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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: My Thoughts on the ML Safety Course, published by zeshen on September 27, 2022 on The AI Alignment Forum. This summary was written as part of Refine. The ML Safety Course is created by Dan Hendrycks at the Center for AI Safety. Thanks to Adam Shimi and Thomas Woodside for helpful feedback. Overview Background I recently completed the ML Safety Course by watching the videos and browsing through the review questions, and subsequently writing a short summary. As an engineer in the upstream oil and gas industry with some experience in dealing with engineering safety, I find the approach of the course of thinking in this framework especially valuable. This post is meant to be a (perhaps brutally) honest review of the course despite me having no prior working experience in ML. It may end up reflecting my ignorance of the field more than anything else, but I would still consider it as a productive mistake. In many cases, if my review seems to be along the lines of ‘this doesn’t seem to be right’, it should be read as ‘this is how a course participant may misinterpret the course contents’. I am also well aware that it is much easier to criticize something useful than actually doing something useful. For each section of the course, I will give a short summary, describe what I liked, and what I didn’t like. I may be especially brief with the parts about what I liked, and the brevity is no way a reflection about how much I liked it. Thomas Woodside, who helped with creating parts of the course, has kindly provided feedback to this post. His comments are formatted in italics. My Initial Expectations of the Course Having engaged with AI Safety as an outsider, my general impression of the field were: Predominantly based with AI FOOM scenarios and primarily concerned with abstract concepts like agency. Even among prosaic AI alignment, it appears that the general research modus operandi is that people would (somewhat randomly) generate ideas that could be applicable to certain classes of AI safety problems. Although the ideas may be interesting and valuable, they tend to be rather narrow and specific problems and may not be scalable. One of the more common arguments for advocating AI alignment is that failure to align AI systems lead to existential scenarios. From this perspective, a pragmatic approach towards AI safety with the aim of minimizing risks by reducing AI misalignments may not be very useful, since a superintelligent AI will exploit any every slight misalignment and immediately cause human extinction. Hence, I was pleasantly surprised when I came across the ML Safety Course, which I thought would be a good attempt at tackling the problem of prosaic AI alignment in a holistic, systematic, and practical manner. Although this approach may not directly solve the ‘hard problem’ completely, it would still help by minimizing existential risks and buy us more time to address the ‘hard problem’. (Feedback from Thomas: the creators of the course disagree with the framing of “solving the hard problem” as there are many hard problems that need to be iteratively worked on) Summary My overall impression of the course is: It is grounded on real-world safety principles that uses a systematic framework to reduce ML safety risks. It (rightly) does not seem to directly tackle the ‘hard problem’, but in my opinion there is nevertheless a lot of value in buying us more time while solving the ‘hard problem’ (Feedback from Thomas: the creators of the course disagree with the framing of “solving the hard problem” as there are many hard problems that need to be iteratively worked on) It details many approaches that are useful in some specific settings, but it is unclear how it scales towards more powerful AI systems. It covers several approaches that don’t seem to be very related to the ‘core’ r...