For this set of two shows, we decided to do a forum of just us regulars, and we were going to look at a couple of news stories. Those stories turned out to be the bulk of an extended conversation. We realized that the theme in all of them were the claims that companies made vs. what actually happens in workplaces and organizations.
Needless to say, this is an opinionated set of shows this go around as we discuss the promise of Machine Learning and what it actually delivers. We look at it in the light of other promises made over the past fifty years and how, often, it’s not the best idea that wins the day, but the first idea to gain traction that does.
We are back with Peter Varhol for Part 2 of our discussion on Machine Learning and AI. In this episode, we pick up with Peter and discuss the pitfalls of machine learning algorithms, where they can help us and where they often fail us. Also, what is the role of the tester and testing in machine learning and AI? How intimately involved with the problem domain do testers need to be? How can they learn to understand the analytical parts and what they need to accomplish within that problem domain.