Why ChatGPT Can't Draw a Full Glass of Wine

I think you're looking at it too abstractly. An LLM isn't representing anything, it has a bag of numbers that some other algorithm produced for it. When you give it some numbers, it takes them and does matrix operations with them in order to randomly select a token from a softmax distribution, one at a time, until the EOS token is generated. If they don't have any training data that covers a particular concept, they can't map it onto a world model and make predictions about that concept based on an understanding of the world and how it works. [This video]( ▶ this video ) illustrates it pretty well. These things may or may not end up being fixed in the models, but that's only because they've been further trained with the specific examples. Brains have world models. Cats see a cup of water, and they know exactly what will happen when you tip it over (and you can bet they're gonna do it).

Draw a clock that shows [time other than 10:10] Draw a wine glass that's totally full to the brim etc. ▶ this video link

I think you're missing the context everyone else has - this video is where the "AI can't draw a full glass of wine" meme got traction ▶ this video The prompts (some generated by ChatGPT itself, since it's instructing DALL-E behind the scenes) include phrases like "full to the brim" and "almost spilling over" that are not up to interpretation at all.

Well, maybe. Maybe not. See "Why can't ChatGPT draw a full glass of wine?" ▶ this video Diffusion models do posses some capability to synthesize ideas, but that capability does not necessarily generalize to every possible use case. So it's impossible to say for certain that that is what is happening.

I completely agree. You can see this limitation if you try to create anything complex with the current state of the art. There is also a great video by Alex O'Connor on a simple prompt to generate a full glass of wine, which illustrates this really well - ▶ this video This wave of AI is amazing. It's a great tool for many applications, and it will even revolutionize many of them, but it is still far from AGI, a term that has been hijacked by the tech industry to hype up their products. In some ways AI has advanced a lot, but in other ways it has been set back by the hype and muddying of the terms.