AI safety syllabus

This page was written by Jan Leike, with contributions and comments by David Krueger, Jelena Luketina, Victoria Krakovna, Daniel Dewey, Laurent Orseau, and others. It is intended as a guide to working on technical aspects of AI safety. See our guide to working in AI policy and strategy for another approach.
This is a syllabus of relevant background reading material and courses related to AI safety. It is intended as a guide for undergraduates in mathematics and computer science planning their degree, as well as people from other disciplines who are thinking about moving into AI safety. It includes tips how to design your degree, how to transition into research, and the relevant conferences. This is not intended as a general guide of how to become a researcher.
Reading List
We now recommend using the bibliography from the Center for Human-Compatible AI at UC Berkeley. Their list is more comprehensive and up-to-date than the one below.
This is a list of the most relevant reading topics and the appropriate material. The chapter recommendations are indicative of what you should know. If you find the topic interesting, read more! As an undergraduate student, you can plan these courses into your degree. As a graduate student, you can use the provided material to extend your knowledge into areas that you do not have much background in. Focus on the textbooks and lecture notes and use the video lectures as supplementary materials.

In the land of the blind, the one-eyed man is king – or so the saying goes. In his new book, Deep Work, Cal Newport argues that when it comes to deep concentration, we have become the land of the blind.
Source: Google Ngram












