ML engineering for AI safety & robustness: a Google Brain engineer's guide to entering the field - 80,000 Hours
Note that this guide was written in November 2018 to complement an in-depth conversation on the 80,000 Hours Podcast with Catherine Olsson and Daniel Ziegler on how to transition from computer science and software engineering in general into ML engineering, with a focus on alignment and safety. If you like this guide, we'd strongly encourage you to check out the podcast episode where we discuss some of the instructions here, and other relevant advice. 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.
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This is the new discipline, ML engineering. New technologies, new jobs, new opportunities. Very positive.
Let´s hope for the best and I really hope that the AI and all its automation would materialize...