In this episode, Byron and Mike talk about AGI, Turing Test, machine learning, jobs, and Takt.
Mike Tamir serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science masters program. Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH. Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.
Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. I’m excited today, our guest is Mike Tamir. He is the Chief Data Science Officer at Takt, and he’s also a lecturer at UC Berkeley. If you look him up online and read what people have to say about him, you notice that some really, really smart people say Mike is the smartest person they know. Which implies one of two things: Either he really is that awesome, or he has dirt on people and is not above using it to get good accolades. Welcome to the show, Mike!
Mark Cuban came to Austin, where we’re based, and gave a talk at South By Southwest where he said the first trillionaires are going to be in artificial intelligence. And he said something very interesting, that if he was going to do it all over again, he’d study philosophy as an undergrad, and then get into artificial intelligence. You studied philosophy at Columbia, is that true?
I did, and also my graduate degree, actually, was a philosophy degree, cross-discipline with mathematical physics.
So how does that work? What was your thinking? Way back in the day, did you know you were going to end up where you were, and this was useful? That’s a pretty fascinating path, so I’m curious, what changed, you know, from 18-year-old Mike to today?