News Stephanie Gil, Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), researches how robots, drones, autonomous vehicles, and other physical agents can make decisions, coordinate with one another, and act effectively in complex real-world environments. Much of her recent work focuses on how advances in artificial intelligence and machine learning can help autonomous systems move from controlled settings into the physical world. The following Q&A was developed from interviews with Gil. It has been edited for clarity, length, and context. Q: Why is it difficult to get autonomous systems, like autonomous vehicles, to work well in real-world environments? A: One of the biggest barriers is uncertainty. When you put robots or autonomous systems into the real world, they do not have perfect information. There may be parts of the environment that are simply unknown. The data they sense directly may be incomplete. The data they receive from another robot or from the network may not be trustworthy. But even with all of that uncertainty, the system still needs to make decisions that are helpful and useful. If it is a drone working with a search and rescue team, it needs to decide where to go next. If it is an autonomous rideshare vehicle, it needs to coordinate with other vehicles and respond to requests around a city. If it is working with marine biologists, it needs to be in the right place at the right time to collect useful data. That question — how do you make good decisions under uncertainty — is central to a lot of the work in my group. Q: This problem has been partly solved, right? Autonomous vehicles have been deployed for ridesharing in major cities. A: If you think about autonomous rideshare vehicles as