In Machine Learning You Want to Minimize the Free Energy

Richard Feynman invented the bound approximation that was used in machine learning. It's a way of turning impossible integration inference marginalization problems into an optimization problem. The idea is to create a quantity that you can measure, represent and compute that is always bound but smaller than or bigger than what you want to maximize or minimize. Machine learning uses this technique to learn more about human surprises.