Early lessons about independent exploration
Someone recently asked for my advice on pursuing independent research and exploration. My answer ended up long and winding, so I thought it might be useful for others to read and contemplate. Though I'm mostly speaking from my experience in machine learning and human-computer interaction, I think my general takeaways apply to many fields.
Eventually, this will end up on my blog. In the meantime, here's a less polished thought dump.
The biggest lesson I’ve learned is that a research field is simply a community of people who share (1) a small set of problems they agree are important and interesting, and (2) a set of investigative methods to go after those problems and uncover new knowledge. This definition of a research field is broader (and, I’d argue, more accurate) than the version tied strictly to academia, at least if your main goal is to make a meaningful discovery or claim about the world that matters beyond your own curiosity.
Given that framing, one way to think about how to make use of an independent exploration period is to figure out what community you want to contribute knowledge to, learn where those people congregate, identify the problems they consider significant, and become familiar with how that community evaluates and integrates new ideas into their canon. You can then use that understanding to talk about problems of interest to you in a way that makes the community listen, and frame your solutions/ideas/discoveries in ways that have a high chance of nudging that community in a direction you believe is right.
For instance, my current work intersects two communities: the interpretability research community closer to ML academia, and the more commercially oriented “tools for thought” or “HCI for AI” community. When I talk to the former, I focus on how my work can help debug and improve model performance. When I address the latter, I try to get people excited about the idea of a totally new way to interact with information. Each community cares about different things, so I frame my work accordingly.
Finally, the way you share your ideas—through academic conferences, open-source releases, demos, or personal networking—will vary. In general, I’ve found it valuable to regularly talk about what I'm working on and always reiterate why I'm working on it, both in public and with trusted friends, because that helps others figure out whether they identify themselves to be in the same community, per the above definition, as you and your work.
Be lucid about what you want to understand or enable. Know your audience. Communicate clearly and regularly.