Home » Nvidia CEO Jen-Hsun Huang Keynote at CES 2017 (Full Transcript)

Nvidia CEO Jen-Hsun Huang Keynote at CES 2017 (Full Transcript)

Jen-Hsun Huan

Nvidia, founder and CEO Jen-Hsun Huang gave his opening keynote address at CES 2017 on Wednesday, January 4, 2017 at Las Vegas. Following is the full transcript of the CES keynote event.



Gary Shapiro – President and CEO, CTA

Jen-Hsun Huang – Founder and CEO, Nvidia

Aaron Flynn – General Manager, BioWare

Scott Keogh – President of Audi America


Listen to the MP3 Audio here: Nvidia CEO Jen-Hsun Huang Keynote at CES 2017



Gary Shapiro – President and CEO, CTA

[Audio starts abruptly]…. more than tripling. He visited the CTA headquarters in 2008 and he spoke to our staff at our staff meeting. He was also profiled that year on the cover story for CTA’s i3 magazine. He is focused, engaging and certainly visionary. Ladies and gentlemen, please welcome Nvidia’s founder and CEO Jen-Hsun Huang to share his vision of the exciting future ahead.

Jen-Hsun Huang – Founder and CEO, Nvidia

Thank you for that great introduction, Gary. It’s great to be here. We have a special treat for you, so we’re going to just walk offstage real quick, play this video for you and we’ll be right back.

[Video Presentation: (Voice-over) It all starts with our power to imagine, bringing the future we dream to life in amazing new ways, stories that take us to far off worlds, brilliant ideas that lead off the screen and incredible new adventures around every corner. Imagination has unlocked the promise of AI where robots help us build a better future, give us a helping hand and even entertain us. Imagination fuels new breakthroughs and autonomous vehicles letting us take to the road without taking the route, delivering the world to our doorstep and driving our competitive spirit. Imagination powers exciting new discoveries and drive to accelerate the cure for cancer, give the death a new voice and help the injured become whole again. Our imagination opens up amazing new worlds, Nvidia brings them to life with AI computing that turns visionary ideas into life-changing innovation and sparks our sense of wonder about what comes next. – Video concludes]

We are going through unquestionably the most exciting times in the computer industry that all of us have ever witnessed. What we thought was going to be science fiction for years to come is becoming reality as we speak. Our work at Nvidia is dedicated towards a computing model focused on visual and AI computing. It’s built on top of the GPU that we pioneered. This computing model is able to solve problems that normal computing is unable to solve and we dedicate ourselves to challenging — tackling the most challenging computing problems in the world.

There are four areas that we focus on. Surprisingly for many, over the years we dedicated ourselves to video games, not to mention the fact that it’s incredibly fun, it’s incredibly beautiful, and we love it. Video games is also the highest volume, most computationally intensive application the world has ever known. It is about achieving virtual reality. And now all of a sudden all the technologies are coming together for us to finally achieve virtual reality, augmented reality, mixed reality and bring together the experience of the holodeck for real for the first time. Computer graphics technology, computer vision technology and artificial intelligence will come together to realize this exciting new computing platform that we call VR, AR or mixed reality.

Our technology is also used in cloud. AI supercomputers are being built all over the world today so that all of you, when you’re talking to the internet, when you’re making queries, those searches are passed through artificial intelligence, so that the query that you make, the assist that you seek for is much more helpful to you.

And then lastly, some of the most exciting things working on today, the most impactful work that we’re doing for the society, for the industry, self-driving cars and autonomous vehicles. These four areas we’ve been endeavoring for some time. And then all of a sudden the last several years an enormous breakthrough happened.

Researchers all around the world working on a new field, a new technique of machine learning called deep learning met the GPU and the Big Bang of artificial intelligence happened. This technique allows software to write software, allows computers to learn from experience and data, and allows the computer to recognize complex patterns that are easy for you and I, but incredibly hard for computers. And it does so — it does so by hierarchically building up feature representations to represent very complex information, complex patterns but building it up from hierarchies of simpler patterns, the ability to recognize a face, for example, with its infinite variability, built on layers and layers and layers of artificial networks. The lowest layer could just be edges, edges made up the pixels, the layer above that could be contours, shapes, textures, motifs, the layer before above that could be parts of a human face and then eventually it’s able to understand the representation of a face, and understand the representation of a face in its incredible variability. It could be — you could be wearing your hair a little bit differently. You could be wearing a hat, you could be looking away partly and somehow — somehow we human can recognize that person.

Finally, for the very first time using this technique called deep learning, we’re able to do the same with computers. This ability to perceive the world is just an enormous breakthrough and I’m going to show you why. Why this foundational technology, foundational capability was so important. It just had one incredible challenge, one incredible handicap. And that is the amount of computation necessary, the amount of data processing necessary is absolutely enormous. And then one day — one day the AI researchers met the GPU that we invented. And the Big Bang of modern AI happened.

The achievements have been fast and furious. Some of the things that we’ve been able to accomplish in just the last several years: absolutely mind-blowing. You have all heard about the AlphaGo achievement. Demis Hassabis and his DeepMind team was able to teach a computer how to play Go, the most complex game we know how, more variability than all the atoms in the universe, more moves. And yet this computer was able to learn Go from the world’s masters and then play the master of our modern era and beat it.

Network has been able to play Doom, which is a game of maze, maze finding, resource management while you’re staying away from monsters. A network is able to learn how to play Go. A network has been able to learn the style of artists van Gogh, Monet, Picasso and apply that style to photographs. A network has been able to synthesize our voice instead of our voices stitched together from a whole bunch of little tiny chunks. This network is able to learn the tonation of our voice and from the words that we feed it synthesized how we would speak. A network was able to learn to recognize an image and understand its context and caption that image. A network was able to turn images that it sees in computer cameras and translated directly to repeat a trial-and-error called reinforcement learning to eventually adjust its motors and learn motor skills. A network was able to learn how to walk by itself. Just by teaching the kinematics of a robot, a robot that is actually sitting on the ground after repeated trying the robot was able to stand up and walk. And in fact, we’ve been able to teach a car how to drive.

Driving is a skill, it’s not mathematics. Kids can learn how to drive, adults drive and yet we do know computation whatsoever, we do know Newtonian physics whatsoever, in our head we just drive. We’ve been able to teach a car how to drive. The achievements that you see in front of you is impossible until just recently. It was just impossible until recently, and all of a sudden because we’re now able to understand the complex nature of the world, to perceive the world through vision, through audio technology, through natural language. We’re now able to apply artificial intelligence to solve problems that we had never conceived in the past.

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