How are we supposed to think about Machine Learning? How are businesses going to change? This week I interview Joshua Gans (youtube, mp3), Professor of Strategic Management at the Rotman School of Business at the University of Toronto and the Chief Economist at the University’s Creative Destruction Lab. Joshua is the co-author, along with Ajay Agarwal and Avi Goldfarb, of Prediction Machines: The Simple Economics of Artificial Intelligence.
Links for this show:
On why economists are joining tech companies?
Joshua Gans: ..we’re talking here about artificial intelligence and if there’s ever a place where, academics and business of sort of fused together, it’s in that field, you know, all of the main pioneers of artificial intelligence, almost, almost all are, um, not now purely academics… I think there are situations in which maybe always has been more integration versus others..
David Wright: and maybe you identified an important idea there which is a lot of technology emerges from academia as well from engineering departments and computer science departments and so they sort of naturally dragged along a few of their friends. Maybe instead you should join us, you’d have something to say here…
Commenting on Benedict Evan’s conception of Machine Learning as data processing:
David Wright: So there’s an analyst who works for Andreessen Horowitz: Benedict Evans, you’ve probably heard of him. And he has a framework for evaluating AI. He wrote a blog post a couple months ago where he said, really, there’s three ways of thinking about the applications.
- The first is do the things we’ve already do, but doing them better.
- Then: Do you ask new questions of existing data that we already have.
- And the third is bringing new data to analyze.
The third one is the most advanced one, the ones that’s the most sexy, let’s call it. And that’s you spend your time second ago talking about but the first two are really probably where we’re generating a lot more of the value I would argue. And so how should we think about that? As a kind of evolution of the ability of AI to do things we already do but a little bit better..
Joshua Gans: I mean the issue that I have with that setup of, you know, what is it doing, it’s not that it’s wrong, but it’s like hard to see. Interesting because you know, we’re going to learn stuff from data and this is true. And so, you know, that data, more data, new data, the whole thing. It tends to put an emphasis on finding the data. But the way we see it, *it’s more finding the problem.*
The bottom line:
Joshua Gans: I think in the next let’s be more interesting in the next five years, there’ll be a startup somewhere who manages to reformulate what, what wasn’t a prediction problem as a prediction problem. Solve that. And it impacts broadly on our lives. The one thing I know about these radical innovations is how they actually ended up manifesting themselves, was always different from what people were imagined at this stage and you know, and I think the same is going to be true of AI.
All that and much more, including a theory of the mind, a discussion of physical intelligence and of course applications for the insurance industry!
Are you an actuary? Someone you know? Check out the Not Unprofessional Project, for the price of a CAS webinar you get unlimited access to content dedicated to Continuing Education Credits for Actuaries, especially Professionalism credits. CE On Your Commute!
Subscribe to the Not Unreasonable Podcast in iTunes, stitcher, or by rss feed. Sign up for the mailing list at notunreasonable.com/signup. See older show notes at notunreasonable.com/podcast.