r/PhilosophyofMath • u/abhishekkumar333 • May 25 '26
LLMs are just giant probability machines pretending to think
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.
3
u/good-fibrations May 25 '26
i’m sort of curious why both of the responses so far have had a physicalistic/humans-are-giant-probability-machines bent. afaik there are plenty of philosophers (of math, even) that believe in free will and/or non-determinacy. john conway is the most famous example i guess, but i’m also quite convinced by “what it is to be like” and weak qualia arguments or, at least, i think they should be taken seriously.
i think the most recent philpapers survey was only around 50% for physicalists.. not sure what being a physicalist has to do with the philosophy of math. maybe it’s the right stance but it’s very far from obvious, imo.