<p class="MsoNormal">In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can be learned from experience. At the end of the episode, we discuss one (unproven) theory from the field of neuroscience that our "dreams" are actually neural simulations of variations of events we have experienced during the day and "unlearning" of these dreams helps us to organize our memory!</p>
<p class="MsoNormal">Visit us at: <a href="http://www.learningmachines101.com">www.learningmachines101.com</a> to obtain additional references, make suggestions regarding topics for future podcast episodes by joining the learning machines 101 community, and download free machine learning software! </p>

LM101-023: How to Build a Deep Learning Machine

Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life.  For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that will be addressed in this podcast series!