Byron Galbraith is the Chief Data Scientist and co-founder of Talla, where he works to translate the latest advancements in machine learning and natural language processing to build AI-powered conversational agents. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has also held several software engineering roles including back-end system engineer, full stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.
What color should that button be to convert more sales? What ad will most likely get clicked on? What movie recommendations should be displayed to keep subscribers engaged? What should we have for lunch? These are all examples of iterated decision problems — the same choice has to be made repeatedly with the goal being to arrive at an optimal decision strategy by incorporating the results of the previous decisions. In this talk I will describe the Bayesian Bandit solution to these types of problems, how it adaptively learns to minimize regret, how additional contextual information can be incorporated, and how it compares to the more traditional A/B testing solution.