9: ML Experimentation - Fail Fast, Learn Faster

Experimentation is the foundation of machine learning and artificial intelligence model development. As William Blake said, ‘The true method of knowledge is experiment.’  An experiment is a way to answer the question, what happens when? Like what happens when I try this combination of chess moves? While the principles are the same, experimenting in the digital world of ML is very different than in the physical world. As a human being, I am limited in my ability to try out x number of combinations in a day, because I have to sleep and eat and rest and take mental breaks. But a computer never tires. It can crunch numbers all day and all night until it has tried every possible combination. In ML, there is the unique ability to more freely rely on computational power and big data to run thousands of simulations until some consensus is reached. In this episode, Saurabh and Melody talk to Alegion’s Chief Data Scientist, Cheryl Martin, all about experimentation in ML and how to fail fast to learn faster. 

Om Podcasten

No BiAS is a podcast about the emerging and ever-shifting terrain of artificial intelligence and machine learning. Each episode your host, Melody Travers, University of Potsdam, gets to pick the very big brains of machine learning researchers Nikhil Kumar, Carnegie Mellon University, and Saurabh Bagalkar, Arizona State University, and hear their different perspectives on the frontier of AI technology.