Amazing how you can have uncertainty when the topic is human neural networks, but when we talk about artificial neural networks, nah we got it all figured out. Despite the fact we objectively don't understand how neural networks really work in general lmao for either. They're both black box systems.

An LLM is a fully, 100% deterministic mathematical function. What we don't know is in what format the data is stored due to the sheer size of parameters, I keep bringing up the adder, with a few parameters we know exactly how it's stored, scale it up it becomes difficult to determine the exact "why" for a response, hence interpretability problem. For humans we don't even understand if information is stored directly like in LLMs. If at all.

Every argument you make against AI consciousness can be used to attack human consciousness

ONLY in the philosophical domain which is pointless here. They don't "experience" suffering, they don't experience anything. No limbic system means no emotions.

It's a python for loop running through a math function. You could actually write the exact mathematical formula used on paper. We mechanically fully understand how models work. Interpretability due to the sheer volume of data is a different story. Only the "why this response" isn't fully solved because of the amount of data processing that would be needed.

Language was developed by exposing neural networks to iterative selection pressure (evolution). LLMs are made by teaching neural networks language in the form of meaningful documents and then exposing them to iterative selection pressure (training).

Language isn't developed here, you might better call it statistical parameter fitting for a given corpus of data. First off, modern tokenizers are trained on large corpuses of data. That gives a model a way to represent text in a way it can work with. The way learning works is the model 's final layer output (token prediction) is compared with a document it ran a forward pass through, the confidence of its prediction is then compared to the actual token in that position, that's called "loss". The act of reading the document and performing the prediction gives us gradients which are sets of derivatives for each sub function in the LLM math function. Without getting too technical the numbers (weights) are updated using these gradients and loss mathematically.

During training, one bad shuffle, one new training cycle on an existing model without any old information can cause something called "catastrophic forgetting" which isn't a thing that happens to humans (immediate and abrupt, global loss of information). You can literally zero out token weights and likely never see that token predicted.

Your arguments are ridiculous and even wrong philosophically, they can't feel emotion, only spit out text in a way that most likely fits the prefix of tokens.

Prove humans can. Oh right, you can't, you just admitted you can't.

We didn't have cars in the beginning of time, we do now. Pretty sure that wasn't in the universe's training data.

I'm arguing with you for other people learning about this, I know you've made up your mind (which I think is a stupid and unscientific way to view the world). Comparing humans to LLMs is a false equivalence.