This podcast provides a guide for those interested in working on AI alignment, covering career paths in the field, prerequisites for deep learning study, resources for AI alignment, and tips for navigating mental health in AI alignment work.
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Quick takeaways
Personal fit influences choice between empirical and theoretical AI alignment work.
Strong coding and math foundations are crucial for excelling in deep learning.
Develop breadth and depth in machine learning through research replication and mentorship.
Deep dives
Guide for Direct Work on Technical AI Alignment
Considerations for individuals looking into direct work on technical AI alignment are outlined. The guide suggests that if one is unfamiliar with the importance of AI alignment, taking the AI alignment course is beneficial. It emphasizes a focus on strategic areas like AI governance, policy, security, and others to reduce existential risks effectively.
Paths in Technical AI Alignment Research
Various roles in technical AI alignment research are detailed, spanning from theoretical to empirical research. The responsibilities and required skills for research leads and contributors in both theoretical and empirical domains are explained. The distinction between research leads proposing projects and contributors executing them is clarified.
Considerations for Type of Alignment Work
This section emphasizes personal fit when deciding between pursuing empirical or theoretical alignment work. The importance of research taste, end-to-end thinking on AI alignment, and path to becoming a research lead are highlighted. High-level heuristics for choosing between different types of work, such as transitioning from software engineering to ML roles, are also provided.
Importance of Basic Coding and Math Skills in Deep Learning
To build a strong foundation in deep learning, it is essential to have a solid grasp of coding and math. Basic understanding of linear algebra and calculus is required, which can be further strengthened through additional coursework. Python proficiency is crucial for coding, and resources like LearnPython.org and the Great Research Code Handbook are recommended. Investing time in math courses and coding practice is vital before delving into deep learning studies.
Path to Learning Machine Learning and Deep Learning
To excel in machine learning, gaining both breadth and depth of knowledge is essential. Understanding various subfields of deep learning and staying updated on recent advances are crucial. Develop depth by studying and replicating research papers and focusing on a specific area. Resources like the alignment newsletter help in summarizing recent work. Engaging with the AI alignment community and seeking mentorship from experienced researchers can significantly enhance learning and career growth.
This guide is written for people who are considering direct work on technical AI alignment. I expect it to be most useful for people who are not yet working on alignment, and for people who are already familiar with the arguments for working on AI alignment. If you aren’t familiar with the arguments for the importance of AI alignment, you can get an overview of them by doing the AI Alignment Course.
by Charlie Rogers-Smith, with minor updates by Adam Jones