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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: dalle2 comments, published by nostalgebraist on April 26, 2022 on LessWrong. (i.) On April 6, OpenAI announced “DALLE-2” to great fanfare. There are two different ways that OpenAI talks to the public about its models: as research, and as marketable products. These two ways of speaking are utterly distinct, and seemingly immiscible. The OpenAI blog contains various posts written in each of the two voices, the “research” voice and the “product” voice, but each post is wholly one or the other. Here’s OpenAI introducing a model (the original DALLE) in the research voice: DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs. [.] Like GPT-3, DALL·E is a transformer language model. It receives both the text and the image as a single stream of data containing up to 1280 tokens, and is trained using maximum likelihood to generate all of the tokens, one after another. This training procedure allows DALL·E to not only generate an image from scratch, but also to regenerate any rectangular region of an existing image that extends to the bottom-right corner, in a way that is consistent with the text prompt. And here they are, introducing a model (Codex) in the product voice: OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories. [.] GPT-3’s main skill is generating natural language in response to a natural language prompt, meaning the only way it affects the world is through the mind of the reader. OpenAI Codex has much of the natural language understanding of GPT-3, but it produces working code—meaning you can issue commands in English to any piece of software with an API. OpenAI Codex empowers computers to better understand people’s intent, which can empower everyone to do more with computers. Interestingly, when OpenAI is planning to announce a model as a product, they tend not to publicize the research leading up to it. They don’t write research posts as they go along, and then write a product post once they’re done. They do publish the research, in PDFs on the Arxiv. The researchers who were directly involved might tweet a link to it. And of course these papers are immediately noticed by ML geeks with RSS feeds, and then by their entire social networks. It’s not like OpenAI is trying to hide these publications. It’s just not making a big deal out of them. Remember when the GPT-3 paper came out? It didn’t get a splashy announcement. It didn’t get noted in the blog at all. It was just dumped unceremoniously onto the Arxiv. And then, a few weeks later, they donned the product voice and announced the “OpenAI API,” GPT-3 as a service. Their post on the API was full of enthusiasm, but contained almost no technical details. It mentioned the term “GPT-3″ only in passing: Today the API runs models with weights from the GPT-3 family with many speed and throughput improvements. Machine learning is moving very fast, and we’re constantly upgrading our technology so that our users stay up to date. It didn’t even mention how many parameters the model had! (My tone might sound negative. Just to be clear, I’m not trying to criticize OpenAI for the above. I’m just pointing out recurring patterns in their PR.) (ii.) DALLE-2 was announced in the product voice. In fact, their blog post on it is the most over-the-top product-voice-y thing imaginable. It goes so far in the direction of “aiming for a non-technical audience” that it ends up seemingly addressed to small children. As in a picture book, it offers simple sentences in gigantic letters, one or two per page, each one nestled between huge comforting expanses of blank space. And sentences themselves are . well, st...