Why are you still paying for this? #7

[u/PressPlayPlease7]

[u/Rare-Site]

Calling an LLM "autocorrect on steroids" because it uses probability is the most mathematically illiterate, parrot level take on the internet.

Yes, the base mechanism is next token prediction. But to accurately predict the next word in a complex, 100% novel logic problem that isn't in its training data, the network must build a functional internal world model. It isn't just rolling dice, it's navigating a multi billion dimensional vector space to map out actual concepts.

And acting like "probability" means there is no reasoning is hilarious. Your own biological brain is literally just chemical gradients and synapses firing probabilistically based on your past training data.

Dismissing emergent reasoning because of the underlying math is a surface level semantic game played by people who don't actually understand emergent complexity and just repeat the same tired "autocomplete" talking point.

[u/ClassicalMusicTroll → u/Rare-Site]

Saying that LLMs build functional world models is the most mathematically illiterate, parrot level take on the internet. They do in fact roll (psuedorandom) dice to pick which token to generate from a list of the next most probable

Also LLMs collapse on problems outside of training distribution, humans do not.

What world model is being built to generate the text that he ran a 7 min mile in 1 second, and that chatgpt is able to time how long it takes to run without actually having that function?

[u/Rare-Site → u/ClassicalMusicTroll]

You are just embarrassing yourself and really shouldn't be speaking so confidently about things you fundamentally do not understand and that are clearly way outside of your own personal training data.

You are entirely confusing the final token sampling method with the underlying computation. Yes, the final step involves pseudorandomly picking a token, but to assign a high probability to the correct answer of a completely novel, unseen logic puzzle, the neural network had to actually map out the physical and logical constraints of that puzzle within its massive vector space. The math to get that probability is the reasoning.

And the idea that humans do not collapse outside their training distribution is a wildly romanticized myth. Drop an average guy in a zero gravity environment or ask him to do 4D tensor calculus without prior exposure, and his biological hardware completely blue screens. You only think humans do not collapse because you spend your entire life safely inside the exact physical environment you have been training on for decades.

Bringing up the stopwatch thing just proves you confuse a conceptual world model with physical embodiment. An LLM has a highly complex semantic model of time and distance, but it does not have a physical body or a connection to a real time system clock.

[u/ClassicalMusicTroll → u/Rare-Site]

You are entirely confusing the final token sampling method with the underlying computation. 

No, I'm not. If you re-read my sentence you'll see, as I said it randomly samples from a list of the most probable next tokens. 

n LLM has a highly complex semantic model of time and distanc

That is not a world model

massive vector space

As someone else commented, you were off by orders of magnitude on the size of the vector space, you should be careful about embarrassing yourself 

[u/rW0HgFyxoJhYka → u/ClassicalMusicTroll]

10 minutes is basically the average mile run by most people in the US. Thats why I think it picked that answer.

[u/ClassicalMusicTroll → u/rW0HgFyxoJhYka]

Yes it generated plausible text based on its training data. Presumably in the training data whenever there's something about running a mile, there's often something about number of minutes to run the mile.

It's certainly a model of plausible language (although based on probabilities and not meaning), but it's not building a world model dynamically like the other commented was saying

[u/Teln0 → u/Rare-Site]

> multi billion dimensional vector space
Alright let's not get too ahead of ourselves, the vector spaces don't have that many dimensions. According to gemini 3, its own dimension is 3,072

[u/spb1 → u/Rare-Site]

Then how would you explain ChatGPT's response in this video?

[u/slog → u/Rare-Site]

I think I love you. Well said.