Alright. Suppose "meaning" (or "understanding") is something which exists in human head.
It might seem as a complete black box, but we can get some information about it by observing human interactions.
E.g. suppose Алиса does not know English, but she has a bunch of cards with instructions "Turn left", "Bring me an apple", etc. If she shows these cards to Bob and Bob wants to help her, Bob can carry out instructions in a card. If they play this game, the meaning which card induces in Bob's head will be understood by Алиса, thus she will be able to map these cards to meaning in her head.
So there's a way to map meaning which is mediated by language.
Now from math perspective, if we are able to estimate semantic similarity between utterances we might be able to embed them into a latent "semantic" space.
If you accept that the process of LLM training captures some aspects of meaning of the language, you can also see how it leads to some degree of self-awareness. If you believe that meaning cannot be modeled with math then there's no way anyone can convince you.
How does math encode meaning if there is no Alice and Bob? You should quickly realize the absurdity of your argument once you take people out of the equation.
Not sure what you mean... A NN training process can extract semantics from observations. That semantics can be then subsequently applied e.g. to robots. So it doesn't depend on humans beyond production of observations.
The function/mathematics in an NN (neural network) is meaningless unless there is an outside observer to attribute meaning to it. There is no such thing as a meaningful mathematical expression without a conscious observer to give it meaning. Fundamentally there is no objective difference between one instance of a NN with one parameter, f(θ), evaluated on some input, f(θ)(x), and another instance of the same network with a small perturbation of the parameter, f(θ+ε), evaluated on the same input, f(θ+ε)(x), unless a conscious observer perceives the output and attributes meaning to the differences because the arithmetic operations performed by the network are the same in both networks in terms of their objective complexity and energy utilization.
How does the universe encode meaning if there is no Alice and Bob?
One common answer is: it doesn't.
And yet, here we are, creating meaning for ourselves despite being a state of the quantum wave functions for the relevant fermion and boson fields, evolving over time according to a mathematical equation.
(Philosophical question: if the time-evolution of the wave functions couldn't be described by some mathematical equation, what would that imply?)
The universe does not have a finite symbolic description. Whatever meaning you attribute to the symbols has no objective reality beyond how people interpret those symbols. Same is true for the arithmetic performed by neural networks to flash lights on the screen which people interpret as meaningful messages.
> The universe does not have a finite symbolic description
Why do you believe that? Have you mixed up the universe with Gödel's incompleteness theorems?
Your past light cone is finite in current standard models of cosmology, and according to the best available models of quantum mechanics a finite light cone has a finite representation — in a quantised sense, even, with a maximum number of bits, not just a finite number of real-valued dimensions.
Even if the universe outside your past light cone is infinite, that's unobservable.
> Same is true for the arithmetic performed by neural networks to flash lights on the screen which people interpret as meaningful messages.
This statement is fully compatible with the proposition that an artificial neural network itself is capable of attributing meaning in the same way as a biological neural network.
It does not say anything, one way or the other, about what is needed to make a difference between what can and cannot have (or give) meaning.
It might seem as a complete black box, but we can get some information about it by observing human interactions.
E.g. suppose Алиса does not know English, but she has a bunch of cards with instructions "Turn left", "Bring me an apple", etc. If she shows these cards to Bob and Bob wants to help her, Bob can carry out instructions in a card. If they play this game, the meaning which card induces in Bob's head will be understood by Алиса, thus she will be able to map these cards to meaning in her head.
So there's a way to map meaning which is mediated by language.
Now from math perspective, if we are able to estimate semantic similarity between utterances we might be able to embed them into a latent "semantic" space.
If you accept that the process of LLM training captures some aspects of meaning of the language, you can also see how it leads to some degree of self-awareness. If you believe that meaning cannot be modeled with math then there's no way anyone can convince you.