How to Build a Career in AI, Part 3 Choosing Projects

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A person holding a giant sheet with tips on how to find projects

Dear friends,

In the last two letters, I wrote about developing a career in AI and shared tips for gaining technical skills. This time, I’d like to discuss an important step in building a career: project work.

It goes without saying that we should only work on projects that are responsible and ethical, and that benefit people. But those limits leave a large variety to choose from. I wrote previously about how to identify and scope AI projects. This and next week’s letter have a different emphasis: picking and executing projects with an eye toward career development.

A fruitful career will include many projects, hopefully growing in scope, complexity, and impact over time. Thus, it is fine to start small. Use early projects to learn and gradually step up to bigger projects as your skills grow.

When you’re starting out, don’t expect others to hand great ideas or resources to you on a platter. Many people start by working on small projects in their spare time. With initial successes — even small ones — under your belt, your growing skills increase your ability to come up with better ideas, and it becomes easier to persuade others to help you step up to bigger projects.

What if you don’t have any project ideas? Here are a few ways to generate them:

  • Join existing projects. If you find someone else with an idea, ask to join their project.
  • Keep reading and talking to people. I come up with new ideas whenever I spend a lot of time reading, taking courses, or talking with domain experts. I’m confident that you will, too.
  • Focus on an application area. Many researchers are trying to advance basic AI technology — say, by inventing the next generation of transformers or further scaling up language models — so, while this is an exciting direction, it is hard. But the variety of applications to which machine learning has not yet been applied is vast! I’m fortunate to have been able to apply neural networks to everything from autonomous helicopter flight to online advertising, partly because I jumped in when relatively few people were working on those applications. If your company or school cares about a particular application, explore the possibilities for machine learning. That can give you a first look at a potentially creative application — one where you can do unique work — that no one else has done yet.
  • Develop a side hustle. Even if you have a full-time job, a fun project that may or may not develop into something bigger can stir the creative juices and strengthen bonds with collaborators. When I was a full-time professor, working on online education wasn’t part of my “job” (which was doing research and teaching classes). It was a fun hobby that I often worked on out of passion for education. My early experiences recording videos at home helped me later in working on online education in a more substantive way. Silicon Valley abounds with stories of startups that started as side projects. So long as it doesn’t create a conflict with your employer, these projects can be a stepping stone to something significant.

Given a few project ideas, which one should you jump into? Here’s a quick checklist of factors to consider:

  • Will the project help you grow technically? Ideally, it should be challenging enough to stretch your skills but not so hard that you have little chance of success. This will put you on a path toward mastering ever-greater technical complexity.
  • Do you have good teammates to work with? If not, are there people you can discuss things with? We learn a lot from the people around us, and good collaborators will have a huge impact on your growth.
  • Can it be a stepping stone? If the project is successful, will its technical complexity and/or business impact make it a meaningful stepping stone to larger projects? (If the project is bigger than those you’ve worked on before, there’s a good chance it could be such a stepping stone.)

Finally, avoid analysis paralysis. It doesn’t make sense to spend a month deciding whether to work on a project that would take a week to complete. You'll work on multiple projects over the course of your career, so you’ll have ample opportunity to refine your thinking on what’s worthwhile. Given the huge number of possible AI projects, rather than the conventional “ready, aim, fire” approach, you can accelerate your progress with “ready, fire, aim.”

Keep learning!



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