GTC 2017: Tesla V100: Powering Our AI Future (NVIDIA keynote part 7)

and let's let's go through a couple of quick demos you know you guys know that that it's a GPU so although I haven't spoken much about graphics it is able to do graphics and so 10 days ago 10 days ago I reached out to Devadas on Chabad asan is is the head of the studio at square enix and this is as you know the 30 years of square and they're well known around the world for the incredible cinematic production value of their films and video games and with with this generation with Final Fantasy 15 his vision Tabata son's vision was to unify the pipeline unify the workflow unified the graphics engine of cinematic film and real-time computer graphics with a vision that some day cinematic film and computer graphics have essentially the same visual effects and so 10 days ago I reached out to the balasana I asked him if he if he could you know do something for all of you and he just jumped on the opportunity he first of all he apologized he said look I just thought there's not enough time to really do anything at the level that Square would like to do however he dedicated his engineers they worked around the clock they basically haven't gone to sleep for 10 days and let's take a look at the great gift that Tabata sign and the Square Enix guys have done for us first stimulators mother simulators you're not a geometry in here the lighting system the soft shadows [Music] the character is directly out of the movie metal looks like metal you can almost touch the leather that's a good leather jacket guys that's a good leather jacket I just noticed that that's a good list I'm gonna have to call it the bottom son okay good job amazing and so so he also sent us he also sent us what he believes that it will look like someday in a near future and this is what video games will look like ladies and gentlemen a quick trailer Kings clave that's how I want my video game to look this engine is called the luminous engine it incorporates a hundred percent of all the physics processing that we've developed in game works and all the particle systems you saw the explosions the fire all of the destruction so beautiful all made possible because of a video physics engine well let's think about let's look at something new several years ago when we announced Kepler which was a groundbreaking GPU it was our first double precision it was our first double precision GPU it is the GPU that ended up in our nation's fastest supercomputer the Oakridge Titan it is the namesake of the GeForce Titan X and then the fastest supercomputer in our nation the fastest supercomputer you can build for yourself the type next several years ago we demonstrated Kepler simulating the future of our world you know people always look at the past of our world by looking at all the images from the sky and we could we can learn about how our universe was created and informed but very few people really think about how our universe is going to turn out well in order to figure out how our universe is going to turn out we have astrophysicists in our company as I mentioned earlier one of the things that we're super proud of is we have computer vision experts we have astrophysicists we have quantum chemists we have all of these look we have molecular biologists we have people who are expert in these computational Sciences so that we can work with all of you to advance your work and so ladies and gentlemen Steven Jones is going to give us what the galaxy looks like billions of years from now okay to you Velma it looked like a piece of artwork but that is actually a live simulation that we're showing an n body simulation code here courtesy of your own bergdorf sand salon porter he's part of leiden observatory and this is a simulation of the andromeda galaxy on the right hand side and it takes even wait wait I need your demo to last less than a billion years all right I get going I've got a skew title B so we'll start so yeah follow me on trauma two here and it's flying in towards the Milky Way and in five billion years that for billing is it's going to make a closed path it's going to swing past us and it's going to start spinning stars of the Milky Way and the Andromeda but gravity's going to inevitably take over and what you can see where we're running this on Volta and the number of stars we can simulate a hundred million bodies per second we can see the bar structure of the Milky Way right there right next to us as it comes plunging back for its second pass towards the core of the Andromeda galaxy and then the cores get much closer and you start seeing stars being flung off to all sides one of the amazing things is you can look out into the universe and you can see galaxies colliding in just this way and you see the same kinds of structures and see these stars being thrown off in waves as the cause orbit each other and then finally merge and so about 5.3 billion years you can see the timer in the corner there the merge is finally this is at this at the moment when all the stars gets thrown away and we get left with one single giant galaxy fusion of the two with a whole pile of stars probably including our Sun unfortunately to flying out into the universe all right thank you all right so but so the amazing thing that the amazing thing about that demonstration before I go into the next one they may be thing about that demonstration is this five years ago we showed it to you on Kepler versus today basically was eight times seven to eight times performance improvement in five years seven to eight times improvement in five years so we say 7 to 8 times performance in five years and during that same time the microprocessor sir has improved in performance by about 50% okay 50% another way to think about it seven to eight times in five years it's basically about seventy percent per year the benefits of accelerated computing okay so one of the one of the really important and only at Nvidia what engineers love so much watching galaxies make love that science porn right there brother Thank You Steven thanks everybody that was incredible where it was beautiful okay now let's talk about deep learning so recently Cornell did paper that was really amazing so what they were able to do is you guys know that that it's now possible to take two pieces of art and learn the learn learn learn the style of Picasso or Monet or Van Gogh and you could apply it to a photograph and it turns your photograph into a Monet okay change your photograph into money it's called style transfer well the Cornell team dr. balitz team realized that that what this left behind is artistic meaning that that it's distorted the photograph is now distorted no longer retains its original fidelity it doesn't look like what it used to look like the buildings don't look like buildings anymore they kind of look like buildings it applies art to cats and dogs and grass and so what they would like to do is photo graphic style transfer you learn the style from one photograph and you apply it to another photograph you learn to supplement one photograph you learn you applied to another program basically the way works well you know what I'm going to do is I'm going to I'm going to kick it off Julie the my dad please did I say right the Marais demerit demerit a you know some mouth gray I said that so that Madison could hear it she's in France and she's gonna come up dumb okay come okay tonight I mean I could start all of you okay all right so go do I have to click hi Julie de la hey super play Julie is one of our deep learning computer scientists and she speaks French and she was a professor and so so she is going to learn English so she could communicate with me and I'm going to learn French so I can understand her and we're going to use transfer learning to achieve that so one one photograph the first photograph we learn the style of that okay next please keep on going and we're going to try to apply it to this one now the thing that I has to understand is how to understand the structure as well as the style it has to understand the structure as well as the style because it needs to apply the right style to the right areas of the photograph and so it needs to understand a building is a building a cats a cat you know waters water to walk away to walk away the clouds are clouds and when it applies it it generates it it's drawing the pixels one at a time regenerating the photograph in this new style and when the Cornell engineers wrote the paper they use the title next to do that and it took three to four minutes to process this image this is now on Volta go ahead fart away and so it starts out here's this artificial intelligence network is trying to draw this image it's trying to generate this image from scratch it took this these this style and this image and it says I want to recreate something that is photographic and notice the beach looks like the beach the clouds still look like clouds and somehow the style of that image has now been applied to this image pretty great thank you Oh wha inserting that French in my mind just made me lose my trip my track we're in trouble okay so deep learning deep learning style transfer now what was possible on tight neck in several minutes is now possible in a few seconds and you can now kind of get a sense for what deep learning can do what the artificial intelligence network is able to do is able to generate an image based on what you teach it now it didn't just learn from these two images it had to learn structure from lots and lots of images it has to understand what are important features from lots and lots and images and after it's done learning all of that you could give it two new images and say I want you to take this structure this image and apply the art the style to this image and it just does it all by itself

Channel: NVIDIA

Views: 17,870

Rating: 4.9159665 out of 5

Keywords: NVIDIA, GTC 2017, GPU Technology Conference, Jensen Huang, AI computing, artificial intelligence, keynote, Tesla V100, Square Enix, demo

Id: MbyEAj2XoII

Length: 11min 54sec (714 seconds)

Published: Thu May 11 2017