The Nonlinear Library AF - More people getting into AI safety should do a PhD by AdamGleave
Mar 14, 2024
18:22
Welcome 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: More people getting into AI safety should do a PhD, published by AdamGleave on March 14, 2024 on The AI Alignment Forum.
Doing a PhD is a strong option to get great at developing and evaluating research ideas. These skills are necessary to become an AI safety research lead, one of the key talent bottlenecks in AI safety, and are helpful in a variety of other roles. By contrast, my impression is that currently many individuals with the goal of being a research lead pursue options like independent research or engineering-focused positions instead of doing a PhD.
This post details the reasons I believe these alternatives are usually much worse at training people to be research leads.
I think many early-career researchers in AI safety are undervaluing PhDs. Anecdotally, I think it's noteworthy that people in the AI safety community were often surprised to find out I was doing a PhD, and positively shocked when I told them I was having a great experience. In addition, I expect many of the negatives attributed to PhDs are really negatives on any pathway involving open-ended, exploratory research that is key to growing to become a research lead.
I am not arguing that most people contributing to AI safety should do PhDs. In fact, a PhD is not the best preparation for the majority of roles. If you want to become a really strong empirical research contributor, then start working as a research engineer on a great team: you will learn how to execute and implement faster than in a PhD. There are also a variety of key roles in communications, project management, field building and operations where a PhD is of limited use.
But we believe a PhD is excellent preparation for becoming a research lead with your own distinctive research direction that you can clearly communicate and ultimately supervise junior researchers to work on.
However, career paths are highly individual and involve myriad trade-offs. Doing a PhD may or may not be the right path for any individual person: I simply think it has a better track record than most alternatives, and so should be the default for most people. In the post I'll also consider counter-arguments to a PhD, as well as reasons why particular people might be better fits for alternative options. I also discuss how to make the most of a PhD if you do decide to pursue this route.
Author Contributions: This post primarily reflects the opinion of Adam Gleave so is written using an "I" personal pronoun. Alejandro Ortega and Sean McGowan made substantial contributions writing the initial draft of the post based on informal conversations with Adam. This resulting draft was then lightly edited by Adam, including feedback & suggestions from Euan McLean and Siao Si Looi.
Why be a research lead?
AI safety progress can be substantially accelerated by people who can develop and evaluate new ideas, and mentor new people to develop this skill. Other skills are also in high demand, such as entrepreneurial ability, people management and ML engineering. But being one of the few researchers who can develop a compelling new agenda is one of the best roles to fill.
This ability also pairs well with other skills: for example, someone with a distinct agenda who is also entrepreneurial would be well placed to start a new organisation.
Inspired by Rohin Shah's terminology, I will call this kind of person a research lead: someone who generates (and filters) research ideas and determines how to respond to results.
Research leads are expected to propose and lead research projects. They need strong knowledge of AI alignment and ML. They also need to be at least competent at executing on research projects: for empirically focused projects, this means adequate programming and ML engineering ability, whereas a theory lead would need stronger mathematical ability.
However, what real...
Doing a PhD is a strong option to get great at developing and evaluating research ideas. These skills are necessary to become an AI safety research lead, one of the key talent bottlenecks in AI safety, and are helpful in a variety of other roles. By contrast, my impression is that currently many individuals with the goal of being a research lead pursue options like independent research or engineering-focused positions instead of doing a PhD.
This post details the reasons I believe these alternatives are usually much worse at training people to be research leads.
I think many early-career researchers in AI safety are undervaluing PhDs. Anecdotally, I think it's noteworthy that people in the AI safety community were often surprised to find out I was doing a PhD, and positively shocked when I told them I was having a great experience. In addition, I expect many of the negatives attributed to PhDs are really negatives on any pathway involving open-ended, exploratory research that is key to growing to become a research lead.
I am not arguing that most people contributing to AI safety should do PhDs. In fact, a PhD is not the best preparation for the majority of roles. If you want to become a really strong empirical research contributor, then start working as a research engineer on a great team: you will learn how to execute and implement faster than in a PhD. There are also a variety of key roles in communications, project management, field building and operations where a PhD is of limited use.
But we believe a PhD is excellent preparation for becoming a research lead with your own distinctive research direction that you can clearly communicate and ultimately supervise junior researchers to work on.
However, career paths are highly individual and involve myriad trade-offs. Doing a PhD may or may not be the right path for any individual person: I simply think it has a better track record than most alternatives, and so should be the default for most people. In the post I'll also consider counter-arguments to a PhD, as well as reasons why particular people might be better fits for alternative options. I also discuss how to make the most of a PhD if you do decide to pursue this route.
Author Contributions: This post primarily reflects the opinion of Adam Gleave so is written using an "I" personal pronoun. Alejandro Ortega and Sean McGowan made substantial contributions writing the initial draft of the post based on informal conversations with Adam. This resulting draft was then lightly edited by Adam, including feedback & suggestions from Euan McLean and Siao Si Looi.
Why be a research lead?
AI safety progress can be substantially accelerated by people who can develop and evaluate new ideas, and mentor new people to develop this skill. Other skills are also in high demand, such as entrepreneurial ability, people management and ML engineering. But being one of the few researchers who can develop a compelling new agenda is one of the best roles to fill.
This ability also pairs well with other skills: for example, someone with a distinct agenda who is also entrepreneurial would be well placed to start a new organisation.
Inspired by Rohin Shah's terminology, I will call this kind of person a research lead: someone who generates (and filters) research ideas and determines how to respond to results.
Research leads are expected to propose and lead research projects. They need strong knowledge of AI alignment and ML. They also need to be at least competent at executing on research projects: for empirically focused projects, this means adequate programming and ML engineering ability, whereas a theory lead would need stronger mathematical ability.
However, what real...