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Your first random prompt is far from random. It contains the fragments "sublim", "chr", "cross" and "corpt" in addition to the isolated "E", which all project t

Your first random prompt is far from random. It contains the fragments "sublim", "chr", "cross" and "corpt" in addition to the isolated "E", which all project the solution down towards Latin and Christianity. You can easily see that these language models are in some sense working on fragments as much as they are on the actual words isolated by spaces in your sentence. Just take a test sentence and enter as a prompt to get some images. Then take that same sentence, remove all spaces and add new spaces in random locations, making gibberish words. You will see that the results will retain quite a few elements from the original prompt, while other things (predominantly monosyllables) become lost. To me, I have not seen a single example that cannot just be explained by saying this is all just linear algebra, with a mind-bogglingly huge and nasty set of operators that has some randomness in it and that projects from the vector space of sentences written in ASCII letters onto a small subset of the vector space of 1024x1024x24bit images. If you then think about doing this just in the "stupid way", imagine you have an input vector that is 4096 bytes long (in some sense the character limit of DALL-E 2) and an output vector that is 3 million bytes long. A single dense matrix representing one such mapping has 6 billion parameters - but you want something very sparse here, since you know that the output is very sparse in the possible output vector space. So let's say you have a sparsity factor of somewhere around 10^5. Then with the 3.5 billion parameters of DALL-E 2, you can "afford" somewhere around 10^5 such matrices. Of course you can apply these matrices successively. Is it then so far fetched to believe that if you thought of those 10^5 matrices as a basis set for your transformation, with a separate ordering vector to say which matrices to apply in what order, and you then spent a huge amount of computing power running an optimizer to get a very good basis set and a very good dictionary of ordering vectors, based on a large corpus of images with caption, that you would not get something comparably impressive as DALL-E 2? When people are wowed that you can change the style of the image by saying "oil painting" or "impressionist", what more is that than one more of the basis set matrices being tacked on in the ordering vector?

IIAOPSW 4y ago
We don't disagree at all about what DallE does or what I would expect to see on certain outputs. What I'm saying is maybe that output is actually correct and we just don't know it yet. Maybe the brain really does encode some amount of information on just the sound fragments. Maybe vocabulary words aren't wholly arbitrary signifiers but instead come from some deeper system our brain uses to encode things. There is already a known human version of consistent output given nonsense words. Its called Bouba and Kiki. https://en.wikipedia.org/wiki/Bouba/kiki_effect https://en.wikipedia.org/wiki/Bouba/kiki_effect There's other evidence in support of this idea, but it would be a hassle for me to dig it up now. >Your first random prompt is far from random. It contains the fragments "sublim", "chr", "cross" and "corpt" in addition to the isolated "E", which all project the solution down towards Latin and Christianity. Oh right, I omitted a sentence. DallE gave me churches. DallE-mini gave me bicycle racing and beetles. Both of them behaved self-consistently, but were not consistent with each other. I did test changing some of the words that seemed like they might be having the steering effects like you pointed out. The new phrase was "E sublumary widge fraus chronly non estoi". DallE mini gave me mostly doctors/medical procedures with a few indiscernible scenes mixed in. Real DallE gave me scrabble, word tiles, pages from a book. Its like this. If you talk to a dog with the "who's a good boy" voice, the dog will understand "who's a good boy", even if you're actually saying "who's a little asshole". Likewise, a pronounceable string will generally feel like it belongs within a certain envelope of meanings, even if its not actually a phrase in any real spoken language. I showed some maps that DallE generated which happened to have some places labelled with its usual gibberish. People said the place names felt vaguely like they might be in Eastern Europe. Interpretation of nonsense words works both in the direction of human to DallE and DallE to human!

IIAOPSW 4y ago

We don't disagree at all about what DallE does or what I would expect to see on certain outputs. What I'm saying is maybe that output is actually correct and we just don't know it yet. Maybe the brain really does encode some amount of information on just the sound fragments. Maybe vocabulary words aren't wholly arbitrary signifiers but instead come from some deeper system our brain uses to encode things. There is already a known human version of consistent output given nonsense words. Its called Bouba and Kiki. https://en.wikipedia.org/wiki/Bouba/kiki_effect https://en.wikipedia.org/wiki/Bouba/kiki_effect There's other evidence in support of this idea, but it would be a hassle for me to dig it up now. >Your first random prompt is far from random. It contains the fragments "sublim", "chr", "cross" and "corpt" in addition to the isolated "E", which all project the solution down towards Latin and Christianity. Oh right, I omitted a sentence. DallE gave me churches. DallE-mini gave me bicycle racing and beetles. Both of them behaved self-consistently, but were not consistent with each other. I did test changing some of the words that seemed like they might be having the steering effects like you pointed out. The new phrase was "E sublumary widge fraus chronly non estoi". DallE mini gave me mostly doctors/medical procedures with a few indiscernible scenes mixed in. Real DallE gave me scrabble, word tiles, pages from a book. Its like this. If you talk to a dog with the "who's a good boy" voice, the dog will understand "who's a good boy", even if you're actually saying "who's a little asshole". Likewise, a pronounceable string will generally feel like it belongs within a certain envelope of meanings, even if its not actually a phrase in any real spoken language. I showed some maps that DallE generated which happened to have some places labelled with its usual gibberish. People said the place names felt vaguely like they might be in Eastern Europe. Interpretation of nonsense words works both in the direction of human to DallE and DallE to human!