The Feinstein Institutes for Medical Research has spun out a startup whose artificial-intelligence device could help paralyzed patients regain the use of their hands. Earlier this month, the startup, Neuvotion Inc., announced a $1.1 million funding round from the Long Island Angel Network and the Good Shepherd Rehabilitation Network based in Allentown, Pennsylvania. The Darien, Connecticut, startup is in the process of transferring research developed in the laboratory of Chad Bouton, vice president of advanced engineering at the Feinstein Institutes, a unit of Northwell Health. Bouton also is founder of Neuvotion. The company's initial device, NeuStim, is worn as a patch on the patient's forearm and is being positioned for use in clinics and at home.

The idea of implementing reinforcement learning in a computer was one of the earliest ideas about the possibility of AI, but reinforcement learning remained on the margin of AI until relatively recently. Today we see reinforcement learning playing essential roles in some of the most impressive AI applications. This article presents observations from the author’s personal experience with reinforcement learning over the most recent 40 years of its history in AI, focusing on striking connections that emerged between largely separate disciplines and on some of the findings that surprised him along the way. These connections and surprises place reinforcement learning in a historical context, and they help explain the success it is finding in modern AI. The article concludes by discussing some of the challenges that need to be faced as reinforcement learning moves out into real world.