[Article] “ML engineering for AI safety & robustness: a Google Brain engineer’s guide to entering the field” by Catherine Olsson, the 80,000 Hours team

Technical AI safety is a multifaceted area of research, with many sub-questions in areas such as reward learning, robustness, and interpretability. These will all need to be answered in order to make sure AI development will go well for humanity as systems become more and more powerful. Not all of these questions are best tackled with abstract mathematics research; some can be approached with concrete coding experiments and machine learning (ML) prototypes. As a result, some AI safety research teams are looking to hire a growing number of Software Engineers and ML Research Engineers. Narrated by AI. ---Outline:(01:52) What are the necessary qualifications for these positions?(03:22) How can I best learn Machine Learning engineering skills if I don’t yet have the necessary experience?(03:31) Initial investigation(03:55) ML basics(05:35) Learn ML implementation and debugging, and speak with the team you want to join(07:47) Case study: Daniel Ziegler's ML self-study experience(10:51) Now apply for jobs(13:30) Learn more --- First published: November 2nd, 2018 Source: https://80000hours.org/articles/ml-engineering-career-transition-guide --- Narrated by TYPE III AUDIO.