I’m rather curious to see how the EU’s privacy laws are going to handle this.
(Original article is from Fortune, but Yahoo Finance doesn’t have a paywall)
I’m rather curious to see how the EU’s privacy laws are going to handle this.
(Original article is from Fortune, but Yahoo Finance doesn’t have a paywall)
No, the way humans know things and LLMs know things is entirely different.
The flaw in your understanding is believing that LLMs have internal representations of memes and cats and cars. They do not. They have no memories or internal facts… whereas I think most people agree that humans can actually know things and have internal memories and truths.
It is fundamentally different from asking you to forget that cats exist. You are incapable of altering your memories because that is how brains work. LLMs are incapable of removing information because the information is used to build the model with which they choose their words, which is then undifferentiatable when it’s inside the model.
An LLM has no understanding of anything you ask it and is simply a mathematical model of word weights. Unless you truly believe humans have no internal reality and no memories and simply say things based on what is the most likely response, you also believe humans and LLM knowledge is entirely different to each other.
No, I disagree. Human knowledge is semantic in nature. “A cat walks across a room” is very close, in semantic space, to “The dog walked through the bedroom” even though they’re not sharing any individual words in common. Cat maps to dog, across maps to through, bedroom maps to room, and walks maps to walked. We can draw a semantic network showing how “volcano” maps onto “migraine” using a semantic network derived from human subject survey results.
LLMs absolutely have a model of “cats.” “Cat” is a region in an N dimensional semantic vector space that can be measured against every other concept for proximity, which is a metric space measure of relatedness. This idea has been leveraged since the days of latent semantic analysis and all of the work that went into that research.
For context, I’m thinking in terms of cognitive linguistics as described by researchers like Fauconnier and Lakoff who explore how conceptual bundling and metaphor define and constrain human thought. Those concepts imply that a realization can be made in a metric space such that the distance between ideas is related to how different those ideas are, which can in turn be inferred by contextual usage observed over many occurrences. 
The biggest difference between a large model (as primitive as they are, but we’re talking about model-building as a concept here) and human modeling is that human knowledge is embodied. At the end of the day we exist in a physical, social, and informational universe that a model trained on the artifacts can only reproduce as a secondary phenomenon.
But that’s world apart from saying that the cross-linking and mutual dependencies in a metric concept-space is not remotely analogous between humans and large models.
It’s not a world apart; it is the difference itself. And no, they are not remotely analogous.
When we talk about a “cat,” we talk about something we know and experience; something we have a mental model for. And when we speak of cats, we synthesize our actual lived memories and experiences into responses.
When an LLM talks about a “cat,” it does not have a referent. There is no internal model of a cat to it. Cat is simply a word with weights relative to other words. It does not think of a “cat” when it says “cat” because it does not know what a “cat” is and, indeed, cannot think at all. Think of it as a very complicated pachinko machine, as another comment pointed out. The ball you drop is the question and it hits a bunch of pegs on the way down that are words. There is no thought or concept behind the words; it is simply chance that creates the output.
Unless you truly believe humans are dead machines on the inside and that our responses to prompts are based merely on the likelihood of words being connected, then you also believe that humans and LLMs are completely different on a very fundamental level.
Could you outline what you think a human cognitive model of “cat” looks like without referring to anything non-cat?
Yes; it is a cat. I can think of what that is. Can an LLM?
Describe it. Imagine I’ve never encountered a cat, because I’m from Mars.
You can’t! It’s like describing fire to someone that’s never experienced fire.
This is the root of experience and memory and why humans are different from LLMs. Which, again, can never understand or experience a cat or fire. But the difference is more fundamental than that. To an LLM, there is no difference between fire and cat. They are simply words with frequencies attached that lead to other words. Their difference is the positions they occupy in a mathematical model where sometimes it will output one instead of the other, nothing more.
Unless you’re arguing my inability to express a mental construct to you completely means I myself don’t experience it. Which I think you would agree is absurd?
I have absolutely no idea what your model is for how humans understand, relate, and communicate concepts.
How is that germane to this question? Do you agree humans can experience mental phenomena? Like, do you think I have any mental models at all?
If so, then that is the difference between me and an LLM.