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Language: en

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I am a creator

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Blending art and technology

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To immerse our senses

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I am a healer

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Helping us take the next step

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And see what's possible

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I am a pioneer

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Finding life-saving answers

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And pushing the edge to the outer limits.

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I am a guardian

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Defending our oceans

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Magnificent creatures that call them home

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I am a protector

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Helping the earth breathe easier

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And watching over it for generations to come

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I am a storyteller

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Giving emotion to words

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And bringing them to life.

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I am even the composer of the music.

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I am AI

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Brought to life by NVIDIA, deep learning, and brilliant minds everywhere.

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There are powerful forces shaping the world's industries.

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Accelerated computing that we pioneered has supercharged scientific discovery, while providing the computer industry a path forward.

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Artificial intelligence particularly, has seen incredible advances.

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With NVIDIA GPUs computers learn, and software writes software  no human can.

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The AI software is delivered as a service from the cloud, performing automation at the speed-of-light.

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Software is now composed of microservices that scale across the entire data center - treating the data center as a single-unit of computing.

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AI and 5G are the ingredients to kick start the 4th industrial revolution

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where automation and robotics can be deployed to the far edges of the world.

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There is one more miracle we need, the metaverse,  a virtual world that is a digital twin of ours.

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Welcome to GTC 2021 - we are going to talk about these dynamics and more.

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Let me give you the architecture of my talk.

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It's organized in four stacks - this is how we work - as a full-stack computing platform company.

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The flow also reflects the waves of AI and how we're expanding the reach of our platform to solve new problems, and to enter new markets.

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First is Omniverse - built from the ground-up on NVIDIA's body of work. It is a platform to create and simulate virtual worlds.

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We'll feature many applications of Omniverse, like design collaboration, simulation, and future robotic factories.

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The second stack is DGX and high-performance data centers.

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I'll feature BlueField, new DGXs, new chips, and the new work we're doing in AI, drug discovery, and quantum computing.

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Here, I'll also talk about Arm and new Arm partnerships.

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The third stack is one of our most important new platforms - NVIDIA EGX with Aerial 5G.

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Now, enterprises and industries can do AI and deploy AI-on-5G.

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We'll talk about NVIDIA AI and Pre-Trained Models, like Jarvis Conversational AI.

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And finally, our work with the auto industry to revolutionize the future of transportation - NVIDIA Drive.

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We'll talk about new chips, new platforms and software, and lots of new customers.

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Let's get started.

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Scientists, researchers, developers, and creators are using NVIDIA to do amazing things.

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Your work gets global reach with the installed base of over a billion CUDA GPUs shipped and 250 ExaFLOPS of GPU computing power in the cloud.

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Two and a half million developers and 7,500 startups are creating thousands of applications for accelerated computing.

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We are thrilled by the growth of the ecosystem we are building together and will continue to put our heart and soul into advancing it.

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Building tools for the Da Vincis of our time is our purpose. And in doing so, we also help create the future.

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Democratizing high-performance computers is one of NVIDIA's greatest contributions to science.

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With just a GeForce, every student can have a supercomputer.

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This is how Alex Krizhevsky, Ilya, and Hinton trained AlexNet that caught the world's attention on deep learning.

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And with GPUs in supercomputers, we gave scientists a time machine.

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A scientist once told me that because of NVIDIA's work, he can do his life's work in his lifetime.

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I can't think of a greater purpose.

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Let me highlight a few achievements from last year.

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NVIDIA is continually optimizing the full stack.

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With the chips you have, your software runs faster every year and even faster if you upgrade.

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On our gold suite of important science codes, we increased performance 13-fold in the last 5 years, and for some, performance doubled every year.

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NAMD molecular dynamics simulator, for example, was re-architected and can now run across multiple GPUs.

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Researchers led by Dr. Rommie Amaro at UC San Diego, used this multi-GPU NAMD, running on Oak Ridge Summit supercomputer's 20,000 NVIDIA GPUs,

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 to do the largest atomic simulation ever - 305 million atoms.

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This work was critical to a better understanding of the COVID-19 virus and accelerated the making of the vaccine.

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Dr. Amaro and her collaborators won the Gordon Bell Award for this important work.

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I'm very proud to welcome Dr. Amaro and more than 100,000 of you to this year's GTC - our largest-ever by double.

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We have some of the greatest computer scientists and researchers of our time speaking here.

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3 Turing award winners, 12 Gordon Bell award winners,  9 Kaggle Grand Masters - and even 10 Oscar winners.

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We're also delighted to have the brightest minds from industry sharing their discoveries.

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Leaders from every field - healthcare, auto, finance, retail, energy, internet services, every major enterprise IT company.

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They're bringing you their latest work in COVID research, data science, cybersecurity, new approaches to computer graphics

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and the most recent advances in AI and robotics.

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In total, 1,600 talks about the most important technologies of our time, from the leaders in the field that are shaping our world.

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Welcome to GTC.

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Let's start where NVIDIA started...computer graphics.

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Computer graphics is the driving force of our technology. Hundreds of millions of gamers and creators each year seek out the best NVIDIA has to offer.

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At its core, computer graphics is about simulations - using mathematics and computer science to simulate the interactions of light 

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and material, the physics of objects, particles, and waves; and now simulating intelligence and animation.

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The science, engineering, and artistry that we dedicate in pursuit of achieving mother nature's physics has led to incredible advances.

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And allowed our technology to contribute to advancing the basic sciences, the arts, and the industries.

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This last year, we introduced the 2nd generation of RTX - a new rendering approach that fuses rasterization and programmable shading with

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hardware-accelerated ray tracing and artificial intelligence. This is the culmination of ten years of research.

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RTX has reset computer graphics, giving developers a powerful new tool just as rasterization plateaus.

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Let me show you some amazing footage from games in development. The technology and artistry is amazing.

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We¡¯re giving the world¡¯s billion gamers an incredible reason to upgrade.

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RTX is a reset of computer graphics.

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It has enabled us to build Omniverse - a platform for connecting 3D worlds into a shared virtual world.

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Ones not unlike the science fiction metaverse first described by Neal Stephenson in his early 1990s novel, Snow Crash, where the metaverse

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would be collectives of shared 3D spaces and virtually-enhanced physical spaces that are extensions of the internet.

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Pieces of the early-metaverse vision are already here - massive online social games like Fortnite or user-created virtual worlds like Minecraft.

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Let me tell you about Omniverse from the perspective of two applications - design collaboration and digital twins.

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There are several major parts of the platform.

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First, the Omniverse Nucleus, a database engine that connects users

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and enables the interchange of 3D assets and scene descriptions.

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Once connected, designers doing modeling, layout, shading, animation, lighting, special effects or rendering can collaborate to create a scene.

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The Omniverse Nucleus is described with the open standard USD, Universal Scene Description, a fabulous interchange framework invented by Pixar.

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Multiple users can connect to Nucleus, transmitting and receiving changes to their world as USD snippets.

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The 2nd part of Omniverse is the composition, rendering, and animation engine - the simulation of the virtual world.

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Omniverse is a platform built from the ground up to be physically-based. It is fully path-traced. Physics is simulated with NVIDIA PhysX,

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materials are simulated with NVIDIA MDL and Omniverse is fully integrated with NVIDIA AI.

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Omniverse is cloud-native, multi-GPU scalable and runs on any RTX platform and streams remotely to any device.

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The third part is NVIDIA CloudXR, a stargate if you will.

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You can teleport into Omniverse with VR and AIs can teleport out of Omniverse with AR.

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Omniverse was released to open beta in December.

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Let me show you what talented creators are doing.

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Creators are doing amazing things with Omniverse.

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At Foster and Partners, designers in 17 locations around the world, are designing buildings together in their Omniverse shared virtual space.

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ILM is testing Omniverse to bring together internal and external tool pipelines from multiple studios.

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Omniverse lets them collaborate, render final shots in real time and create massive virtual sets like holodecks.

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Ericsson is using Omniverse to do real-time 5G wave propagation simulation, with many multi-path interferences.

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Twin Earth is creating a digital twin of Earth that will run on 20,000 NVIDIA GPUs.

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And Activision is using Omniverse to organize their more than 100,000 3D assets into a shared and searchable world.

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Bentley is the world's leading infrastructure engineering software company.

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Everything that's constructed - roads and bridges, rail and transit systems, airports and seaports -

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about 3% of the world's GDP or three-and-a-half trillion dollars a year.

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Bentley's software is used to design, model, and simulate the largest infrastructure projects in the world.

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90% of the world's top 250 engineering firms use Bentley.

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They have a new platform called iTwin - an exciting strategy to use the 3D model, after construction, to monitor and optimize the performance throughout its life.

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We are super excited to partner with Bentley to create infrastructure digital twins in Omniverse.

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Bentley is the first 3rd-party company to be developing a suite of applications on the Omniverse platform.

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This is just an awesome use of Omniverse, a great example of digital twins and Bentley is the perfect partner.

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And here's Perry Nightingale from WPP, the largest ad agency in the world, to tell you what they're doing.

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WPP is the largest marketing services organization on the planet, and because of that, we're also one of the largest production companies in the world 

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That is a major carbon hotspot for us.

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We've partnered with NVIDIA, to capture locations virtually and bring them to life with studios in Omniverse.

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Over 10 billion points have turned into a giant mesh in Omniverse.

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For the first time, we can shoot locations virtually that are as real as the actual places themselves.

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Omniverse also changes the way we make work.

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A collaborative platform, that means multiple artists, at multiple points in the pipeline, in multiple parts of the world can collaborate on a single scene.

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Real time CGI in sustainable studios. Collaboration with Omniverse is the future of film at WPP.

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One of the most important features of Omniverse is that it obeys the laws of physics.

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Omniverse can simulate particles, fluids, materials, springs and cables.

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This is a fundamental capability for robotics.

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Once trained, the AI and software can be downloaded from Omniverse.

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In this video, you'll see Omniverse's physics simulation with rigid and soft bodies, fluids, and finite element modeling.

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And a lot more - enjoy!

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Omniverse is a physically-based virtual world where robots can learn to be robots.

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They'll come in all sizes and shapes - box movers, pick and place arms, forklifts, cars, trucks.

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In the future, a factory will be a robot, orchestrating many robots inside, building cars that are robots themselves.

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We can use Omniverse to create a virtual factory, train and simulate the factory and its robotic workers inside.

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The AI and software that run the virtual factory are exactly the same as what will run the actual one.

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The virtual and physical factories and their robots will operate in a loop.

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They are digital twins.

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Connecting to ERP systems, simulating the throughput of the factory,

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simulating new plant layouts, and becoming the dashboard of the operator - even uplinking into a robot to teleoperate it.

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BMW may very well be the world's largest custom-manufacturing company.

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BMW produces over 2 million cars a year. In their most advanced factory, a car a minute. Every car is different.

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We are working with BMW to create a future factory. Designed completely in digital, simulated from beginning to end in Omniverse, creating a

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digital twin, and operating a factory where robots and humans work together.

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Let's take a look at the BMW factory.

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Welcome to BMW Production, Jensen. I am pleased to show you why BMW sets the standards for innovation and flexibility. Our collaboration

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with NVIDIA Omniverse and NVIDIA AI leads into a new era of digitalization of automobile production.

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Fantastic to be with you, Milan. I am excited to do this virtual factory visit with you.

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We are inside the digital twin of BMW's assembly system, powered by Omniverse. For the first time, we are able to have our entire factory in

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simulation. Global teams can collaborate using different software packages like Revit, CATIA, or point clouds to design and plan the factory

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in real time 3D. The capability to operate in a perfect simulation revolutionizes BMW's planning processes.

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BMW regularly reconfigures its factories to accommodate new vehicle launches. Here we see 2 planning experts located in different parts of

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the world, testing a new line design in Omniverse. One of them wormholes into an assembly simulation with a motion capture suit, records

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task movements, while the other expert adjusts the line design - in real time.

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They work together to optimize the line as well as worker ergonomics and safety.

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¡°Can you tell how far I have to bend down there?¡±

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¡°First, I¡¯ll get you a taller one¡±

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¡°Yeah, it¡¯s perfect¡±

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We would like to be able to do this at scale in simulation.

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That's exactly why NVIDIA has Digital Human for simulation.

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Digital Humans are trained with data from real associates.

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You can then use Digital Humans in simulation to test new workflows for worker ergonomics and efficiency.

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Now, your factories employ 57,000 people that share workspace with many robots designed to make their jobs easier.

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Let's talk about them.

00:35:42.807 --> 00:35:43.791
You are right, Jensen.

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Robots are crucial for a modern production system. With NVIDIA Isaac robotics platform, BMW is deploying a fleet of

00:35:52.483 --> 00:35:57.506
intelligent robots for logistics to improve the material flow in our production.

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This agility is necessary since we produce 2.5 million vehicles per year, 99% of them are custom.

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Synthetic data generation and domain randomization available in Isaac are key to bootstrapping machine learning.

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Isaac Sim generates millions of synthetic images and vary the environment to teach the robots.

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Domain randomization can generate an infinite permutation of photorealistic objects, textures, orientations and lighting conditions.

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It is ideal for generating ground truth, whether for detection, segmentation, or depth perception.

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Let me show you an example of how we can combine it all to operate your factory.

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With NVIDIA's Fleet Command, your associates can securely orchestrate robots and other devices in the factory from Mission Control.

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They could monitor in real time complex manufacturing cells, update software over the air, launch robot missions and teleoperate.

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When a robot needs a helping hand, an alert can be sent to Mission Control and one of your associates can take control to help the robot.

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We're in the digital twin of one of your factories, but you have 30 others spread across 15 countries.

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The scale of BMW production is impressive, Milan.

00:37:20.972 --> 00:37:29.044
Indeed, Jensen, the scale and complexity of our production network requires BMW to constantly innovate.

00:37:29.044 --> 00:37:34.159
I am happy about the tight collaboration between our two companies.

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NVIDIA Omniverse and NVIDIA AI give us the chance to simulate all 31 factories in our production network.

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These new innovations will reduce the planning times, improve flexibility and precision and at the end, produce 30% more efficient planning processes.

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Milan, I could not be more proud of the innovations that our collaboration is bringing to the Factories of the Future.

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I appreciate you hosting me for a virtual visit of the digital twin of your BMW Production.

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It is a work of art!

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The ecosystem is really excited about Omniverse.

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This open platform, with USD universal 3D interchange, connects them into a large network of users.

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We have 12 connectors to major design tools already with another 40 in flight.

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Omniverse Connector SDK is available for download now.

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You can see that the most important design tools are already signed-up.

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Our lighthouse partners are from some of the world's largest industries - Media and Entertainment, Gaming, Architecture, Engineering, and

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Construction; Manufacturing, Telecommunications, Infrastructure, and Automotive.

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Computer makers worldwide are building NVIDIA-Certified workstations, notebooks, and servers optimized for Omniverse.

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And starting this summer, Omniverse will be available for enterprise license.

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Omniverse - NVIDIA's platform for creating and simulating shared virtual worlds.

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Data center is the new unit of computing.

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Cloud computing and AI are driving fundamental changes in the architecture of data centers.

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Traditionally, enterprise data centers ran monolithic software packages.

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Virtualization started the trend toward software-defined data centers - allowing applications to move about and letting IT manage from a "single-pane of glass".

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With virtualization, the compute, networking, storage, and security functions are emulated in software running on the CPU.

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Though easier to manage, the added CPU load reduced the data center's capacity to run applications, which is its primary purpose.

00:39:47.251 --> 00:39:51.522
This illustration shows the added CPU load in the gold-colored part of the stack.

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Cloud computing re-architected data centers again, now to provision services for billions of consumers.

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Monolithic applications were disaggregated into smaller microservices that can take advantage of any idle resource.

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Equally important, multiple engineering teams can work concurrently using CI/CD methods.

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Data center networks became swamped by east-west traffic generated by disaggregated microservices.

00:40:18.382 --> 00:40:22.119
CSPs tackled this with Mellanox's high-speed low-latency networking.

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Then, deep learning emerged. Magical internet services were rolled out, attracting more customers, and better engagement than ever.

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Deep learning is compute-intensive which drove adoption of GPUs.

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Nearly overnight, consumer AI services became the biggest users of GPU supercomputing technologies.

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Now, adding Zero-Trust security initiatives makes infrastructure software processing one of the largest workloads in the data center.

00:40:53.083 --> 00:41:00.090
The answer is a new type of chip for Data Center Infrastructure Processing like NVIDIA's Bluefield DPU.

00:41:00.090 --> 00:41:04.028
Let me illustrate this with our own cloud gaming service, GeForce Now, as an example.

00:41:04.428 --> 00:41:07.598
GeForce Now is NVIDIA's GeForce-in-the-cloud service.

00:41:08.232 --> 00:41:15.172
GeForce Now serves 10 million members in 70 countries. Incredible growth.

00:41:15.706 --> 00:41:19.417
GeForce Now is a seriously hard consumer service to deliver.

00:41:19.417 --> 00:41:32.475
Everything matters - speed of light, visual quality, frame rate, response, smoothness, start-up time, server cost, and most important of all, security.

00:41:33.390 --> 00:41:35.259
We're transitioning GeForce Now to BlueField.

00:41:35.893 --> 00:41:43.300
With Bluefield, we can isolate the infrastructure from the game instances, and offload and accelerate the networking, storage, and security.

00:41:44.068 --> 00:41:52.176
The GeForce Now infrastructure is costly. With BlueField, we will improve our quality of service and concurrent users at the same time - the

00:41:52.343 --> 00:41:53.944
ROI of BlueField is excellent.

00:41:54.712 --> 00:42:00.184
I'm thrilled to announce our first data center infrastructure SDK - DOCA 1.0 is available today!

00:42:00.818 --> 00:42:02.920
DOCA is our SDK to program BlueField.

00:42:03.587 --> 00:42:05.489
There's all kinds of great technology inside.

00:42:05.890 --> 00:42:15.375
Deep packet inspection, secure boot, TLS crypto offload, RegEX acceleration, and a very exciting capability - a hardware-based,

00:42:15.375 --> 00:42:20.813
real-time clock that can be used for synchronous data centers, 5G, and video broadcast.

00:42:21.705 --> 00:42:25.175
We have great partners working with us to optimize leading platforms on BlueField:

00:42:25.709 --> 00:42:34.752
Infrastructure software providers, edge and CDN providers, cybersecurity solutions, and storage providers - basically the world's leading

00:42:34.752 --> 00:42:36.754
companies in data center infrastructure.

00:42:37.421 --> 00:42:42.793
Though we're just getting started with BlueField 2, today we're announcing BlueField 3.

00:42:42.793 --> 00:42:44.461
22 billion transistors.

00:42:44.929 --> 00:42:47.698
The first 400 Gbps networking chip.

00:42:48.165 --> 00:42:54.638
16 Arm CPUs to run the entire virtualization software stack - for instance, running VMware ESX.

00:42:55.005 --> 00:43:03.614
BlueField 3 takes security to a whole new level, fully offloading and accelerating IPSEC and TLS cryptography, secret key management, and

00:43:03.614 --> 00:43:05.049
regular expression processing.

00:43:05.783 --> 00:43:09.253
We are on a pace to introduce a new Bluefield generation every 18 months.

00:43:10.120 --> 00:43:16.093
BlueField 3 will do 400 Gbps and be 10x the processing capability of BlueField 2.

00:43:17.161 --> 00:43:25.436
And BlueField 4 will do 800 Gbps and add NVIDIA's AI computing technologies to get another 10x boost.

00:43:26.236 --> 00:43:30.240
100x in 3 years -- and all of it will be needed.

00:43:31.175 --> 00:43:39.750
A simple way to think about this is that 1/3rd of the roughly 30 million data center servers shipped each year are consumed running the

00:43:39.750 --> 00:43:42.286
software-defined data center stack.

00:43:43.020 --> 00:43:46.090
This workload is increasing much faster than Moore's law.

00:43:46.690 --> 00:43:52.563
So, unless we offload and accelerate this workload, data centers will have fewer and fewer CPUs to run applications.

00:43:52.930 --> 00:43:54.264
The time for BlueField has come.

00:43:55.065 --> 00:44:04.241
At the beginning of the big bang of modern AI, we recognized the need to create a new kind of computer for a new way of developing software.

00:44:04.642 --> 00:44:08.545
Software will be written by software running on AI computers.

00:44:09.613 --> 00:44:19.723
This new type of computer will need new chips, new system architecture, new ways to network, new software, and new methodologies and tools.

00:44:20.524 --> 00:44:24.995
We've invested billions into this intuition, and it has proven helpful to the industry.

00:44:25.763 --> 00:44:29.767
It all comes together as DGX - a computer for AI.

00:44:30.234 --> 00:44:37.374
We offer DGX as a fully integrated system, as well as offer the components to the industry to create differentiated options.

00:44:37.841 --> 00:44:41.286
I am pleased to see so much AI research advancing because of DGX -

00:44:41.286 --> 00:44:48.997
top universities, research hospitals, telcos, banks, consumer products companies, car makers, and aerospace companies.

00:44:49.787 --> 00:44:56.319
DGX help their AI researchers - whose expertise is rare, scarce, and their work strategic.

00:44:56.319 --> 00:44:58.862
It is imperative to make sure they have the right instrument.

00:44:59.663 --> 00:45:06.570
Simply, if software is to be written by computers, then companies with the best software engineers will also need the best computers.

00:45:07.204 --> 00:45:10.441
We offer several configurations - all software compatible.

00:45:10.908 --> 00:45:18.849
The DGX A100 is a building block that contains 5 petaFLOPS of computing and superfast storage and networking to feed it.

00:45:19.316 --> 00:45:25.889
DGX Station is an AI data center in-a-box designed for workgroups - plugs into a normal outlet.

00:45:26.423 --> 00:45:35.634
And DGX SuperPOD is a fully integrated, fully network-optimized, AI-data-center-as-a-product. SuperPOD is for intensive AI research and development.

00:45:36.734 --> 00:45:41.083
NVIDIA's own new supercomputer, called Selene, is 4 SuperPODs.

00:45:41.083 --> 00:45:46.943
It is the 5th fastest supercomputer and the fastest industrial supercomputer in the world's Top 500.

00:45:47.745 --> 00:45:50.914
We have a new DGX Station 320G.

00:45:51.482 --> 00:46:04.591
DGX Station can train large models - 320 gigabytes of super-fast HBM2e connected to 4 A100 GPUs over 8 terabytes per second of memory bandwidth.

00:46:04.762 --> 00:46:07.998
8 terabytes transferred in one second.

00:46:08.332 --> 00:46:12.236
It would take 40 CPU servers to achieve this memory bandwidth.

00:46:13.303 --> 00:46:23.614
DGX Station plugs into a normal wall outlet, like a big gaming rig, consumes just 1,500 watts, and is liquid-cooled to a silent 37 db.

00:46:24.348 --> 00:46:27.284
Take a look at the cinematic that our engineers and creative team did.

00:47:30.814 --> 00:47:34.284
A CPU cluster of this performance would cost about a million dollars today.

00:47:34.818 --> 00:47:41.959
DGX Station is $149,000 - the ideal AI programming companion for every AI researcher.

00:47:42.659 --> 00:47:45.729
Today we are also announcing a new DGX SuperPOD.

00:47:46.063 --> 00:47:47.464
Three major upgrades:

00:47:48.098 --> 00:47:59.710
The new 80 gigabyte A100 which brings the SuperPOD to 90 terabytes of HBM2 memory, with aggregate bandwidth of 2.2 exabytes per second.

00:48:00.744 --> 00:48:10.921
It would take 11,000 CPU servers to achieve this bandwidth - about a 250-rack data center - 15 times bigger than the SuperPOD.

00:48:12.389 --> 00:48:15.993
Second, SuperPOD has been upgraded with NVIDIA BlueField-2.

00:48:16.727 --> 00:48:25.903
SuperPOD is now the world's first cloud-native supercomputer, multi-tenant shareable, with full isolation and bare-metal performance.

00:48:26.937 --> 00:48:33.610
And third, we're offering Base Command, the DGX management and orchestration tool used within NVIDIA.

00:48:34.845 --> 00:48:42.519
We use Base Command to support thousands of engineers, over two hundred teams, consuming a million-plus GPU-hours a week.

00:48:43.420 --> 00:48:48.258
DGX SuperPOD starts at seven million dollars and scales to sixty million dollars for a full system.

00:48:49.693 --> 00:48:51.862
Let me highlight 3 great uses of DGX.

00:48:52.562 --> 00:48:56.033
Transformers have led to dramatic breakthroughs in Natural Language Processing.

00:48:56.867 --> 00:49:02.539
Like RNN and LSTM, Transformers are designed to inference on sequential data.

00:49:03.173 --> 00:49:12.816
However, Transformers, more than meets the eyes, are not trained sequentially, but use a mechanism called attention such that Transformers

00:49:12.816 --> 00:49:14.117
can be trained in parallel.

00:49:14.751 --> 00:49:22.781
This breakthrough reduced training time, which more importantly enabled the training of huge models with a correspondingly enormous amount of data.

00:49:23.560 --> 00:49:27.864
Unsupervised learning can now achieve excellent results, but the models are huge.

00:49:28.665 --> 00:49:32.002
Google's Transformer was 65 million parameters.

00:49:32.869 --> 00:49:38.275
OpenAI's GPT-3 is 175 billion parameters.

00:49:38.742 --> 00:49:41.845
That's 3000x times larger in just 3 years.

00:49:42.579 --> 00:49:45.182
The applications for GPT-3 are really incredible.

00:49:45.649 --> 00:49:47.150
Generate document summaries.

00:49:47.718 --> 00:49:49.319
Email phrase completion.

00:49:50.120 --> 00:49:57.461
GPT-3 can even generate Javascript and HTML from plain English - essentially telling an AI to write code based on what you want it to do.

00:49:58.528 --> 00:50:03.734
Model sizes are growing exponentially - on a pace of doubling every two and half months.

00:50:04.534 --> 00:50:11.341
We expect to see multi-trillion-parameter models by next year, and 100 trillion+ parameter models by 2023.

00:50:13.110 --> 00:50:19.082
As a very loose comparison, the human brain has roughly 125 trillion synapses.

00:50:20.250 --> 00:50:22.252
 So these transformer models are getting quite large. 

00:50:22.986 --> 00:50:26.456
Training models of this scale is incredible computer science.

00:50:27.324 --> 00:50:32.996
Today, we are announcing NVIDIA Megatron - for training Transformers.

00:50:34.164 --> 00:50:41.505
Megatron trains giant Transformer models - it partitions and distributes the model for optimal multi-GPU and multi-node parallelism.

00:50:42.406 --> 00:50:51.481
Megatron does fast data loading, micro batching, scheduling and syncing, kernel fusing. It pushes the limits of every NVIDIA invention -

00:50:51.648 --> 00:50:56.186
NCCL, NVLink, Infiniband, Tensor Cores.

00:50:57.154 --> 00:51:01.691
Even with Megatron, a trillion-parameter model will take about 3-4 months to train on Selene.

00:51:02.659 --> 00:51:05.862
So, lots of DGX SuperPODS will be needed around the world

00:51:06.563 --> 00:51:10.834
Inferencing giant Transformer models is also a great computer science challenge.

00:51:11.234 --> 00:51:22.476
GPT-3 is so big, with so many floating-point operations, that it would take a dual-CPU server over a minute to respond to a single 128-word query.

00:51:23.713 --> 00:51:29.319
And GPT-3 is so large that it doesn't fit in GPU memory - so it will have to be distributed.

00:51:30.187 --> 00:51:33.423
multi-GPU multi-node inference has never been done.

00:51:34.257 --> 00:51:39.563
Today, we're announcing the Megatron Triton Inference Server.

00:51:40.764 --> 00:51:50.273
A DGX with Megatron Triton will respond within a second! Not a minute - a second! And for 16 queries at the same time.

00:51:51.174 --> 00:51:59.649
DGX is 1000 times faster and opens up many new use-cases, like call-center support, where a one-minute response is effectively unusable.

00:52:01.118 --> 00:52:03.019
Naver is Korea's #1 search engine.

00:52:03.920 --> 00:52:10.360
They installed a DGX SuperPOD and are running their AI platform CLOVA to train language models for Korean.

00:52:11.261 --> 00:52:15.275
I expect many leading service providers around the world to do the same

00:52:15.275 --> 00:52:20.455
Use DGX to develop and operate region-specific and industry-specific language services.

00:52:22.339 --> 00:52:31.114
NVIDIA Clara Discovery is our suite of acceleration libraries created for computational drug discovery - from imaging, to quantum chemistry,

00:52:31.748 --> 00:52:39.923
to gene variant-calling, to using NLP to understand genetics, and using AI to generate new drug compounds.

00:52:41.124 --> 00:52:44.528
Today we're announcing four new models available in Clara Discovery:

00:52:45.495 --> 00:52:49.831
MegaMolBART is a model for generating biomolecular compounds.

00:52:49.831 --> 00:52:56.617
This method has seen recent success with Insilico Medicine using AI to find a new drug in less than two years.

00:52:57.073 --> 00:53:05.549
NVIDIA ATAC-seq denoising algorithm for rare and single cell epi-genomics is helping to understand gene expression for individual cells.

00:53:06.516 --> 00:53:12.689
AlphaFold1 is a model that can predict the 3D structure of a protein from the amino acid sequence.

00:53:12.689 --> 00:53:18.895
GatorTron is the world's largest clinical language model that can read and understand doctors' notes.

00:53:18.895 --> 00:53:26.468
GatorTron was developed at UoF, using Megatron, and trained on the DGX SuperPOD gifted

00:53:26.468 --> 00:53:29.877
to his alma mater by Chris Malachowsky, who founded NVIDIA with Curtis and me.

00:53:31.041 --> 00:53:39.082
Oxford Nanopore is the 3rd generation genomics sequencing technology capable of ultra high throughput in digitizing biology -

00:53:39.082 --> 00:53:46.356
1/5 of the SARS-CoV-2 virus genomes in the global database were generated on Oxford Nanopore.

00:53:46.356 --> 00:53:52.596
Last year, Oxford Nanopore developed a diagnostic test for COVID-19 called LamPORE, which is used by NHS.

00:53:53.463 --> 00:53:55.865
Oxford Nanopore is GPU-accelerated throughout.

00:53:56.766 --> 00:54:06.539
DNA samples pass through nanopores and the current signal is fed into an AI model, like speech recognition, but trained to recognize genetic code. 

00:54:07.577 --> 00:54:11.648
Another model called Medaka reads the code and detects genetic variants.

00:54:12.015 --> 00:54:14.784
Both models were trained on DGX SuperPOD.

00:54:15.352 --> 00:54:24.327
These new deep learning algorithms achieve 99.9% detection accuracy of single nucleotide variants - this is the gold standard of human sequencing.

00:54:24.861 --> 00:54:32.602
Pharma is a 1.3 trillion-dollar industry where a new drug can take 10+ years and fails 90% of the time.

00:54:33.770 --> 00:54:40.443
Schrodinger is the leading physics-based and machine learning computational platform for drug discovery and material science.

00:54:40.844 --> 00:54:50.808
Schrodinger is already a heavy user of NVIDIA GPUs, recently entering into an agreement to use hundreds of millions of NVIDIA GPU hours on the Google cloud.

00:54:51.554 --> 00:54:59.996
Some customers can't use the cloud, so today we are announcing a partnership to accelerate Schrodinger's drug discovery workflow with NVIDIA

00:55:00.063 --> 00:55:04.301
Clara Discovery libraries and NVIDIA DGX.

00:55:04.301 --> 00:55:10.740
The world's top 20 pharmas use Schrodinger today. Their researchers are going to see a giant boost in productivity.

00:55:11.574 --> 00:55:19.182
Recursion is a biotech company using leading-edge computer science to decode biology to industrialize drug discovery.

00:55:19.949 --> 00:55:30.506
The Recursion Operating System is built on NVIDIA DGX SuperPOD for generating, analyzing and gaining insight from massive biological and chemical datasets.

00:55:31.561 --> 00:55:37.767
They call their SuperPOD the BioHive-1 - it's the most powerful computer at any pharma today.

00:55:38.702 --> 00:55:46.409
Using deep learning on DGX, Recursion is classifying cell responses after exposure to small molecule drugs.

00:55:47.277 --> 00:55:56.226
Quantum computing is a field of physics that studies the use of natural quantum behavior - superposition, entanglement, and interference - to build a computer.

00:55:56.853 --> 00:56:01.491
The computation is performed using quantum circuits that operate on quantum bits - called qubits.

00:56:02.492 --> 00:56:09.866
Qubits can be 0 or 1, like a classical computing bit, but also in superposition - meaning they exist simultaneously in both states.

00:56:10.533 --> 00:56:15.672
The qubits can be entangled where the behavior of one can affect or control the behavior of others.

00:56:16.206 --> 00:56:22.078
Adding and entangling more qubits lets quantum computers calculate exponentially more information.

00:56:22.812 --> 00:56:26.883
There is a large community around the world doing research in quantum computers and algorithms.

00:56:27.550 --> 00:56:32.689
Well over 50 teams in industry, academia, and national labs are researching the field.

00:56:33.390 --> 00:56:34.624
We're working with many of them.

00:56:35.325 --> 00:56:44.067
Quantum computing can solve Exponential Order Complexity problems, like factoring large numbers for cryptography, simulating atoms and

00:56:44.067 --> 00:56:49.806
molecules for drug discovery, finding shortest path optimizations, like the traveling salesman problem,

00:56:50.507 --> 00:56:57.647
The limiter in quantum computing is decoherence, falling out of quantum states, caused by the tiniest of background noise

00:56:58.081 --> 00:56:59.649
So error correction is essential.

00:57:00.483 --> 00:57:07.323
It is estimated that to solve meaningful problems, several million physical qubits will be required to sufficiently error correct.

00:57:07.957 --> 00:57:17.357
The research community is making fast progress, doubling physical Qubits each year, so likely achieving the milestone by 2035 to 2040.

00:57:17.357 --> 00:57:19.759
Well within my career horizon.

00:57:20.036 --> 00:57:26.843
In the meantime, our mission is to help the community research the computer of tomorrow with the fastest computer of today.

00:57:27.777 --> 00:57:34.117
Today, we're announcing cuQuantum - an acceleration library designed for simulating quantum circuits

00:57:34.484 --> 00:57:37.520
for both Tensor Network Solvers and State Vector Solvers.

00:57:38.788 --> 00:57:44.260
It is optimized to scale to large GPU memories, multiple GPUs, and multiple DGX nodes.

00:57:45.428 --> 00:57:54.571
The speed-up of cuQuantum on DGX is excellent. Running the cuQuantum Benchmark, state vector simulation takes 10 days on a dual-CPU server

00:57:55.271 --> 00:58:02.979
but only 2 hours on a DGX A100. cuQuantum on DGX can productively simulate 10's of qubits.

00:58:04.481 --> 00:58:15.792
And Caltech, using Contengra/Quimb, simulated the Sycamore quantum circuit at depth 20 in record time using cuQuantum on NVIDIA's Selene supercomputer.

00:58:16.392 --> 00:58:23.299
 What would have taken years on CPUs can now run in a few days on cuQuantum and DGX.

00:58:24.167 --> 00:58:31.808
cuQuantum will accelerate quantum circuit simulators so researchers can design better quantum computers and verify their results, architect

00:58:31.808 --> 00:58:38.348
hybrid quantum-classical systems -and discover more Quantum-Optimal algorithms like Shor's and Grover's.

00:58:39.115 --> 00:58:43.853
cuQuantum on DGX is going to give the quantum community a huge boost.

00:58:44.554 --> 00:58:49.659
I'm hoping cuQuantum will do for quantum computing what cuDNN did for deep learning.

00:58:50.493 --> 00:58:54.964
Modern data centers host diverse applications that require varying system architectures.

00:58:55.532 --> 00:59:01.337
Enterprise servers are optimized for a balance of strong single-threaded performance and a nominal number of cores.

00:59:01.905 --> 00:59:10.046
Hyperscale servers, optimized for microservice containers, are designed for a high number of cores, low cost, and great energy-efficiency.

00:59:10.680 --> 00:59:14.918
Storage servers are optimized for large number of cores and high IO throughput.

00:59:15.285 --> 00:59:22.592
Deep learning training servers are built like supercomputers - with the largest number of fast CPU cores, the fastest memory, 

00:59:22.592 --> 00:59:26.796
the fastest IO, and high-speed links to connect the GPUs.

00:59:26.996 --> 00:59:33.670
Deep learning inference servers are optimized for energy-efficiency and best ability to process a large number of models concurrently.

00:59:34.437 --> 00:59:45.448
The genius of the x86 server architecture is the ability to do a good job using varying configurations of the CPU, memory, PCI express, and

00:59:45.448 --> 00:59:48.384
peripherals to serve all of these applications.

00:59:49.218 --> 00:59:56.793
Yet processing large amounts of data remains a challenge for computer systems today - this is particularly true for AI models like

00:59:56.793 --> 00:59:58.595
transformers and recommender systems.

00:59:59.228 --> 01:00:03.099
Let me illustrate the bottleneck with half of a DGX.

01:00:03.733 --> 01:00:10.440
Each Ampere GPU is connected to 80GB of super fast memory running at 2 TB/sec.

01:00:11.374 --> 01:00:18.715
Together, the 4 Amperes process 320 GB at 8 Terabytes per second.

01:00:19.382 --> 01:00:27.308
Contrast that with CPU memory, which is 1TB large, but only 0.2 Terabytes per second.

01:00:27.308 --> 01:00:32.178
The CPU memory is 3 times larger but 40 times slower than the GPU.

01:00:33.029 --> 01:00:39.102
We would love to utilize the full 1,320 GB of memory of this node to train AI models.

01:00:39.869 --> 01:00:41.404
So, why not something like this?

01:00:42.171 --> 01:00:49.379
Make faster CPU memories, connect 4 channels to the CPU, a dedicated channel to feed each GPU.

01:00:49.846 --> 01:00:53.750
Even if a package can be made, PCIe is now the bottleneck.

01:00:54.450 --> 01:01:02.258
We can surely use NVLINK. NVLINK is fast enough. But no x86 CPU has NVLINK, not to mention 4 NVLINKS.

01:01:03.593 --> 01:01:13.469
Today, we're announcing our first data center CPU, Project Grace, named after Grace Hopper, a computer scientist and U.S. Navy rear Admiral,

01:01:13.903 --> 01:01:16.105
who in the '50s pioneered computer programming.

01:01:17.006 --> 01:01:23.646
Grace is Arm-based and purpose-built for accelerated computing applications of large amounts of data - such as AI.

01:01:24.380 --> 01:01:26.358
Grace highlights the beauty of Arm.

01:01:26.358 --> 01:01:33.527
Their IP model allowed us to create the optimal CPU for this application which achieves x-factors speed-up.

01:01:34.323 --> 01:01:45.568
The Arm core in Grace is a next generation off-the-shelf IP for servers. Each CPU will deliver over 300 SpecInt with a total of over 2,400

01:01:45.568 --> 01:01:51.774
SPECint_rate CPU performance for an 8-GPU DGX.

01:01:52.275 --> 01:02:00.149
For comparison, todays DGX, the highest performance computer in the world today is 450 SPECint_rate.

01:02:00.883 --> 01:02:07.256
2400 SPECint_rate with Grace versus 450 SPECint_rate today.

01:02:07.890 --> 01:02:17.133
So look at this again - Before, After, Before, After.

01:02:18.367 --> 01:02:21.604
Amazing increase in system and memory bandwidth.

01:02:22.538 --> 01:02:25.174
Today, we're introducing a new kind of computer.

01:02:26.075 --> 01:02:28.578
The basic building block of the modern data center.

01:02:29.479 --> 01:02:30.146
Here it is.

01:02:42.391 --> 01:02:50.688
What I'm about to show you brings together the latest GPU accelerated computing, Mellanox high performance networking, and something brand new.

01:02:51.267 --> 01:02:52.935
The final piece of the puzzle.

01:02:59.142 --> 01:03:07.416
The world's first CPU designed for terabyte-scale accelerated computing... her secret codename - GRACE.

01:03:09.385 --> 01:03:19.310
This powerful, Arm-based CPU gives us the third foundational technology for computing, and the ability to rearchitect every aspect of the data center for AI.

01:03:20.663 --> 01:03:27.804
We're thrilled to announce the Swiss National Supercomputing Center will build a supercomputer powered by Grace and our next generation GPU.

01:03:28.504 --> 01:03:39.582
This new supercomputer, called Alps, will be 20 exaflops for AI, 10 times faster than the world's fastest supercomputer today.

01:03:40.583 --> 01:03:48.100
Alps will be used to do whole-earth-scale weather and climate simulation, quantum chemistry and quantum physics for the Large Hadron Collider.

01:03:48.925 --> 01:03:54.730
Alps will be built by HPE and is come on-line in 2023.

01:03:54.730 --> 01:04:02.605
We're thrilled by the enthusiasm of the supercomputing community, welcoming us to make Arm a top-notch scientific computing platform.

01:04:03.372 --> 01:04:11.214
Our data center roadmap is now a rhythm consisting of 3-chips: CPU, GPU, and DPU.

01:04:12.114 --> 01:04:16.986
Each chip architecture has a two-year rhythm with likely a kicker in between.

01:04:17.954 --> 01:04:20.590
One year will focus on x86 platforms.

01:04:21.023 --> 01:04:23.125
One year will focus on Arm platforms.

01:04:23.826 --> 01:04:26.662
Every year will see new exciting products from us.

01:04:27.396 --> 01:04:34.403
The NVIDIA architecture and platforms will support x86 and Arm - whatever customers and markets prefer.

01:04:35.338 --> 01:04:39.075
Three chips. Yearly Leaps. One Architecture.

01:04:39.976 --> 01:04:42.311
Arm is the most popular CPU in the world.

01:04:43.246 --> 01:04:51.921
For good reason - its super energy-efficient. Its open licensing model inspires a world of innovators to create products around it.

01:04:52.955 --> 01:04:55.391
Arm is used broadly in mobile and embedded today.

01:04:56.392 --> 01:05:03.018
For other markets like the cloud, enterprise and edge data centers, supercomputing and PCs.

01:05:03.018 --> 01:05:07.096
Arm is just starting and has great growth opportunities.

01:05:07.637 --> 01:05:14.110
Each market has different applications and has unique systems, software, peripherals, and ecosystems.

01:05:15.011 --> 01:05:18.347
For the markets we serve, we can accelerate Arm's adoption.

01:05:19.248 --> 01:05:20.950
Let's start with the big one - Cloud.

01:05:21.951 --> 01:05:29.558
One of the earliest designers of Arm CPUs for data centers is AWS - its Graviton CPUs are extremely impressive.

01:05:30.259 --> 01:05:37.767
Today, we're announcing NVIDIA and AWS are partnering to bring Graviton2 and NVIDIA GPUs together.

01:05:38.668 --> 01:05:44.774
This partnership brings Arm into the most demanding cloud workloads - AI and cloud gaming.

01:05:45.408 --> 01:05:50.079
Mobile gaming is growing fast and is the primary form of gaming in some markets.

01:05:50.813 --> 01:05:58.621
With AWS-designed Graviton2, users can stream Arm-based applications and Android games straight from AWS.

01:05:59.422 --> 01:06:00.656
It's expected later this year.

01:06:01.524 --> 01:06:08.197
We are announcing a partnership with Ampere Computing to create a scientific and cloud computing SDK and reference system.

01:06:09.265 --> 01:06:19.141
Ampere Computing's Altra CPU is excellent - 80 cores, 285 SPECint17, right up there with the highest performance x86.

01:06:20.176 --> 01:06:25.581
We are seeing excellent reception at supercomputing centers around the world and at Android cloud gaming services.

01:06:26.282 --> 01:06:33.689
We are also announcing a partnership with Marvell to create an edge and enterprise computing SDK and reference system.

01:06:34.623 --> 01:06:42.765
Marvell Octeon excels at IO, storage and 5G processing. This system is ideal for hyperconverged edge servers.

01:06:44.066 --> 01:06:50.439
We're announcing a partnership with Mediatek to create a reference system and SDK for Chrome OS and Linux PC's.

01:06:51.140 --> 01:06:54.777
Mediatek is the world's largest SOC maker.

01:06:55.611 --> 01:07:01.250
Combining NVIDIA GPUs and Mediatek SOCs will make excellent PCs and notebooks.

01:07:02.485 --> 01:07:09.492
AI, computers automating intelligence, is the most powerful technology force of our time.

01:07:10.192 --> 01:07:11.927
We see AI in four waves.

01:07:12.261 --> 01:07:20.202
The first wave was to reinvent computing for this new way of doing software - we're all in and have been driving this for nearly 10 years.

01:07:20.903 --> 01:07:27.810
The first adopters of AI were the internet companies - they have excellent computer scientists, large computing infrastructures, and the

01:07:27.810 --> 01:07:29.712
ability to collect a lot of training data.

01:07:30.880 --> 01:07:32.915
We are now at the beginning of the next wave.

01:07:33.716 --> 01:07:42.691
The next wave is enterprise and the industrial edge, where AI can revolutionize the world's largest industries. From manufacturing,

01:07:42.792 --> 01:07:47.797
logistics, agriculture, healthcare, financial services, and transportation.

01:07:48.531 --> 01:07:53.869
There are many challenges to overcome, one of which is connectivity, which 5G will solve.

01:07:54.503 --> 01:08:01.510
And then autonomous systems. Self-driving cars are an excellent example. But everything that moves will eventually be autonomous.

01:08:02.244 --> 01:08:09.518
The industrial edge and autonomous systems are the most challenging, but also the largest opportunities for AI to make an impact.

01:08:10.152 --> 01:08:17.560
Trillion dollar industries can soon apply AI to improve productivity, and invent new products, services and business models.

01:08:18.761 --> 01:08:24.800
We have to make AI easier to use - turn AI from computer science to computer products.

01:08:25.201 --> 01:08:32.842
We're building the new computing platform for this fundamentally new software approach - the computer for the age of AI.

01:08:33.676 --> 01:08:47.103
AI is not just about an algorithm - building and operating AI is a fundamental change in every aspect of software - Andrej Karpathy rightly called it Software 2.0.

01:08:47.123 --> 01:08:54.663
Machine learning, at the highest level, is a continuous learning system that starts with data scientists developing data strategies 

01:08:54.663 --> 01:09:00.636
and engineering predictive features - this data is the digital life experience of a company.

01:09:01.470 --> 01:09:06.675
Training involves inventing or adapting an AI model that learns to make the desired predictions.

01:09:07.543 --> 01:09:14.049
Simulation and validation test the AI application for accuracy, generalization, and potential bias.

01:09:14.550 --> 01:09:22.165
And finally, orchestrating a fleet of computers, whether in your data center or at the edge in the warehouse, farms, or wireless base stations.

01:09:22.191 --> 01:09:31.233
NVIDIA created the chips, systems, and libraries needed for end-to-end machine learning - for example, technologies like Tensor Core GPUs,

01:09:31.333 --> 01:09:36.972
NVLINK, DGX, cuDNN, RAPIDS, NCCL, GPU Direct, DOCA, and so much more.

01:09:37.673 --> 01:09:39.642
We call the platform NVIDIA AI.

01:09:40.709 --> 01:09:46.649
NVIDIA AI libraries accelerate every step, from data processing to fleet orchestration.

01:09:47.416 --> 01:09:51.787
NVIDIA AI is integrated into all of the industry's popular tools and workflows.

01:09:52.788 --> 01:10:00.196
NVIDIA AI is in every cloud, used by the world's largest companies, and by over 7,500 AI startups around the world.

01:10:00.596 --> 01:10:11.006
And NVIDIA AI runs on any system that includes NVIDIA GPUs, from PCs and laptops, to workstations, to supercomputers, in any cloud, to our

01:10:11.006 --> 01:10:13.209
$99 Jetson robot computer.

01:10:13.976 --> 01:10:18.247
One segment of computing we've not served is enterprise computing.

01:10:19.181 --> 01:10:23.118
70% of the world's enterprises run VMware, as we do at NVIDIA.

01:10:23.619 --> 01:10:27.890
VMware was created to run many applications on one virtualized machine.

01:10:28.857 --> 01:10:36.098
AI, on the other hand, runs a single job, bare-metal, on multiple GPUs and often multiple nodes.

01:10:36.932 --> 01:10:44.473
All of the NVIDIA optimizations for compute and data transfer are now plumbed through the VMware stack so AI workloads can be distributed to

01:10:44.473 --> 01:10:47.610
multiple systems and achieve bare-metal performance.

01:10:48.444 --> 01:10:51.914
The VMware stack is also offloaded and accelerated on NVIDIA BlueField.

01:10:52.815 --> 01:11:02.224
NVIDIA AI now runs in its full glory on VMware, which means everything that has been accelerated by NVIDIA AI now runs great on VMware.

01:11:03.158 --> 01:11:07.630
AI applications can be deployed and orchestrated with Kubernetes running on VMware Tanzu.

01:11:08.364 --> 01:11:11.867
We call this platform NVIDIA EGX for Enterprise.

01:11:12.534 --> 01:11:22.444
The enterprise IT ecosystem is thrilled - finally the 300,000 VMware enterprise customers can easily build an AI computing infrastructure

01:11:22.778 --> 01:11:25.814
that seamlessly integrates into their existing environment.

01:11:26.548 --> 01:11:32.721
In total, over 50 servers from the world's top server makers will be certified for NVIDIA EGX Enterprise.

01:11:33.022 --> 01:11:38.227
BlueField 2 offloads and accelerates the VMware stack and does the networking for distributed computing.

01:11:38.794 --> 01:11:46.001
Enterprise can choose big or small GPUs for heavy-compute or heavy-graphics workloads like Omniverse, or mix and match.

01:11:46.902 --> 01:11:48.370
All run NVIDIA AI.

01:11:49.171 --> 01:11:58.013
Enterprise companies make up the world's largest industries and they operate at the edge - in hospitals, factories, plants, warehouses,

01:11:58.013 --> 01:12:02.117
stores, farms, cities and roads - far from data centers.

01:12:03.185 --> 01:12:04.787
The missing link is 5G.

01:12:05.621 --> 01:12:10.059
Consumer 5G is great, but Private 5G is revolutionary.

01:12:10.893 --> 01:12:20.102
Today, we're announcing the Aerial A100 - bringing together 5G and AI into a new type of computing platform designed for the edge.

01:12:20.636 --> 01:12:29.578
Aerial A100 integrates the Ampere GPU and BlueField DPU into one card - this is the most advanced PCI express card ever created.

01:12:29.978 --> 01:12:35.918
So, it's not a surprise that Aerial A100 in an EGX system will be a complete 5G base station.

01:12:36.952 --> 01:12:52.201
Aerial A100 delivers up to full 20 Gbps and can process up to 9 100Mhz massive MIMO for 64T64R - or 64 transmit and 64 receive antenna arrays

01:12:52.201 --> 01:12:54.603
- state of the art capabilities.

01:12:55.270 --> 01:13:06.843
Aerial A100 is software-defined, with accelerated features like PHY, Virtual Network Functions, network acceleration, packet pacing, and line-rate cryptography.

01:13:08.117 --> 01:13:16.825
Our partners ERICSSON, Fujitsu, Mavenir, Altran, and Radisys will build their total 5G solutions on top of the Aerial library.

01:13:17.826 --> 01:13:28.303
NVIDIA EGX server with Aerial A100 is the first 5G base-station that is also a cloud-native, secure, AI edge data center.

01:13:29.138 --> 01:13:32.708
We have brought the power of the cloud to the 5G edge.

01:13:33.609 --> 01:13:36.779
Aerial also extends the power of 5G into the cloud.

01:13:37.312 --> 01:13:43.152
Today, we are excited to announce that Google will support NVIDIA Aerial in the GCP cloud.

01:13:43.986 --> 01:13:45.621
I have an important new platform to tell you about.

01:13:46.522 --> 01:13:54.229
The rise of microservice-based applications and hybrid-cloud has exposed billions of connections in a data center to potential attack.

01:13:54.863 --> 01:14:04.889
Modern Zero-Trust security models assume the intruder is already inside and all container-to-container communications should be inspected, even within a node. 

01:14:05.274 --> 01:14:06.708
This is not possible today.

01:14:07.476 --> 01:14:12.448
The CPU load of monitoring every piece of traffic is simply too great.

01:14:13.215 --> 01:14:20.489
Today, we are announcing NVIDIA Morpheus - a data center security platform for real-time all-packet inspection.

01:14:21.156 --> 01:14:28.530
Morpheus is built on NVIDIA AI, NVIDIA BlueField, Net-Q network telemetry software, and EGX.

01:14:29.431 --> 01:14:39.374
We're working to create solutions with industry leaders in data center security - Fortinet, Red Hat, Cloudflare, Splunk, F5, and Aria

01:14:39.374 --> 01:14:46.782
Cybersecurity. And early customers - Booz Allen Hamilton, Best Buy, and of course, our own team at NVIDIA.

01:14:47.716 --> 01:14:49.952
Let me show you how we're using Morpheus at NVIDIA.

01:14:52.654 --> 01:14:54.089
It starts with a network.

01:14:54.523 --> 01:15:01.563
Here we see a representation of a network, where dots are servers and lines (the edges) are packets flowing between those servers.

01:15:01.897 --> 01:15:09.938
Except in this network, Morpheus is deployed. This enables AI inferencing across your entire network, including east/west traffic. The

01:15:09.938 --> 01:15:17.613
particular model being used here has been trained to identify sensitive information - AWS credentials, GitHub credentials, private keys,

01:15:17.713 --> 01:15:22.751
passwords. If observed in the packet, these would appear as red lines, and we don't see any of that.

01:15:23.519 --> 01:15:24.820
Uh oh, what happened.

01:15:25.454 --> 01:15:29.391
An updated configuration was deployed to a critical business app on this server.

01:15:30.025 --> 01:15:34.830
This update accidentally removed encryption, and now everything that communicates with that app

01:15:34.830 --> 01:15:37.933
sends and receives sensitive credentials in the clear.

01:15:38.800 --> 01:15:47.075
This can quickly impact additional servers. This translates to continuing exposure on the network. The AI model in Morpheus is searching

01:15:47.075 --> 01:15:53.982
through every packet for any of these credentials, continually flagging when it encounters such data. And rather than using pattern

01:15:53.982 --> 01:16:00.589
matching, this is done with a deep neural network - trained to generalize and identify patterns beyond static rule sets.

01:16:01.557 --> 01:16:08.330
Notice all of the individual lines. It's easy to see how quickly a human could be overwhelmed by the vast amount of data coming in.

01:16:09.364 --> 01:16:13.835
Scrolling through the raw data gives a sense of the massive scale and complexity that is involved.

01:16:14.636 --> 01:16:19.408
With Morpheus, we immediately see the lines that represent leaked sensitive information.

01:16:20.008 --> 01:16:27.149
By hovering over one of those red lines, we show complete info about the credential, making it easy to triage and remediate.

01:16:27.950 --> 01:16:35.991
But what happens when this remediation is necessary? Morpheus enables cyber applications to integrate and collect information for automated

01:16:36.058 --> 01:16:38.894
incident management and action prioritization.

01:16:39.361 --> 01:16:48.003
Originating servers, destination servers, actual exposed credentials, and even the raw data is available. This speeds recovery and informs

01:16:48.036 --> 01:16:50.739
which keys were compromised and need be rotated.

01:16:51.206 --> 01:16:53.976
With Morpheus, the chaos becomes manageable.

01:16:56.812 --> 01:17:02.951
The IT ecosystem has been hungry for an AI computing platform that is enterprise and edge ready.

01:17:03.619 --> 01:17:10.258
NVIDIA AI on EGX with Aerial 5G is the foundation of what the IT ecosystem has been waiting for.

01:17:10.859 --> 01:17:20.535
We are supported by leaders from all across the IT industry - from systems, infrastructure software, storage and security, data analytics,

01:17:20.969 --> 01:17:26.708
industrial edge solutions, manufacturing design and automation, to 5G infrastructure.

01:17:27.609 --> 01:17:36.018
To complete our enterprise offering, we now have NVIDIA AI Enterprise software so businesses can get direct-line support from NVIDIA.

01:17:37.085 --> 01:17:43.992
NVIDIA AI Enterprise is optimized and certified for VMware, and offers services and support needed by mission-critical enterprises.

01:17:45.394 --> 01:17:49.064
Deep learning has unquestionably revolutionized computing.

01:17:49.798 --> 01:17:54.002
Researchers continue to innovate at lightspeed with new models and new variants.

01:17:54.536 --> 01:18:01.109
We're creating new systems to expand AI into new segments - like Arm, enterprise, or 5G edge.

01:18:01.443 --> 01:18:06.615
We're also using these systems to do basic research in AI and building new AI products and services.

01:18:07.249 --> 01:18:11.186
Let me show you some of our work in AI, basic and applied research.

01:18:11.853 --> 01:18:14.189
DLSS: Deep Learning Super Sampling.

01:18:14.923 --> 01:18:18.060
StyleGAN: AI high resolution image generator.

01:18:18.960 --> 01:18:24.399
GANcraft: a neural rendering engine, turning Minecraft into realistic 3D.

01:18:25.333 --> 01:18:30.005
GANverse3D: turns photographs into animatable 3D models.

01:18:31.106 --> 01:18:38.613
Face Vid2Vid: a talking-head rendering engine that can reduce streaming bandwidth by 10x, while re-posing the head and eyes.

01:18:39.448 --> 01:18:46.421
Sim2Real: a quadruped trained in Omniverse, and the AI can run in a real robot - digital twins.

01:18:47.255 --> 01:18:52.527
SimNet: a Physics Informed Neural Network solver that can simulate large-scale multi-physics.

01:18:53.295 --> 01:18:56.698
BioMegatron: the largest biomedical language model ever trained.

01:18:57.866 --> 01:19:00.836
3DGT: Omniverse synthetic data generation.

01:19:01.670 --> 01:19:04.940
and OrbNet: a machine learning quantum solver for quantum chemistry.

01:19:05.640 --> 01:19:09.277
This is just a small sampling of the AI work we're doing at NVIDIA.

01:19:09.778 --> 01:19:12.581
We're building AIs to use in our products and platforms.

01:19:13.381 --> 01:19:17.686
We're also packaging up the models to be easily integrated into your applications.

01:19:18.353 --> 01:19:22.491
These are essentially no-coding open-source applications that you can modify.

01:19:23.225 --> 01:19:28.330
Now, we're offering NGC pre-trained models, that you can plug into these applications, or ones you develop.

01:19:28.897 --> 01:19:34.669
These pre-trained models are production quality, trained by experts, and will continue to benefit from refinement.

01:19:35.403 --> 01:19:39.574
There are new credentials to tell you about the models' development, testing, and use.

01:19:40.208 --> 01:19:42.944
And each comes with a reference application sample code.

01:19:43.745 --> 01:19:52.731
NVIDIA pre-trained models are state-of-the-art and meticulously trained, but there is infinite diversity of application domains, environments, and specializations.

01:19:53.522 --> 01:19:58.527
No one has all the data - sometimes it's rare, sometimes they're trade-secrets.

01:19:59.194 --> 01:20:05.167
So we created technology for you to fine-tune and adapt NVIDIA pre-trained models for your applications.

01:20:06.001 --> 01:20:10.705
TAO applies transfer learning on your data to fine-tune our models with your data.

01:20:11.173 --> 01:20:19.314
TAO has an excellent federated learning system to let multiple parties collectively train a shared model while protecting data privacy.

01:20:20.081 --> 01:20:27.255
NVIDIA federated learning is a big deal - researchers at different hospitals can collaborate on one AI model while keeping their data

01:20:27.255 --> 01:20:29.291
separate to protect patient privacy.

01:20:30.025 --> 01:20:35.330
TAO uses NVIDIA's great TensorRT to optimize the model for the target GPU system.

01:20:35.964 --> 01:20:42.437
With NVIDIA pre-trained models and TAO, something previously impossible for many, can now be done in hours.

01:20:43.171 --> 01:20:56.748
Fleet Command is a cloud-native platform for securely operating and orchestrating AI across a distributed fleet of computers - it was purpose-built for operating AI at the edge.

01:20:57.686 --> 01:21:06.828
Fleet Command running on Certified EGX systems will feature secure boot, attestation, uplink and downlink, to a confidential enclave.

01:21:07.762 --> 01:21:11.032
From any cloud or on-prem, you can monitor the health of your fleet.

01:21:11.867 --> 01:21:17.320
Let's take a look at how one customer is using NGC pre-trained models and TAO to fine-tune models

01:21:17.320 --> 01:21:22.969
and run in our Metropolis smart city application, orchestrated by Fleet Command.

01:21:25.213 --> 01:21:28.884
No two industrial environments are the same. And conditions routinely change

01:21:29.284 --> 01:21:36.258
Adapting, managing, and operationalizing AI-enabled applications at the edge for unique, specific sites can be incredibly challenging -

01:21:36.258 --> 01:21:38.560
requiring a lot of data and time for model training.

01:21:39.394 --> 01:21:42.898
Here, we're building applications for a factory with multiple problems to solve

01:21:43.632 --> 01:21:46.401
Understanding how a factory floor space is used over time

01:21:46.801 --> 01:21:50.438
Ensuring worker safety, with constantly evolving machinery and risk factors

01:21:51.106 --> 01:21:54.309
Inspecting products on the factory line, where operations change routinely

01:21:55.277 --> 01:22:00.982
We start with a Metropolis application running 3 pretrained models from NGC. We are up and running in minutes.

01:22:01.216 --> 01:22:07.555
But since the environment is visually quite different from the data used to train this model, the video analytics accuracy at this specific

01:22:07.555 --> 01:22:12.527
site isn't great. People are not being accurately recognized and tracked and defects are being missed.

01:22:13.061 --> 01:22:15.163
Now let's use NVIDIA TAO to solve for this.

01:22:15.430 --> 01:22:22.037
With this simple UI, we re-train and adapt our pre-trained models with labelled data from the specific environment we're deploying in.

01:22:22.637 --> 01:22:23.838
We select our datasets.

01:22:24.439 --> 01:22:29.978
Each is a few 100 images, as opposed to millions of labelled images required if we were to train this from scratch.

01:22:30.645 --> 01:22:34.616
With NVIDIA Tao we go from 65% to over 90% accuracy.

01:22:35.216 --> 01:22:40.922
And through pruning & quantization, compute complexity is reduced by 2x - with no reduction in accuracy

01:22:40.922 --> 01:22:42.557
and real-time performance is maintained.

01:22:44.159 --> 01:22:48.863
In this example, the result is 3 models specifically trained for our factory - all in just minutes

01:22:49.731 --> 01:22:55.003
With one-click, we update and deploy these optimized models onto NVIDIA Certified servers with Fleet Command, 

01:22:55.236 --> 01:22:57.305
seamlessly and securely from the cloud.

01:22:58.573 --> 01:23:05.814
From secure boot to our confidential AI Enclave in GPUs, application data and critical intellectual property remains safe.

01:23:06.314 --> 01:23:09.851
AI accuracy, system performance and health can be monitored remotely.

01:23:10.385 --> 01:23:13.888
This establishes a feedback loop for continuous application enhancements.

01:23:14.356 --> 01:23:19.461
The factory now has an end-to-end framework to adapt to changing conditions - often and easily.

01:23:20.362 --> 01:23:27.669
We make it easy to adapt and optimize NGC pretrained models with NVIDIA TAO and deploy and orchestrate applications with Fleet Command.

01:23:30.505 --> 01:23:36.945
We have all kinds of computer vision, speech, language and robotics models, and more coming all the time.

01:23:37.545 --> 01:23:39.948
Some are the work of our genetics and medical imaging team.

01:23:40.482 --> 01:23:45.754
For example, this model that predicts supplemental oxygen needs from xX-ray and electronic health records.

01:23:46.287 --> 01:23:52.727
This is a collaboration of 20 hospitals across 8 countries and 5 continents for COVID-19.

01:23:53.028 --> 01:23:56.898
Federated learning and NVIDIA TAO was essential to make this possible.

01:23:58.533 --> 01:24:04.072
The world needs a state-of-the-art conversational AI that can be customized and processed anywhere.

01:24:04.706 --> 01:24:16.373
Today, we're announcing the availability of NVIDIA Jarvis - a state-of-the-art deep learning AI for speech recognition, language understanding, translations, and speech.

01:24:17.252 --> 01:24:27.949
End-to-end GPU-accelerated, Jarvis interacts in about 100 milliseconds - listening, understanding, and responding faster than the blink of a human eye. 

01:24:28.930 --> 01:24:34.718
We trained Jarvis for several million GPU-hours, on over 1 billion pages of text,

01:24:34.718 --> 01:24:41.293
and sixty thousand hours of speech in different languages, accents, environments and lingos.

01:24:41.810 --> 01:24:46.305
Out of the box, Jarvis achieves a world-class 90% recognition accuracy.

01:24:46.305 --> 01:24:51.568
You can get even better for your application by refining with your own data using NVIDIA TAO.

01:24:52.520 --> 01:24:57.792
Jarvis supports 5 languages today - English, Japanese, Spanish, German, French, and Russian.

01:24:58.560 --> 01:25:12.784
Jarvis does excellent translation, in the Bilingual Evaluation Understudy benchmark, Jarvis scores 40 points for English-to-Japanese, and 50 points for English-to-Spanish.

01:25:13.608 --> 01:25:18.780
40 is high quality and state-of-the-art. 50 is considered fluent translation.

01:25:19.881 --> 01:25:26.588
Jarvis can be customized for domain jargon. We've trained Jarvis for technical and healthcare scenarios. It's easy with TAO.

01:25:26.588 --> 01:25:33.394
And Jarvis now speaks with expression and emotion that you can control - no more mechanical talk.

01:25:33.862 --> 01:25:44.172
And lastly, this is a big one - Jarvis can be deployed in the cloud, on EGX in your data center, at the edge in a shop, warehouse, or

01:25:44.172 --> 01:25:49.944
factory running on EGX Aerial, or inside a delivery robot running on a Jetson computer.

01:25:50.712 --> 01:25:58.920
Jarvis early access program began last May and our Conversational AI software has been downloaded 45,000 times.

01:25:59.587 --> 01:26:08.796
Among early users is T-Mobile, the U.S. telecom giant. They're using Jarvis to offer exceptional customer service that demands high-quality

01:26:08.796 --> 01:26:12.000
and low-latency needed in real-time speech recognition.

01:26:12.967 --> 01:26:21.305
We're announcing a partnership with Mozilla Common Voice, one of the world's largest multi-language voice datasets, and its openly available to all 

01:26:22.076 --> 01:26:33.348
NVIDIA will use our DGXs to process and train Jarvis with the dataset of 150,000 speakers in 65 languages and offer Jarvis back to the community for free.

01:26:33.655 --> 01:26:36.984
So go to Mozilla Common Voice and make some recordings!

01:26:36.984 --> 01:26:42.789
Let's make universal translation possible and help people around the world understand each other. 

01:26:43.498 --> 01:26:44.866
Now let me show you Jarvis.

01:26:45.967 --> 01:26:48.069
The first part of Jarvis is speech recognition.

01:26:48.303 --> 01:26:51.870
Jarvis is over 90% accurate out-of-the-box - that's world-class.

01:26:51.870 --> 01:26:58.001
And you can still use TAO to make it even better for your application, like customizing for healthcare jargon.

01:26:58.846 --> 01:27:05.887
Chest x-ray shows left retrocardiac opacity, and this may be due to atelectasis, aspiration, or early pneumonia.

01:27:07.388 --> 01:27:10.592
I have no idea what I said but Jarvis recognized it perfectly.

01:27:11.226 --> 01:27:13.795
Jarvis translation now supports five languages.

01:27:13.995 --> 01:27:15.149
Let's do Japanese.

01:27:15.149 --> 01:27:25.990
"Excuse me, I'm looking for the famous Jangara Ramen shop. It should be nearby, but I don't see it on my map. Can you show me the way? I'm very hungry."

01:27:28.710 --> 01:27:33.615
That's great. Excellent accuracy. I think. Instantaneous response.

01:27:34.082 --> 01:27:37.285
You can do German, French, and Russian--with more languages on the way.

01:27:37.952 --> 01:27:39.554
Jarvis also speaks with feelings.

01:27:40.188 --> 01:27:44.025
Let's try this - "The more you buy, the more you save!"

01:27:48.596 --> 01:27:51.499
"The more you buy, the more you save"

01:27:52.033 --> 01:27:53.935
I think we are going to need more enthusiasm.

01:27:59.741 --> 01:28:02.710
"The more you buy, the more you save!"

01:28:02.910 --> 01:28:13.187
NVIDIA Jarvis - state-of-the-art deep learning conversational AI, interactive response, 5 languages, customize with TAO,

01:28:13.187 --> 01:28:16.697
and deploy from cloud, to edge, to autonomous systems.

01:28:17.492 --> 01:28:24.866
Recommender systems are the most important machine learning pipeline in the world today.

01:28:24.866 --> 01:28:29.707
It is the engine for search, ads, on-line shopping, music, books, movies, user-generated content, news.

01:28:30.538 --> 01:28:38.279
Recommender systems predict your needs and preferences from past interactions with you, your explicit preferences

01:28:38.279 --> 01:28:41.049
and learned preferences using methods called collaborative and content filtering.

01:28:42.283 --> 01:28:47.422
Trillions of items to be recommended to billions of people - the problem space is quite large.

01:28:48.256 --> 01:28:55.327
We would like to productize a state-of-the-art recommender system so that all companies can benefit from the transformative capabilities of this AI. 

01:28:56.097 --> 01:29:06.603
We built an open-source recommender system framework, called Merlin, which simplifies an end-to-end workflow from ETL, to training, to validation, to inference.

01:29:07.375 --> 01:29:10.178
It is architected to scale as your dataset grows.

01:29:10.745 --> 01:29:15.450
And if you already have a ton of data, it is the fastest recommender system ever built.

01:29:16.050 --> 01:29:27.328
In our benchmarks, we achieved speed-ups of 10-50x for ETL, 2-10x for training and 3-100 times for inference depending on the exact setup.

01:29:28.663 --> 01:29:31.432
Merlin is now available on NGC.

01:29:32.033 --> 01:29:39.974
Our vision for Maxine is to eventually be your avatar in virtual worlds created in Omniverse - to enable virtual presence.

01:29:40.975 --> 01:29:44.011
The technologies of virtual presence can benefit video conferencing today.

01:29:44.545 --> 01:29:46.347
Alex is going to tell you all about it.

01:29:47.382 --> 01:29:51.219
Hi everyone, I'm Alex and I'm the Product Manager for NVIDIA Maxine.

01:29:51.819 --> 01:29:57.959
These days, we're spending so much time doing video conferencing. Don't you want a much better video communication experience?

01:29:58.760 --> 01:30:07.418
Today, I am so thrilled today to share with you the NVIDIA Maxine, a suite of AI technologies that can improve the video conferencing experience for everyone.

01:30:08.035 --> 01:30:13.741
When combined with NVIDIA Jarvis, Maxine offers the most accurate speech to text .

01:30:13.741 --> 01:30:17.612
See, It's now transcribing everything I'm saying, all in real time.

01:30:18.446 --> 01:30:25.653
And in addition, with the help of Jarvis, Maxine can also translate what I am saying into multiple languages. 

01:30:26.254 --> 01:30:29.190
That would make international meetings so much easier.

01:30:30.024 --> 01:30:34.429
Another great feature I'd love to share with you is Maxine's Eye Contact feature.

01:30:35.363 --> 01:30:39.066
Often times when I am presenting, I am not looking at the camera

01:30:39.467 --> 01:30:47.241
When I turn on Maxine's eye contact feature, it corrects the position of my eyes so that I'm looking back into the camera again. 

01:30:47.241 --> 01:30:49.444
Now, I can make eye contact with everyone else.

01:30:49.844 --> 01:30:52.814
The meeting experience just gets so much more engaging!

01:30:53.114 --> 01:30:59.987
Last but definitely not least, Maxine can even improve the video quality when bandwidth isn't sufficient.

01:31:00.321 --> 01:31:06.627
Now let's take a look at my video quality when bandwidth drops to as low as 50 kbps.

01:31:06.627 --> 01:31:08.162
Definitely not great.

01:31:08.596 --> 01:31:16.103
Maxine's AI face codec can improve the quality of my video even when bandwidth is as low as 50 kbps.

01:31:17.405 --> 01:31:26.914
The most powerful part of Maxine is that all of these amazing AI features can run simultaneously together and all in real time. 

01:31:26.914 --> 01:31:30.384
Or you can just pick and choose a few. It's just that flexible!

01:31:30.885 --> 01:31:40.394
Now before I go let me turn off all the Maxine features so you can see what I looked like without Maxine's help. Not ideal.

01:31:40.394 --> 01:31:42.864
Now let's turn back on all the Maxine features.

01:31:43.698 --> 01:31:47.568
See - so much better thanks to NVIDIA Maxine!

01:31:49.904 --> 01:31:51.539
NGC has pre-trained models.

01:31:52.273 --> 01:31:55.676
TAO lets you fine-tune and adapt models to your applications.

01:31:56.043 --> 01:31:58.479
Fleet Command deploys and orchestrates your models.

01:31:58.880 --> 01:32:07.613
The final piece is the inference server - to infer insight from the continuous streams of data coming into your EGX servers or your cloud instance. 

01:32:08.890 --> 01:32:10.958
NVIDIA Triton is our inference server.

01:32:11.459 --> 01:32:16.564
Triton is a model scheduling and dispatch engine that can handle just about anything you throw at it:

01:32:17.164 --> 01:32:21.469
Any AI model that runs on cuDNN, so basically every AI model.

01:32:22.103 --> 01:32:30.878
From any framework - TensorFlow, Pytorch, ONNX, OpenVINO, TensorRT, or custom C++/python backends.

01:32:31.178 --> 01:32:35.850
Triton schedules on the multiple generations of NVIDIA GPUs and x86 CPUs.

01:32:36.417 --> 01:32:39.921
Triton maximizes the CPU and GPU utilization.

01:32:40.655 --> 01:32:44.292
Triton scales with Kubernetes and handles live updates.

01:32:44.959 --> 01:32:51.499
Triton is fantastic for everything from image to speech recognition, from recommenders to language understanding.

01:32:52.099 --> 01:32:56.871
But let me show you something really hard - generating biomolecules for drug discovery.

01:32:57.505 --> 01:33:06.493
In drug discovery, it all comes down to finding the right molecule, the right shape, the right interactions with a protein - the right pharmaco-kinetic properties.

01:33:07.548 --> 01:33:16.000
Working with scientists from Astra-Zeneca, NVIDIA researchers trained a language model to understand SMILES - the language of chemical structures. 

01:33:16.991 --> 01:33:20.661
We developed the model with Megatron and trained it on a SuperPOD.

01:33:20.861 --> 01:33:32.947
Then created a generative model that reads the structure of successful chemical drug-compounds to then generate potentially effective novel-compounds. 

01:33:33.874 --> 01:33:45.824
Now we can use AI to generate candidate compounds that can then be further refined with physics-based simulations, like docking or Schr?dinger's FEP+.

01:33:47.455 --> 01:33:52.994
Generating with a CPU is slow - it takes almost 8 seconds to generate one molecule.

01:33:53.961 --> 01:34:01.402
On a single A100 with Triton, it takes about 0.3 seconds - 32X faster!

01:34:02.603 --> 01:34:08.909
Using Triton Inference Server, we can scale this up to a SuperPOD and generate thousands of molecules per second.

01:34:09.877 --> 01:34:13.981
AI and simulation is going to revolutionize drug discovery.

01:34:14.649 --> 01:34:19.578
We provide these AI capabilities to our ecosystem of millions of developers around the world.

01:34:19.578 --> 01:34:22.954
Thousands of companies are building their most important services on NVIDIA AI.

01:34:23.691 --> 01:34:28.195
BestBuy is using Morpheus as the foundation of AI-based anomaly detection in their network.

01:34:29.063 --> 01:34:37.104
GE Healthcare has built an echocardiogram that uses TensorRT to quickly identify different views of the wall motion of the heart and select the best ones for analysis.

01:34:39.340 --> 01:34:46.405
Spotify has over 4 billion playlists. They're using RAPIDS to analyze models more efficiently, which lets them refresh personalized playlists more often.

01:34:46.410 --> 01:34:50.718
This keeps their content fresh with the latest trends. 

01:34:51.519 --> 01:34:59.427
WeChat is using TensorRT to achieve 8msmillisecond latency even when using the state-of-the-art natural language understanding models like BERT.

01:34:59.427 --> 01:35:03.731
This gives them more accuracy and relevant results when maintaining real-time response.

01:35:04.765 --> 01:35:09.303
It's great to see NVIDIA AI used to build these amazing AI services.

01:35:10.204 --> 01:35:19.246
NVIDIA AI on EGX with Aerial 5G the AI computing platform for the enterprise and Edge - the next wave of AI.

01:35:20.881 --> 01:35:23.851
DRIVE AV is our AV platform and service.

01:35:24.685 --> 01:35:31.158
AV is the most intense machine learning and robotics challenge - one of the hardest, but also the greatest impact.

01:35:31.826 --> 01:35:40.735
We're building an AV platform end-to-end - from the AV chips and computers, sensor architecture, data processing, mapping, developing the

01:35:40.735 --> 01:35:46.914
driving software, creating the simulator and digital twin, fleet command operations, to road testing.

01:35:46.914 --> 01:35:51.445
All of it to the highest functional safety and cybersecurity standards. safety and cybersecurity standards.

01:35:51.946 --> 01:35:54.448
None of these things are openly available, so we build them.

01:35:55.249 --> 01:36:03.457
We build in the modules so our customers and partners in the $10 trillion transportation industry can leverage the parts they need.

01:36:04.024 --> 01:36:08.195
Meanwhile, we build our end-to-end AV service in partnership with Mercedes.

01:36:08.696 --> 01:36:10.798
AV computing demand is skyrocketing.

01:36:10.831 --> 01:36:13.334
Orin is a giant leap over Xavier.

01:36:13.934 --> 01:36:20.307
The more developers learn about AV, the more advanced the algorithms become, and the greater the computing demand.

01:36:21.242 --> 01:36:28.649
More computation capacity gives teams faster iteration and speed to market - leading some to call TOPS the new horsepower.

01:36:29.083 --> 01:36:35.422
And many are realizing that the reserved computing capacity today is an opportunity to offer new services tomorrow.

01:36:36.056 --> 01:36:41.362
NVIDIA DRIVE is an open programmable platform that AI engineers all over the world are familiar with.

01:36:41.896 --> 01:36:48.536
New AI technology is being invented on NVIDIA AI constantly - these inventions will be tomorrow's new services.

01:36:49.470 --> 01:36:52.306
Orin is an amazing AV computer.

01:36:52.306 --> 01:36:57.812
Today, we are announcing that Orin was also designed to be the central computer of the car.

01:36:58.312 --> 01:37:08.122
Orin will process in one central computer the cluster, infotainment, passenger interaction AI, and very importantly, the confidence view 

01:37:08.289 --> 01:37:10.057
or the perception world model.

01:37:10.424 --> 01:37:21.061
The confidence view is what the car actually perceives around it and construct it into a 3D surround model - this is what is in the mind of the autopilot AI.

01:37:21.101 --> 01:37:26.941
It is important that the car shows us that its surround perception is accurate, so that we have confidence in its driving.

01:37:27.675 --> 01:37:31.378
In time, rear view mirrors will be replaced by the surround 3D perception.

01:37:32.213 --> 01:37:42.356
The future is one central computer - 4 domains, virtualized and isolated, architected for functional safety and security, software-defined

01:37:42.623 --> 01:37:48.662
and upgradeable for the life of the car - and super smart AI and beautiful graphics.

01:37:49.897 --> 01:37:57.304
Many car companies have already adopted Orin and so it would help them tremendously to also leverage our full AV development platform.

01:37:58.105 --> 01:38:08.382
Today, we are announcing NVIDIA¡¯s 8th generation Hyperion car platform - including reference sensors, AV and central computers, the 3D ground

01:38:08.415 --> 01:38:12.119
truth data recorder, networking, and all of the essential software.

01:38:13.153 --> 01:38:19.260
Hyperion 8 is compatible with the NVIDIA DRIVE AV stack, so easy to adopt and integrate elements of our stack.

01:38:19.793 --> 01:38:28.435
Hyperion 8 is a fully functional, going-to-production, open AV platform for the multi-trillion-dollar transportation ecosystem.

01:38:29.470 --> 01:38:32.206
Orin will be in production in 2022.

01:38:32.473 --> 01:38:38.479
Meanwhile, our next generation is already in full gear and will be yet another giant leap.

01:38:39.179 --> 01:38:42.549
Today, we are announcing NVIDIA DRIVE Atlan.

01:38:43.050 --> 01:38:52.660
DRIVE Atlan will be 1,000 TOPS on one chip - more than the total compute in most Level 5 robotaxis today.

01:38:53.394 --> 01:39:02.603
To achieve higher autonomy in more conditions, sensor resolutions will continue to increase. There will be more of them. AI models will get

01:39:02.603 --> 01:39:09.543
more sophisticated. There will be more redundancy and safety functionality. We're going to need all of the computing we can get.

01:39:10.711 --> 01:39:21.023
Atlan is a technical marvel - fusing all of NVIDIA's technologies in AI, auto, robotics, safety, and Bluefield secure data center technologies.

01:39:21.755 --> 01:39:33.666
AV and software must be planned as multi-generational investments. The software you invested billions in today must carry over to the entire fleet and to future generations.

01:39:34.201 --> 01:39:38.906
The DRIVE Atlan - The Next Level - One Architecture.

01:39:40.541 --> 01:39:42.876
The car industry has become a technology industry.

01:39:43.344 --> 01:39:48.315
Future cars are going to be completely programmable computers and business models are going to be software driven.

01:39:48.916 --> 01:39:52.252
Car companies will offer software services for the life of the car.

01:39:53.053 --> 01:40:02.463
The new tech mindset sees the car not just as a product to sell, but as an installed base of 10's or 100's of millions to build upon,

01:40:03.063 --> 01:40:06.100
creating billions in services and opportunities.

01:40:07.267 --> 01:40:09.670
The world's big brands have giant opportunities.

01:40:10.337 --> 01:40:12.106
Step one was going electric.

01:40:12.573 --> 01:40:16.110
Now the big brands are making their new fleets autonomous and programmable.

01:40:16.110 --> 01:40:19.613
Orin will be powering many next generation EVs.

01:40:20.247 --> 01:40:23.017
And with Mercedes, we are building the end-to-end system.

01:40:24.184 --> 01:40:26.587
The world moves 10 trillion miles a year.

01:40:27.588 --> 01:40:32.092
If only a fraction of these miles were served by robotaxis, the opportunity is giant.

01:40:33.227 --> 01:40:36.030
We expect robotaxis to start ramping in the next couple years.

01:40:36.864 --> 01:40:43.670
These services will also be platforms for all kinds of new services to be built upon - like last mile delivery.

01:40:45.005 --> 01:40:49.276
The Internet moves electrons, trucks move atoms.

01:40:50.044 --> 01:40:59.219
The rise of e-commerce is putting intense pressure on this system - one click and a new TV shows up at your house, another click and a

01:40:59.219 --> 01:41:00.287
cheeseburger shows up.

01:41:01.055 --> 01:41:02.523
This trend will only go up.

01:41:03.257 --> 01:41:05.993
The U.S. will be short by 100K truckers by 2023.

01:41:07.194 --> 01:41:09.997
The EU is short 150K truckers already.

01:41:10.831 --> 01:41:12.733
China is short 4 million drivers.

01:41:13.500 --> 01:41:24.054
So ideas like driverless trucks from hub-to-hub or driverless trucks inside a port or warehouse campus are excellent near-term ways to augment with automation.

01:41:25.546 --> 01:41:28.415
We started my talk with Omniverse, we'll close with Omniverse.

01:41:29.683 --> 01:41:38.632
You can see how important it is to our work, to robotics, to anyone building AIs that interact with the physical world, to have a physically based simulator, or digital twin.

01:41:39.560 --> 01:41:43.363
In the case of Isaac, the digital twin is the factory in Omniverse.

01:41:44.198 --> 01:41:48.869
In the case of DRIVE, the digital twin is the collective memories of the fleet captured in Omniverse.

01:41:49.670 --> 01:41:57.077
The DRIVE Digital Twin is used throughout the development - it's used for HD map reconstruction, synthetic data generation so we can

01:41:57.077 --> 01:42:05.986
bootstrap training new models, new scenario simulations, hardware-in-the-loop simulations, release validation, for replaying unfamiliar

01:42:06.019 --> 01:42:12.426
scenarios experienced by a car, or a teleoperator up-linking into a car to remotely pilot.

01:42:13.360 --> 01:42:18.599
The DRIVE Digital Twin in Omniverse is a virtual world that every car in the fleet is connected to.

01:42:19.333 --> 01:42:26.440
Today, we're announcing that DRIVE Sim, the engine of DRIVE Digital Twin, will be available for the community this summer.

01:42:27.107 --> 01:42:31.612
Let me show you what DRIVE AV and DRIVE Sim can do - this is a mountain of technology.

01:42:31.879 --> 01:42:32.379
Enjoy.

01:45:06.033 --> 01:45:11.305
What a GTC! It's incredible to think how much was done in this last year.

01:45:11.605 --> 01:45:13.173
I spoke about several things:

01:45:13.774 --> 01:45:16.443
NVIDIA is a full-stack computing platform.

01:45:17.077 --> 01:45:25.845
We're building virtual worlds with NVIDIA Omniverse - a miraculous platform that will help build the next wave of AI for robotics and self-driving cars, 

01:45:27.087 --> 01:45:38.255
We announced new DGX systems and new software. Megatron for giant Transformers, Clara for drug discovery, and cuQuantum for quantum computing. 

01:45:39.466 --> 01:45:51.299
NVIDIA is now a 3-chip company. With the addition of Grace CPU, designed for a specialized segment of computing focused on giant-scale AI and HPC. 

01:45:52.212 --> 01:45:58.185
And for data center infrastructure processing, we announced BlueField-3 and DOCA 1.0.

01:45:58.885 --> 01:46:01.121
NVIDIA is expanding the reach of AI.

01:46:01.888 --> 01:46:11.465
We announced an important new platform, NVIDIA EGX with Aerial 5G, to make AI accessible to all companies and industries.

01:46:12.466 --> 01:46:21.274
We're joined by the leaders of IT - VMware, computer makers, infrastructure software, storage, and security providers.

01:46:21.975 --> 01:46:28.281
And to lower the bar to the highest levels of AI, we offer Pre-Trained Models - like

01:46:28.281 --> 01:46:29.916
Jarvis conversational AI,

01:46:30.417 --> 01:46:31.918
Merlin recommender system,

01:46:32.819 --> 01:46:34.554
Maxine virtual conferencing,

01:46:35.122 --> 01:46:36.656
and Morpheus AI security.

01:46:38.225 --> 01:46:41.361
All of it is optimized on NVIDIA AI Enterprise.

01:46:42.062 --> 01:46:48.935
And with new tools like NVIDIA TAO, Fleet Command, and Triton, it's easy for you to customize and deploy on EGX.

01:46:49.803 --> 01:46:59.713
And Drive - our end-to-end platform for the $10 trillion dollar transportation industry, which is becoming one of the world's largest technology industries.
technology industries.

01:47:00.680 --> 01:47:14.861
From Orin and Atlan, the first 1000 TOPS SOC, to Hyperion 8, a fully-operational reference AV-car platform, to Drive Sim, to Drive AV -

01:47:14.861 --> 01:47:16.997
we're working with the industry at every layer.

01:47:18.198 --> 01:47:26.907
20 years ago, all of this was science fiction. 10 years ago, it was a dream. Today, we're living it.

01:47:28.241 --> 01:47:33.847
I want to thank all of you - developers and partners, and a very special thanks to all the NVIDIA employees.

01:47:34.815 --> 01:47:42.255
Our purpose is to advance our craft, so that you, the Da Vincis of our time, can advance yours.

01:47:43.089 --> 01:47:48.895
Ultimately, NVIDIA is an instrument, an instrument for you to do your life's work.

01:47:50.163 --> 01:47:51.364
Have a great GTC.

He interrupted her. Close at hand is a stable where two beautiful ponies are kept. They are snowy white, and are consecrated to the goddess Ku-wanon, the deity of mercy, who is the presiding genius of the temple. They are in the care of a young girl, and it is considered a pious duty to feed them. Pease and beans are for sale outside, and many devotees contribute a few cash for the benefit of the sacred animals. If the poor beasts should eat a quarter of what is offered to them, or, rather, of what is paid for, they would soon die of overfeeding. It is shrewdly suspected that the grain is sold many times over, in consequence of a collusion between the dealers and the keeper of the horses. At all events, the health of the animals is regarded, and it would never do to give them all that is presented.  On their return from the garden they stopped at a place where eggs are hatched by artificial heat. They are placed over brick ovens or furnaces, where a gentle heat is kept up, and a man is constantly on watch to see that the fire neither burns too rapidly nor too slowly. A great heat would kill the vitality of the egg by baking it, while if the temperature falls below a certain point, the hatching process does not go on. When the little chicks appear, they are placed under the care of an artificial mother, which consists of a bed of soft down and feathers, with a cover three or four inches above it. This cover has strips of down hanging from it, and touching the bed below, and the chickens nestle there quite safe from outside cold. The Chinese have practised this artificial hatching and rearing for thousands of years, and relieved the hens of a great deal of the monotony of life. He would not have it in the scabbard, and when I laid it naked in his hand he kissed the hilt. Charlotte sent Gholson for Ned Ferry. Glancing from the window, I noticed that for some better convenience our scouts had left the grove, and the prisoners had been marched in and huddled close to the veranda-steps, under their heavy marching-guard of Louisianians. One of the blue-coats called up to me softly: "Dying--really?" He turned to his fellows--"Boys, Captain's dying."     Assuming an air of having forgotten all about Dick¡¯s rhyme, he went to his place in the seat behind Jeff and the instant his safety belt was snapped Jeff signaled to a farmer who had come over to investigate and satisfy himself that the airplane had legitimate business there; the farmer kicked the stones used as chocks from under the landing tires and Jeff opened up the throttle.  ¡°Yes,¡± Dick supplemented Larry¡¯s new point. ¡°Another thing, Sandy, that doesn¡¯t explain why he¡¯d take three boys and fly a ship he could never use on water¡ªwith an amphibian right here.¡±      Should you leave me too, O my faithless ladie?  And years of remorse and despair been your fate,   That night was a purging. From thenceforward Reuben was to press on straight to his goal, with no more slackenings or diversions. "Is that you, Robin?" said a soft voice; and a female face was seen peeping half way down the stairs. <a href="http://www.mjsdsa.com.cn/">HoME</a><a href="http://mjsdsa.com.cn/">lãñÔóÂÜÀ­³ó
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