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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: How to become an AI safety researcher, published by peterbarnett on April 12, 2022 on The Effective Altruism Forum. What skills do you need to work on AI safety? And what can we learn from the paths people have taken into the field? We were inspired by the 80,000 Hours podcast with Catherine Olsson and Daniel Ziegler, which had great personal stories and advice about getting into AI safety, so we wanted to do it for a larger sample size. To better understand the lives and careers of AI safety researchers, I talked to eleven AI safety researchers in a variety of organizations, roles, and subfields. If you’re interested in getting into AI safety research, we hope this helps you be better informed about what pursuing a career in the field might entail, including things like: How to develop research taste Which specific technical skills to build What non-technical skills you’ll need The first section is about the general patterns we noticed, and the second section describes each person’s individual path. Of note, the people we talked with are not a random sample of AI safety researchers, and it is also important to consider the effects of survivorship bias. However, we still think it's useful and informative to hear about how they got into the field and what skills they have found valuable. This post is part of a project I’ve been working on at Nonlinear. Paths into AI safety What degrees did people get? Perhaps unsurprisingly, the researchers we talked to universally studied at least one STEM field in college, most commonly computer science or mathematics. Most had done research as undergraduates, although this often wasn’t in AI safety specifically; people often said that getting early research experience was valuable. It is sometimes joked that the qualification needed for doing AI safety work is dropping out of a PhD program, which three people here have done (not that we would exactly recommend doing this!). Aside from those three, almost everyone else is doing or has completed a PhD. These PhD programs were often but not universally, in machine learning, or else they were in related fields like computer science or cognitive science. All of the researchers we talked with had at least familiarity with Effective Altruism and/or Rationality, with most people being actively involved in at least one of these communities. For influential reading, Superintelligence and writing by 80,000 Hours were each mentioned by three people as being particularly impactful on their decision to work on AI safety. It is worth noting that Superintelligence was one of the main books about risks from AI when the people we talked with were becoming interested, but may not be the best book to recommend to people now. More recent books would include Human Compatible by Stuart Russell, or The Alignment Problem by Brian Christian. Finally, many of the safety researchers participated in a program designed for early-career researchers, such as those run by MIRI, CHAI, and FHI. Skills The researchers interviewed described the utility of both technical skills (e.g. machine learning, linear algebra) and more general research skills (e.g. developing research taste, writing well). What technical skills should you learn? Technical AI safety research requires a strong understanding of the technical side of machine learning. By ‘technical’ here I basically mean skills related to programming and math. Indeed, a strong command of concepts in the field is important even for those engaged in less technical roles such as field building and strategy. These skills still seem important for understanding the field, especially if you’re talking to technical researchers. Depending on the area you work on, some specific areas will be more useful than others. If you want to do “hands-on” machine learning where you trai...