Comment by soulofmischief

> since an LLM should be able to describer [sic] an image losslessly

Any lossless text description of a given image would just be a structured text output of the exact pixel values, or an encoding that losslessly exploits regularities. Anything less is categorically lossy. An ordinary semantic description of a reasonably complex image will generally be lossy. A short, lossless text description would need to exploit regularities in the image, such as a solid-color image, or a basic pattern or sequence which is easily reproducible, such as a precisely specified checkerboard image.

And being able to represent images losslessly in text simply establishes text’s capacity to encode information. It doesn't establish an LLM’s holistic understanding of images. There is heavy lifting in the phrase “images are a subset.” An image can be almost any conceivable perceptual arrangement of data. The bounds are not clear. Maybe what you meant to suggest was that images of our natural world contain exploitable regularities.

> Natural evolution has also had a much easier time coming up with vision models (everything with eyeballs) than complex language models (as far as we know only a smallish family of monkeys).

It isn't appropriate to explain complex biological systems in terms of today's technologies. This is something people have been doing for a long time. The mind is like a a book, a loom, a camera, an engine, a computer, an LLM/vision model. Etc. The cognitive abilities required for an organism to meaningfully interpret its vision can possibly largely be the same cognitive abilities underlying speech, and all that is missing for a brain to exploit this might be sufficiently advanced vocal cords or manual dexterity; nothing to do with the brain itself, but its auxiliary organs.