 Welcome. We're going to talk about self-driving cars today. Last year, I said that in the future, when you build cars, it's going to be a lot more like a computer. Computer vision technology that had evolved up to this point are going to be difficult to continue to advance to the point where we could have cars that drives itself. All of this computation capability is going to have to be done in real time. All of this, we've been working on the last year. Ladies and gentlemen, the NVIDIA DRIVE PX2. This is the world's first in-car AI supercomputer, and it's designed to make it possible for us to realize the vision of self-driving cars. The computational capability of PX2 is equivalent to 150 MacBook Pros. All together, this entire supercomputer fits in your trunk very nicely, the size of a lunchbox. Our vision is to make it possible for us to finally realize the self-driving car. Humans are the least reliable part of the car. We represent almost all of the fatalities that are caused around the world, over a million deaths each year. We're replacing the human altogether. Self-driving car technology is surely going to make a great contribution to society. The biggest problem is perception. First of all, what is happening around me? What are things that I should be concerned about? And how should the car deal with it? The folks at NVIDIA Research worked together to make it possible to take advantage of the CUDA GPU that we invented. We were able to accelerate the training by 30 to 40 years. Deep learning is able to achieve superhuman perception capability. It is now possible for us to train these incredibly complex networks to recognize objects of all kinds. Just to show it to you, let's now see what it can do. It took a month to train the original network with the ImageNet data set. Without GPU acceleration, that month would have been a couple of years. This is an upcoming data set that will be publicly available soon. It's a cityscape data set. There's more training images and they're very, very finely segmented and detailed. It's a very modern data set. Not one feature detector was coded by hand. It's basically like holding up millions and millions of flashcards to the computer and telling it to learn and basically nudging it in the right direction when it gets things wrong. So this gives you the next level of perception saying, well, you know, what can I drive on? What is this thing at this pixel that I'm looking at? So it's a much more robust way to handle perception in a car. But there's so much more to do. There's so much more to do. Not only do we want to be able to recognize objects, we want to be able to recognize circumstances. Every bus is not the same. There are passenger buses, public transit buses where it's okay to just drive by. School buses, you should stop. Every truck is not a truck. Some trucks are ambulances and you should pull aside. All of these different scenarios, not only do you have to recognize what they are, but understand the circumstances, the special circumstances. These are all trainable things. So whereas today we're focused on training detection, perception, very shortly we're going to move towards training for recognizing circumstances.
 Welcome. We're going to talk about self-driving cars today.
 Last year, I said that in the future, when you build cars, it's going to be a lot more
 like a computer. Computer vision technology that had evolved up to this point are going
 to be difficult to continue to advance to the point where we could have cars that drives
 itself. All of this computation capability is going to have to be done in real time.
 All of this, we've been working on the last year. Ladies and gentlemen, the NVIDIA DRIVE
 PX2. This is the world's first in-car AI supercomputer, and it's designed to make it possible for
 us to realize the vision of self-driving cars. The computational capability of PX2 is equivalent
 to 150 MacBook Pros. All together, this entire supercomputer fits in your trunk very nicely,
 the size of a lunchbox. Our vision is to make it possible for us to finally realize the
 self-driving car. Humans are the least reliable part of the car. We represent almost all of
 the fatalities that are caused around the world, over a million deaths each year. We're
 replacing the human altogether. Self-driving car technology is surely going to make a great
 contribution to society. The biggest problem is perception. First of all, what is happening
 around me? What are things that I should be concerned about? And how should the car deal
 with it? The folks at NVIDIA Research worked together to make it possible to take advantage
 of the CUDA GPU that we invented. We were able to accelerate the training by 30 to 40
 years. Deep learning is able to achieve superhuman perception capability. It is now possible
 for us to train these incredibly complex networks to recognize objects of all kinds. Just to
 show it to you, let's now see what it can do. It took a month to train the original
 network with the ImageNet data set. Without GPU acceleration, that month would have been
 a couple of years. This is an upcoming data set that will be publicly available soon.
 It's a cityscape data set. There's more training images and they're very, very finely segmented
 and detailed. It's a very modern data set. Not one feature detector was coded by hand.
 It's basically like holding up millions and millions of flashcards to the computer and
 telling it to learn and basically nudging it in the right direction when it gets things
 wrong. So this gives you the next level of perception saying, well, you know, what can
 I drive on? What is this thing at this pixel that I'm looking at? So it's a much more robust
 way to handle perception in a car. But there's so much more to do. There's so much more to
 do. Not only do we want to be able to recognize objects, we want to be able to recognize circumstances.
 Every bus is not the same. There are passenger buses, public transit buses where it's okay
 to just drive by. School buses, you should stop. Every truck is not a truck. Some trucks
 are ambulances and you should pull aside. All of these different scenarios, not only
 do you have to recognize what they are, but understand the circumstances, the special
 circumstances. These are all trainable things. So whereas today we're focused on training
 detection, perception, very shortly we're going to move towards training for recognizing
 circumstances.