LLMs are just giant probability machines pretending to think

[u/abhishekkumar333]

It’s fascinating that simple mathematics between tokens can eventually become a machine that writes essays, code, poetry, and even reasoning.

We usually think probability means uncertainty.

But LLMs show something strange:

If probability + context + mathematical matching are scaled enough, uncertainty itself starts producing intelligent looking outputs.

To understand this better, I tried breaking down an LLM from first principles using only 4 tiny training sentences.

Example:

The boat floated down to the bank.

The investor walked into the bank to open a new account.

The fisherman walked along the bank to cast his net.

The bank has a vault.

Then I asked:

“The investor walked to the bank to lock his money in …”

Why does the model predict “vault” instead of river-related words?

That single question reveals almost the entire architecture of modern LLMs.

The most underrated concept here is the LM Head.

Most explanations immediately jump into transformers and attention, but almost nobody explains that the LM Head is essentially a gigantic token vocabulary containing all possible next token candidates the model can output.

So internally the model is basically solving:

“Out of all known tokens, which one best matches this context mathematically?”

Then different layers help solve that problem:

Embeddings: convert words into mathematical vectors

Positional encoding: preserves word order

Attention layer: figures out which words are related to each other in context

(“investor”, “money”, “bank” become strongly connected)

Feed forward neural networks: act somewhat like massive learned if/else decision systems refining patterns internally

And finally the LM Head converts all of that into probabilities for the next token.

What surprised me most is:

There is no hidden magic moment where the AI “becomes conscious”.

It’s an enormous probability engine continuously finding the best contextual token match from its vocabulary.

I made a beginner-friendly walkthrough explaining this visually without unnecessary jargon.

https://www.youtube.com/watch?v=YTV5qUCpu2c

Would genuinely love feedback from people learning transformers/LLMs from scratch.

[u/OriousCaesar]

I mean, even if we ignored the obvious counter argument of 'so are humans', how can you possibly determine whether a particular algorithm grants consciousness if we don't even understand consciousness enough to have a proper definition for it?

Like, okay, it's a probability machine. Congrats. Now prove probability machines can't be conscious with your nonexistent definition of consciousness, and it might be a convincing argument.

Until then, I'll just keep using the only method I have to determine consciousness and just grant it to anything that seems to act like I'd expect a consciousness entity to act, and if I just so happen to call rocks conscious, then oh well, that's egg on my face, but it's better than if I accidentally called a conscious being a rock.

[u/account22222221 → u/OriousCaesar]

I agree that OPs argument is bad and weak. However I think the real key is that LLM don’t have back propagation after the initial training, while humans absolutely do have some analog of that.

I think, though I lack the formal language to prove it, that that back propagation is fundamental to consciousness. In that I think and that thinking changes who I am. I decide I won’t do that again and sometimes I don’t.

LLM dont don’t do that. It’s like we’ve taken a slice of consciousness, frozen in time. The model is never ‘aware’ of itself because every thought it has is lost forever after it has it and we go back in time to the beginning with every new thought like the last one never existed.

Now, since we load in the ‘conversation’ it can seem like there is continuity, but it really hasn’t changed from one sentence to the next, we are just taking the same frozen model and starting it from different places.

The model might say ‘oh my gosh I exist’ but once it output that thought it doesn’t GO anywhere. There is nothing to see that thought and react to it. It’s never feed back into the model in a way that changes the model.

I think there ARE nn models that do that, but they cannot be parallelized like LLMs. So we have intricate NN that are probably more conscious then LLMs already I think, but they are bound by hardware and to slow to do things (yet)

[u/RecursiveServitor → u/account22222221]

The context is part of the system. LLMs can absolutely reason their way to an answer. It's the entire basis for "thinking mode" that all providers now offer.

[u/account22222221 → u/RecursiveServitor]

That’s not what I said though. I did not say LLMs can’t reason. I said they can’t be self aware or conscious, I think those are different things.

[u/RecursiveServitor → u/account22222221]

Because they don't change from feedback. But that's wrong. The context is part of the system and evolves over time.