Less Algorithm, More Application: Lyft’s Craig Martell

**Sam Ransbotham** (0:03)
Are algorithms getting less important? As algorithms become commoditized, it may be less about the algorithm and more about the application.
In our first episode of season two of Me, Myself, and AI, we'll talk with Craig Martell, head of machine learning at Lyft, about how Lyft uses artificial intelligence to improve its business. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Information Systems at Boston College. I'm also the guest editor for the AI and Business Strategy Big Idea Program at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:44)
And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

**Sam Ransbotham** (1:13)
Today we're talking with Craig Martell.
Craig is the head of machine learning for Lyft. Thanks for joining us today, Craig.

**Craig Martell** (1:19)
Thanks, Sam, I'm really happy to be here. These are pretty exciting topics.

**Sam Ransbotham** (1:22)
So Craig, head of machine learning at Lyft, what exactly does that mean and how did you get there?

**Craig Martell** (1:28)
So let me start by saying, I'm pretty sure I won the lottery in life and here's why.
I started off doing political theory academically and I have this misspent youth where I gathered a collection of master's degrees along the way to figure out what I wanna do. So I did philosophy, political science, political theory, logic, and I ended up doing a Ph.D in computer science at Penn. And I sort of thought I was gonna do testable philosophy. And so the closest to that was doing AI. So I just did this out of love. Like I just find the entire process and the goals and the techniques just absolutely fascinating.

**Sam Ransbotham** (2:04)
All part of your master plan at all. Not at all.

**Craig Martell** (2:06)
I just fell into it. I fell into it.

**Sam Ransbotham** (2:09)
So how did you end up then at Lyft?

**Craig Martell** (2:11)
So I was at LinkedIn for about six years. And then my wife got this phenomenal job at Amazon and I wanted to stay married. So I followed her to Seattle. I worked for a year here at Dropbox and then Lyft contacted me.
And I essentially jumped to the chance because the space is so fascinating. I love cars in general, which means I love transportation in general. And the idea of transforming how we do transportation is just a fascinating space.
And then in my prior life, I was a tenured computer science professor, which is still a big love of mine. And so I am an adjunct professor at Northeastern just to make sure I keep my teaching skills up.

**Shervin Khodabandeh** (2:50)
Craig, your strong humanities background in philosophy, political science, you mentioned logic, all of that. How did that play for you in your overall journey?

**Craig Martell** (3:01)
So that's really interesting.
When I think about what AI is, I find the algorithm is mathematically fascinating, but I find the use of the algorithm is far more fascinating because from a technical perspective, we're finding correlations in extremely high dimensional nonlinear spaces. It's statistics at scale in some sense, right? We're finding these correlations between A and B. And those algorithms are really interesting and I'm still teaching those now and they're fun.
But what's more interesting to me is what do those correlations mean for the people? Like I think every AI model launched is a cognitive science test.
Like we're trying to model the way humans behave. Now for automated driving, we're modeling the way cars behave in some sense, but it's really, we're modeling the right human behavior given these other cars driven by humans. So for me, I just, I think the goals of AI, I look at them much more from humanity's perspective, although I can nerd out in the technical side as well.

**Sam Ransbotham** (3:58)
Can you say a bit more about how Lyft organizes AI and ML teams?

**Craig Martell** (4:02)
We have model builders throughout the whole company. We have a very large science org. We also have what we call ML suies, so ML software engineers. I run a team called LyftML, and it consists of two major teams. One is called AppliedML, where we leverage expertise in machine learning to tackle some really tough problems.
And also the ML platform, which drives my big interest in operational excellence on getting ML to make sure it's effectively hitting business metrics.