[MUSIC] Can you tell us a little bit about the work you're doing with self-driving cars. >> I've been working on self-driving cars for the last few years. It's a domain that's exploded, obviously, in interest since early competitions back in the 2005 domain. And what we've been working on really is putting together our own self-driving vehicle that was able to drive on public roads in the regional Waterloo last August. With the self-driving cars area, one of our key research domains is in 3D object detection. So this remains a challenging task for algorithms to perform automatically. Trying to identify every vehicle, every pedestrian, every sign that's in a driving environment. So that the vehicle can make the correct decisions about how it should move and interact with those vehicles. And so we work extensively on how we take in laser data and vision data and radar data. And then fuse that into a complete view of the world around the vehicle. >> When we think of computer vision, we usually think immediately of self-driving cars, and why is that? Well, it's because it's hard to pay attention when driving on the road, right? You can't both be looking at your smartphone and also be looking at the road at the same time. Of course, it's sometimes hard to predict what people are going to be doing on the street, as well. When they're crossing the street with their bike or skateboard, or whatnot. So it's great when we have some sort of camera or sensor that can help us detect these things and prevent accidents before they could potentially occur. And that's one of the limitations of human vision, is attention, is visual attention. So I could be looking at you, Rav, but behind you could be this delicious slice of pizza. But I can only pay attention to one or just some limited number of things at a time. But I can't attend to everything in my visual field all at once at the same time like a camera could. Or like how computer vision could potentially do so. And so that's one of the great things that cameras and computer vision is good for. Helping us pay attention to the whole world around us without having us to look around and make sure that we're paying attention to everything. And that's just in self-driving cars, so I think we all kind of have a good sense of how AI and computer vision shapes the driving and transportation industry. >> Well, self-driving cars are certainly the future. And there's tremendous interest right now in self-driving vehicles. In part because of their potential to really change the way our society works and operates. I'm very excited about being able to get into a self-driving car and read or sit on the phone on the way to work. Instead of having to pilot through Toronto traffic. So I think they represent a really exciting step forward, but there's still lots to do. We still have lots of interesting challenges to solve in the self-driving space. Before we have really robust and safe cars that are able to drive themselves 100% of the time autonomously on our roads. >> We've just launched our own self-driving car specialization on Coursera. And we'd be really happy to see students in this specialization also come and learn more about self-driving. It's a wonderful starting point, it gives you a really nice perspective on the different components of the self-driving software stack and how it actually works. So everywhere from how it perceives the environment, how it makes decisions and plans its way through that environment. To how it controls the vehicle and makes sure it executes those plans safely. So you'll get a nice broad sweep of all of those things from that specialization. And from there you then want to become really good and really deep in one particular area, if you want to work in this domain. Because again, there's so many layers behind this. There's so much foundational knowledge you need to start contributing that you can't go wrong. If you find something interesting, just go after it. And I am sure there'll be companies that'll need you for this. [MUSIC]
