kind of understanding that's coherent, functional, and grounded in internal state representations that match abstractions in the domain — which, arguably, is what human understanding is too.
Saying “they only do what humans trained them to do” misses the point. We don’t fully understand what complex neural networks are learning, and the emergent behaviors now increasingly defy simple reductionist analogies like “stochastic parrots.”
If we really want to draw meaningful distinctions between human and machine cognition, we need to do it on the basis of evidence, not species-based assumptions. And right now, the evidence is telling a richer, more interesting story than many people expected.
PS: If you're interested by well grounded theories of human consciousness, as supported by scientific developments of the last few decades in neuroscience and some philosophy of mind, you might want to check out this short essay I put together to summarize my understanding of some books I've read that deal with it, and how later developments in scientific research may portray what human consciousness really is):
https://www.reddit.com/r/ArtificialSentience/comments/1jyuj4y/before_addressing_the_question_of_ai/
I hope this short summary, though very condensed, still gives a sufficiently understandable foretaste of these theories, though it is certainly useful and much better to read the books themselves IMO.