So go ahead and define it, in concrete terms, external to humans. It can't be equivalent unless there is a definite basis for equivalence. Cat videos don't cut it.
My point is that understanding, as we know it, only exists in the human mind.
If you want to define something that is functionally equivalent but implemented in a machine, that is absolutely fine, but don't point to something a machine does and say "look it's understanding!" without having a concrete model of what understanding is, and how that machine is, in concrete terms, achieving it.
Fine. Then I posit the existence of understanding-2, which is exactly identical to understanding, whatever it is, except for the fact that it can only be done by machines. And now I ask you to prove to me that AI doesn't have understanding-2.
This is just to show you the absurdity of trying to claim that AI doesn't have understanding because by definition only humans have it.
He said understanding is what humans do, not that only humans can do it. Stop arguing against a strawman.
Nobody would define understanding as something only humans can do. But it makes sense to define understanding based on what humans do, since that is our main example of an intelligence. If you want to make another definition of understanding then you need to prove that such a definition doesn't include a lot of behaviors that fails to solve problems human understanding can solve, because then it isn't really the same level of as human understanding.
Ok, so his argument is:
> Humans understand, models are deterministic functions
> Until you have a concrete definition of what understanding is you can't apply it to anything else
> Informal definitions of understanding by those who experience it aren't very useful at all.
Basically he says: "I don't accept you using the term 'understanding' until you provide a formal definition of it, which none of us has. I don't need such definition when I talk about people, because... I assume that they understand".
Which means: given two agents, I decide that I can apply the word "understanding" only to the human one, for no other reason that it is human, and simply refuse to apply it to non-humans, just because.
Clearly there is absolutely nothing that can convince this person that an AI understands- precisely because it's a machine. Put in front of a computer terminal with a real person on the other side- but being told it's a machine- he would refuse to call "understanding" whatever the human on the other side does. Which makes the entire discussion rather pointless, don't you think?
But they're very limited, and if you prompt with a relationship that isn't defined you get best-guess, which will either be quite distant or contaminated with other values.
If you ask Dall-E for "a woman made of birds" you get a composite that also includes trees and/or leaves. Dall-E has values for "made of" and "birds" but its value representation for "birds" is contaminated with contextual trees and branches.
Leonardo doesn't have a value for "made of", so you get a woman surrounded by bird-like blobs.
To understand a cat in a human sense the store would have to include the shape, the movement dynamics in all possible situations, the textures, and a set of defining behaviours that is as complete as possible. It would also have to be able to provide and remember an object-constant instantiation of a specific cat that is clean of contamination.
SORA is maybe 10% of the way there. One of the examples doing the rounds shows some puppies playing in snow. It looks impressive until you realise the puppies are zombies. They have none of the expressions or emotions of a real puppy.
None of this is impossible, but training time, storage, and power consumption all explode the more information you try to include.
Also the complain about 'made of' not being in the training data. Humans who never saw a bird can not draw a bird. Why is that saying something about the model?
I'm not saying that diffusion models act like humans. And I was talking specifically about image generation. My usage of the word understanding is in the task of image generation. I'm not even talking about 'made of', or 'birds'. Just 'cats' and 'hats'. If it can understand 1 thing, it can understand others, but they are not always in the training data.
This is all a non-problem. It kinda remind me of the discussion of what constitutes a 'male', or 'female'. All i want is to refer this one property that i observe in diffusion models. Which is what language is, reference. If you are so covetous of the word 'understand', then provide an alternative to refer to this property and i will gladly use it.
And how do you know that this is not what "understanding" is? To me, understanding the concept of a cat is exactly to immediately recall (or have ready) all the associations, the possibilities, the consequences of the "cat" concept. If you can make up correct sentences about cats and conduct a reasonable conversation about cats, it means that you understand cats.
No, plenty of humans can have reasonable conversations about things with zero understanding about them. We know they don't understand because when put in a situation in practice they fail to use the things they talked about. Understanding means you can apply what you know, not only talk about it.
How can Sora predict where the waves and ripples should go? Is it just "correlations not causal", whatever that means.
I'm no math wiz and my training in statistics is severely lacking but it feels like people need to review what they think it's possible with Generative AI because we are so far from understanding and AGI that my head hurts every time these words show up in a discussion.