It goes without saying that we should only work on projects that are responsible, ethical, and beneficial to people. But those limits leave a large variety to choose from. In the previous chapter, I wrote about how to identify and scope AI projects. This chapter and the next have a slightly 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:
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Join existing projects. If you find someone else with an idea, ask to join their project.
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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.
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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 also very 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.
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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 in 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. As 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:
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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.
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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 hug impact on your growth.
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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 is 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, aim, fire.”
Ready, Fire, Aim
Working on projects requires making tough choices about what to build and how to go about it. Here are two distinct styles:
- Ready, Aim, Fire: Plan carefully and carry out careful validation. Commit and execute only when you have a high degree of confidence in a direction.
- Ready, Fire, Aim: Jump into development and start executing. This allows you to discover problems quickly and pivot along the way if necessary.
Say you’ve built a customer-service chatbot for retailers, and you think it could help restaurants, too. Should you take time to study the restaurant market before starting development, moving slowly but cutting the risk of wasting time and resources? Or jump in right away, moving quickly and accepting a higher risk of pivoting or failing?
Both approaches have their advocates, and the best choice depends on the situation.
Ready, Aim, Fire tends to be superior when the cost of execution is high and a study can shed light on how useful or valuable a project could be. For example, if you can brainstorm a few other use cases ( restaurants, airlines, telcos, and so on) and evaluate these cases to identify the most promising one, it may be worth taking the extra time before committing to a direction.
Ready, Fire, Aim tends to be better if you can execute at low cost and, in doing so, determine whether the direction is feasible and discover tweaks that will make it work. For example, if you can build a prototype quickly to figure out if users want the product, and if canceling or pivoting after a small amount of work is acceptable, then it makes sense to consider jumping in quickly. When taking a shot is inexpensive, it also makes sense to take many shots. In this case, the process is actually Ready, Fire, Aim, Fire, Aim, Fire.
After agreeing upon a project direction, when it comes to building a machine learning model that’s part of the product, I have a bias toward Ready, Fire, Aim. Building models is an iterative process. For many applications, the cost of training and conducting error analysis is not prohibitive. Furthermore, it is very difficult to carry out a study that will shed light on the appropriate model, data, and hyperparameters. So it makes sense to build an end-to-end system quickly and revise it until it works well.
But when committing to a direction means making a costly investment or entering a one-way door ( meaning a decision that’s hard to reverse), it’s often worth spending more time in advance to make sure it really is a good idea.