this post was submitted on 31 Aug 2023
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Ok, so here's the misunderstanding - neural networks absolutely, 100% store information. You can download alpaca right now, and ask it about Paris, or llamas, or who invented the concept of the neural network. It will give you factual information embedded in the weights, there's nowhere else the information could be.
People probably think you don't understand databases because this seems self apparent that neural networks contain information - if they didn't, where does the information come from?
There's no magic involved, you can prove this mathematically. We know how it works and we can visualize the information - we can point to "this number right here is how the model stores the information of where the Eiffel tower is". It's too complex for us to work with right now, but we understand what's going on
Brains store information the same way, except they're much more complex. Ultimately, the connections between neurons are where the data is stored - there's more layers to it, but it's the same idea
And emergent properties absolutely are a thing in math. No sentience or understanding required, nothing necessarily to do with life or physics at all - complexity is where emergent properties emerge from
You are correct this is a misunderstanding here. But it is of your misunderstanding of neural networks, not mine of memory.
LLMs are mathematical models. It does not know any information about Paris, not in the same way humans do or even the Wikipedia does. It knows what words appear in response to questions about Paris. That is not the same thing as knowing anything about Paris. It does not know what Paris is.
I agree with you the word “Paris” exists in it. But I disagree that information is relevant in any human sense.
You have apparently been misled into believing a word generation tool contains any information at all other than word weights. Every word it contains is as exactly meaningless to it as every other word.
Brains do not store data in this way. Firstly, neural networks are mathematical approximations of neurons. But they are not neurons and do not have the same properties of neurons, even in aggregate. Secondly, brains contain thoughts, memories, and consciousness. Even if that is representable in a similar vector space as LLM neural networks (a debatable conjecture), the contents of that vector space are as different as newts are from the color purple.
I encourage you to do some more research on this before continuing to discuss it. Ask ChatGPT itself if its neural networks are like human brains; it will tell you categorically no. Just remember it also doesn’t know what it’s talking about. It is reporting word weights from its corpus and is no substitute for actual thought and research.