In a previous episode, we discussed Markov Decision Processes or MDPs, a framework for decision making and planning. This episode explores the generalization Partially Observable MDPs (POMDPs) which are an incredibly general framework that describes most every agent based system.

Making a decision is a complex task. Today's guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts for autonomous decision making based on probabilistic modeling, reinforcement learning, and game theory. The aim is so that an AI system could make decisions just as good as humans can.

In many real world situations, a person/agent doesn't necessarily know their own objectives or the mechanics of the world they're interacting with. However, if the agent receives rewards which are correlated with the both their actions and the state of the world, then reinforcement learning can be used to discover behaviors that maximize the reward earned.

In this week’s episode, Kyle is joined by Risto Miikkulainen, a professor of computer science and neuroscience at the University of Texas at Austin. They talk about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. They also discuss some of the things Sentient Technologies is working on in stock and finances, retail, e-commerce and web design, as well as the technology behind it-- evolutionary algorithms.