This is an odd list. While it includes various topics is includes things I would expect many to already be familiar with as well as educational/entertainment sources. While I love 3B1B, I don't understand how it belongs on a list like this. Similarly things like Lex's AI podcast.<p>The list also includes very beginner things. Maybe this could be ordered as in a way to progress through subjects or at least something better than alphabetical (and have a section for "entertainment" which would include things like 3B1B, Lex, Robert Miles, etc, which are useful but not hard literature).<p>Additionally: Title should be "DeepMind AI __Resource__ List," many items here are not ones in which you can read.
I'd echo one of their recommendations: Robert Mile's YouTube channel focussing on AI safety. He presents the material in a very interesting and accessible way. For example, this video on whether or not corporations can be considered a form of superintelligence: <a href="https://youtu.be/L5pUA3LsEaw" rel="nofollow">https://youtu.be/L5pUA3LsEaw</a>
This doesn’t make much sense. If you are not already trained in vector calculus, functional analysis and basic classifier and regression algorithms, then most of these reading list items are completely inapplicable (or even dangerous, like when someone reads some blog posts about slapping together neural nets in Keras and suddenly thinks they can build a model suitable for production).<p>On the other hand if you are trained in ML, most of these are not detailed or extended enough to give you anything useful. Doing “a hacker’s intro to X” over and over really, really doesn’t give you any skills. This is particularly true for deep domains like reinforcement learning, computer vision / image processing, and natural language processing.<p>Meanwhile, basic design of experiments for A/B testing, explanatory modeling and simple regressions is a fraught area. Not understanding extremely rigorous details about hypothesis testing, model checking, limitations of statistical significance, etc., can lead to wildly incorrect inferences from poor models that non-experts will completely fail to detect.<p>At best this list seems like “special topics to seem trendy in AI without getting deep / practical insight into any particular area.” There are one or two minor exceptions in the list.
Does anybody know where I might find a copy of these resources that are readable without zooming to 300%? It's like trying to read a pdf on a phone.