ep.9: New Voices in AI: environmental conservation, with Lily Xu

In the theoretical ecology world, for example, what they've been doing for several decades now, planning and what they call adaptive management, looks at Markov decision processes to model how environments change over time If an animal population has 100 individuals, and then a year passes and there's like no hunting, then the population will increase to some amount. But then if there's hunting or if there's a drought or something like that, then the population would decrease by this amount. Those are probabilistic systems that you can model. Markov decision processes have been used in computer science for several decades, and it's awesome that this model has been useful in ecology as well. But then in the past10 or so years, there's been a lot of new advances in computer science for planning using reinforcement learning enabling us to model these systems more effectively, account for uncertainty, do robust planning.