Dr. Murray Cantor has a storied career that spans decades. Recently, he founded Aptage, a company that provides project risk management tools using Bayesian Estimation and machine learning. He’s also the chief scientist at Hail Sports, which focuses on applying precision medicine techniques to sports performance. In his spare time, he’s a consulting mathematician at Pattern Computer, a firm that engineers state-of-the-art pattern recognition solutions for industrial customers. Join Murray and I as we explore the cutting edge of AI and cover:

Murray’s approach to automating processes that humans typically do, the role humans have in the design phase, and how he thinks about designing affordances for human intervention in automated systems

Murray’s opinion on causal modeling (explainability/interpretability), the true stage we are in with XAI, and what’s next for causality in AI models

Murray’s opinions about the 737 Max’s automated trim control system interface (or lack thereof) and how it should have been designed The favorite method Murray has for predicting outcomes from small data sets

The major skill gaps Murray sees with young data scientists in particular

How using science fiction stories can stimulate creative thinking and help kick off an AI initiative successfully with clients, customers and stakeholders

Resources and Links

Murray Cantor on LinkedIn New York Times Expose article on the Boeing 737 Max New Your Times Article on the 737 Max whistleblower

Quotes from Today's Episode

“We're in that stage of this industrial revolution we're going through with augmenting people's ability with machine learning. Right now it's more of a craft than a science. We have people out there who are really good at working with these techniques and algorithms. But they don't necessarily understand they're essentially a solution looking for a problem.” — Murray “A lot of design principles are the same whether or not you have AI. AI just raises the stakes.” — Murray “The big companies right now are jumping the guns and saying they have explainable AI when they don't. It's going to take a while to really get there.” — Murray “Sometimes, it's not always understood by non-designers, but you're not testing the people. You're actually testing the system. In fact, sometimes they tell you to avoid using the word test when you're talking to a participant, and you tell them it's a study to evaluate a piece of software, or in this case a cockpit,  to figure out if it's the right design or not. It’s so that they don't feel like they're a rat in the maze. In reality, we’re studying the maze.” — Brian “Really fundamental to understanding user experience and design is to ask the question, who is the population of people who are going to use this and what is their range of capability?” - Murray “Take the implementation hats off and come up with a moonshot vision. From the moonshot, you might find out there are these little tangents that are actually feasible increments. If you never let yourself dream big, you'll never hit the small incremental steps that you may be able to take.” — Brian

Transcript

Brian:  All right, welcome back to experiencing data. Today I've got Murray Cantor on the line who is... has several different titles in your work, but you're currently the C