This work was performed as a contractor for SERI MATS, but the views expressed are my own and do not necessarily reflect the views of the organization.
I recently conducted interviews with 7 current/former SERI MATS mentors. One of my goals was to understand the qualities that MATS mentors believe are most valuable for junior alignment researchers. I asked questions like:
- Who were your most promising scholars? What made them stand out? What impressed you about them?
- What are some important qualities or skills that you see missing from most MATS scholars?
- What qualities were your scholars most missing? What are some things that you wish they had, or that would’ve made them more impactful?
Qualities that MATS mentors value
- Endurance, happiness, & perseverance: Mentors noted that many scholars get discouraged if they’re not able to quickly come up with a promising research direction quickly, or if they explore 1-2 directions that don’t end up being promising. Mentors commented that their most promising scholars were ones who stay energetic/curious/relentless even when they don’t have a clear direction yet.
- Hustle + resourcefulness: What do you do when you get stuck? Mentors said that many scholars don’t know what to do when they’re stuck, but their promising mentees were able to be resourceful. They would read related things, email people for help, find a relevant Discord server, browse Twitter, and contact other MATS scholars + AIS researchers for help.
- Ability to ask for help + social agency: Many scholars waste a lot of time trying to figure things out on their own. Mentors noted that their most promising scholars were very agentic; they often found other scholars in the program who could help them or other Berkeley researchers who could help them. This also saved mentors time.
- Ability to get to know other scholars + engage in peer mentorship: According to mentors, many scholars rarely interacted with others in the stream/program. Some of the best scholars were able to form productive/mutualistic relationships with other scholars.
- Strong & concrete models of AI safety: Mentors noted that strong models are important but also hard to acquire. Some mentors emphasized that you often don’t get them until you have talked with people who have good models and you’ve spent a lot of time trying to solve problems. Others emphasized that you often don’t get them until you’ve spent a lot of time thinking about the problem for yourself.
- According to one mentor, the best way to get them is just to work closely with a mentor who has these models. No good substitute for just talking to mentors.
- Additionally, mentors noted that reading is undervalued. People have written up how they think about things. One mentor said they have read “everything on Paul’s blog, which was super valuable.”
- ML and LLM expertise: Some mentors valued ML skills, lots of experience playing around with language models, and strong intuitions around prompt engineering. (Unsurprisingly, this was especially true for mentors whose research interests focused on large language models).
- Research communication skills: Being better at efficiently/compactly getting across what they did and what their main problems/bottlenecks were. Some mentors noted that they felt like their (limited) time in meetings with scholars could have been used more effectively if scholars were better at knowing how to communicate ideas succinctly, prioritize the most important points, and generally get better at “leading/steering” meetings.
A few observations
- I was surprised at how often mentors brought up points relating to social skills, mental health, and motivation. I used to be a PhD student in clinical psychology, so I was wondering if I was somehow “fishing” for these kinds of answers, but even when I asked very open-ended questions, these were often in the top 3 things that mentors listed.
- It seems plausible that general training in things like “what to do when you’re stuck on a problem”, “how to use your network to effectively find solutions”, “when & how to ask for help”, “how to stay motivated even when you’re lost”, “how to lead meetings with your research mentors”, and “how to generally take care of your mental health” could be useful.
- When I converse with junior folks about what qualities they’re missing, they often focus on things like “not being smart enough” or “not being a genius” or “not having a PhD.” It’s interesting to notice differences between what junior folks think they’re missing & what mentors think they’re missing.
- I think many of these are highly malleable and all of these are at least somewhat malleable. I hope that readers come away with “ah yes, here are some specific skills I can work on developing” as opposed to “oh I don’t naturally have X, therefore I can never be a good researcher.” (Also, many great researchers have deficits in at least 1-2 of these areas).
Note: These interviews focused on mentors’ experiences during the MATS Summer and Autumn 2022 Cohorts. The current Winter 2022-23 Cohort added some related features, including the scholar support team, the Alignment 201 curriculum, technical writing and research strategy workshops, a Community Manager, regular networking events, and a team of alumni from past cohorts to support current scholars. Feel free to use the MATS contact form if you have further questions about the program.
IMO interesting to note is that it is my impression that these are among the main things you learn in an AI PhD, or at least things you would encounter and have to deal with (The transferable skills, which are in addition to the technical skills).
I would bet this is a big part of "being a good researcher".
Writing this as someone who isn't doing an AI PhD.