When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. Bestseller 26,813 students enrolled Created by Lazy Programmer Inc Apply gradient-based supervised machine learning methods to reinforcement learning Understand reinforcement learning on a technical level Understand the relationship between reinforcement learning and psychology Implement 17 different reinforcement learning algorithms Calculus (derivatives) Have experience with at least a few supervised machine learning methods Good object-oriented programming skills When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

These days, it seems that nearly everything is a smart product. We have smart boards in K-12 classrooms, smart watches, and even smart refrigerators. You can immediately tell that manufacturers love to use this misguided descriptor whenever they integrate modern technology, like touch screens or internet connectivity, to a previously existing product. Do products like these deserve this term? What exactly makes them smart?