I agree 100% that it is way too hard to climb the mountains of knowledge, that it could be much easier, and that there is little incentive and cultural support

I agree 100% that it is way too hard to climb the mountains of knowledge, that it could be much easier, and that there is little incentive and cultural support in academia to work on these topics. Time and time again I see examples of where outcomes are not limited by the existing knowledge, but by the accessibility of said knowledge. I would gladly devote most of my time to distilling knowledge if I saw a viable career path for it. Over the last years, I gave a lot of thought to the topic of conveying information. I see research as the process in which knowledge is created. This knowledge then has to be “encoded” in a format that allows for the transmission to other people. This encoding can be optimized in different ways. Research articles have the advantage that they are close to “lossless” in that they are supposed to contain all information necessary to build up that knowledge. This makes them well suited for archival, especially as they can be stored as a stack of paper. However, research articles are often not optimized for building up that knowledge in an efficient way. I believe that the “encoding” optimized for learning & understanding should be more like a progressive image codec, in that it provides a comprehensive view as soon as possible, filling in further details along the way. This also makes it possible to stop whenever you have reached the level of detail that is relevant to you. The challenge in creating these encodings is to extract the information that provides the most clear & useful picture as soon as possible. I like the word “to distill” for that process, as it is really about extracting the essence of a body of information. Doing this work for research articles is how I understand the goal of distill.pub, which seems extremely valuable to me. However, I think this is just the first step. What is the most useful distillation of all of deep neural networks? Of all of machine learning? Of all of computer science? As others mentioned, there are some forms of publications (review articles, textbooks) that do part of this distillation process, but they only cover part of the spectrum. Preciously few textbook contain a well thought-out summary of their contents and not just an introduction. In my experience, often the least amount of thought is given to the highest level of abstraction (e.g. what is the essence of mathematics?), even though they are the most fundamental ones. It would be great if there was more focus on extracting useful understanding from the ocean of knowledge we already have (useful both in the sense of being applicable on its own as well as being a solid foundation to build more knowledge on). It looks like distill.pub is a step in that direction, and I really hope it will bring more attention and recognition to this kind of work.