EA - Announcing the Introduction to ML Safety Course by ThomasWoodside

Link to original articleWelcome 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: Announcing the Introduction to ML Safety Course, published by ThomasWoodside on August 6, 2022 on The Effective Altruism Forum. TLDR We're announcing a new course designed to introduce students with a background in machine learning to the most relevant concepts in empirical ML-based AI safety. The course is available publicly here. Background AI safety is a small but rapidly growing field, and both younger and more experienced researchers are interested in contributing. However, interest in the field is not enough: researchers are unlikely to make much progress until they understand existing work, which is very difficult if they are simply presented with a list of posts and papers to read. As such, there is a need for curated AI safety curricula that can get new potential researchers up to speed. Richard Ngo’s AGI Safety Fundamentals filled a huge hole in AI safety education, giving hundreds of people a better understanding of the landscape. In our view, it is the best resource for anyone looking for a conceptual overview of AI safety. However, until now there has been no course that aims to introduce students to empirical, machine learning-based AI safety research, which we believe is a crucial part of the field. There has also been no course that is designed as a university course usually is, complete with lectures, readings, and assignments; this makes it more likely that it could be taught at a university. Lastly, and perhaps most importantly, most existing resources assume that the reader has higher-than-average openness to AI x-risk. If we are to onboard more machine learning researchers, this should not to be taken for granted. In this post, we present a new, publicly-available course that Dan Hendrycks has been working on for the last eight months: Introduction to ML Safety. The course is a project of the Center for AI Safety. Introduction to ML Safety Philosophy The purpose of Introduction to ML Safety is to introduce people familiar with machine learning and deep learning to the latest directions in empirical ML safety research and explain existential risk considerations. Our hope is that the course can serve as the default for ML researchers interested in doing work relevant to AI safety, as well as undergraduates who are interested in beginning research in empirical ML safety. The course could also potentially be taught at universities by faculty interested in teaching it. The course contains research areas that many reasonable people concerned about AI x-risk think are valuable, though we exclude those that don’t (yet) have an empirical ML component, as they aren’t really in scope for the course. Most of the areas in the course are also covered in Open Problems in AI X-Risk. The course is still very much in beta, and we will make improvements over the coming year. Part of the improvements will be based on feedback from students in the ML Safety Scholars summer program. Content The course is divided into seven sections, covered below. Each section has lectures, readings, assignments, and (in progress) course notes. Below we present descriptions of each lecture, as well as a link to the YouTube video. The slides, assignments, and notes can be found on the course website. Background The background section introduces the course and also gives an overview of deep learning concepts that are relevant to AI safety work. It includes the following lectures: Introduction: This provides motivation for studying ML safety, with an overview of each of the areas below as well as potential existential hazards. Deep Learning Review: This lecture covers some important deep learning content that is good to review before moving on to the main lectures. This section includes a written assignment and a programming assignment designed to help students review deep learning conce...