Your curiosity is a bit of fresh air after months of seeing people arguing over pointless semantics. So I'm going to attempt to explain my mental model of how this works.

1- This is correct but not really useful view imo. Saying it can fit any arbitrary function doesn't really tell you whether it'll do it given finite resources. Part of your excitement comes from this i think, We've had this universal approximators far longer but we've never had an abstract concept approximated so well. The answer is the scale of the data. I'd like to pay extra attention to GPT's generic training now before moving on to multi modalities. There is this view that compression is intelligence(see hutter prize and kolmogorov complexity/compressor) and these models are really just good compressors. Given that model weights are fixed during the training and they are much smaller than the data we are trying to fit and the objective is to recover the original text(next token prediction), there is no way to achieve this task other than to compress this data really well. as it turned out, the more intelligent you are the more you are able to predict/compress, and if you are forced to compress something, you are essentially being forced to gain intelligence. It's like If you were to take an exam tomorrow on the subject you currently dont know anything about, 1- you could memorize potential answers 2- but if the test is few thousand questions long and there is no way to memorize them given the time/ability to exactly memorize the answers, your best bet is to actually learn the subject and hope to derive the answers during the test. This compression/intelligence duality is somewhat controversial especially among HN crowd who deny the generalization abilities of LLMs, but this is my current mental model and I haven't been able to falsify this view so far.

If you accept this view, the multi modality capability is just engineering. We don't know exactly about GPT4-V, but from the open source multi modal research we can infer the details. given an image and text pair of a dataset where the text explains what's going on in the image(e.g. an image of a cat and a long description of the image), we tokenize/embed the image like we do to text. This could be through Visual transformers(ViT) where the network just generates visual features for each patch of the image and put them in a long sequence. Now, if you give these embeddings to a pretrained LLM, and force it to predict the description of the image(text pair), there is no way to achieve this task other than to look at those image embeddings and gain general image understanding. After your network is capable of understanding the information in given image and express it in natural language, the rest is instruction tuning to use that undersanding. Generative image models like stable diffusion works similarly, only in that you have a contrastive model(CLIP) that you train by forcing it to produce the same embeddings of same concepts(e.g. embeddings of picture of a cat and embeddings text "picture of a cat" is forced to be close to each other during training.). Then you use this dual information to allow your generative part of the model to steer the direction of generation. What's surprising to me in all of this is, we've had these capabilities at this scale(lucky) and we can get more capabilities with just more compute. Like if the current gpt4 had a final loss of 1 on the scale of data it has now, it'll probably be much more capable if we can get the loss to 0.1 somehow. It's exciting!

This is my general understanding and I'd like to be corrected in any of these but hope you find this useful.

2) It seems to be that way. Probably possible even today.