Posts in category: nVidia AI

Each morning millions of bleary-eyed people pour milk into their bowls of cereal or cups of coffee without a second thought as to where that beverage came from.

Few will consider the processes in place to maintain the health of the animals involved in milk production and to ensure that the final product is fit for consumption.

For cattle farmers, few things can sour their efforts like bovine tuberculosis (bTB), a chronic, slow-progressing and debilitating disease. bTB presents significant economic and welfare challenges to the worldwide cattle sector.

Applying GPU-accelerated AI and data science, Scotland’s Rural College (SRUC), headquartered in Edinburgh, recently spearheaded groundbreaking research into how bTB can be monitored and treated more effectively and efficiently.

Bovine Tuberculosis

Caused by bacteria, bTB is highly infectious among cattle and transmissible to other animals and humans.

It also causes substantial financial strain through involuntary culling, animal movement restrictions, and the cost of control and eradication programs. In countries where mandatory eradication programs are not in place for bTB carriers, the disease also carries considerable public health implications.

As bTB is a slow-developing disease, it’s rare for cattle to show any signs of infection until the disease has progressed to its later stages.

To monitor the health of herds, cattle need to receive regular diagnostic tests. Currently, the standard is a single intradermal comparative cervical tuberculin (SICCT) skin test. These tests are time consuming, labor intensive and only correctly identify an infected animal about 50-80 percent of the time.

Milking It

SRUC’s research brought to light a new method of monitoring bTB based on milk samples that were already being collected as part of regular quality control checks through what is called mid-infrared (MIR) analysis.

First, the bTB phenotype (the observable characteristics of an infected animal) was created using data relating to traditional SICCT skin-test results, culture status, whether a cow was slaughtered, and whether any bTB-caused lesions were observed. Information from each of these categories was combined to create a binary phenotype, with zero representing healthy cows and 1 representing bTB-affected cows.

Contemporaneous individual milk MIR data was collected as part of monthly routine milk recording, matched to bTB status of individual animals on the SICCT test date, and converted into 53×20-pixel images. These were used to train a deep convolutional neural network on an NVIDIA DGX Station that was able to identify particular high-level features indicative of bTB infection.

SRUC’s models were able to identify which cows would be expected to fail the SICCT skin test, with an accuracy of 95 percent and a corresponding sensitivity and specificity of 0.96 and 0.94, respectively.

To process the millions of data points used for training their bTB prediction models, the team at SRUC needed a computing system that was fast, stable and secure. Using an NVIDIA DGX Station, models that had previously needed months of work now could be developed in a matter of days. And with RAPIDS data science software on top, the team further accelerated their research and started developing deep learning models in just a few hours.

“By running our models on NVIDIA DGX Station with RAPIDS, we were able to speed up the time it took to develop models at least tenfold,” said Professor Mike Coffey, leader of the Animal Breeding Team and head of EGENES at SRUC. “Speeding up this process means that we’ll be able to get meaningful solutions for combating bTB into the hands of farmers faster and vastly improve how bTB is handled nationwide.”

Moo-ving Forward

Using routinely collected milk samples for the early identification of bTB-infected cows represents an innovative, low-cost and, importantly, noninvasive tool that has the potential to contribute substantially to the push to eradicate bTB in the U.K. and beyond.

Such a tool would enable farmers to get access to crucial information much faster than currently possible. And this would enable farmers to make more efficient and informed decisions that significantly increase the health and welfare of their animals, as well as reduce costs to the farm, government and taxpayer.

The success of predicting bTB status with deep learning also opens up the possibility to calibrate MIR analysis for other diseases, such as paratuberculosis (Johne’s disease), to help improve cattle welfare further.

Explore virtual collaboration and photorealistic simulation with NVIDIA Omniverse open beta, available now.

NVIDIA Omniverse is an open, cloud-native platform that makes it easy to accelerate design workflows and collaborate in real time. Omniverse allows creators, engineers and researchers to collaborate in virtual worlds that are all connected — the beginnings of the term Neal Stephenson coined, “Metaverse.”

The platform enhances efficiency, productivity and flexibility, as teams from across the room or across the globe can enter Omniverse and simultaneously work together on projects with real-time photorealistic rendering.

As part of the open beta, we’ve released several Omniverse applications that can be used within the Omniverse software, including Omniverse View for architecture, engineering and construction professionals; Omniverse Create for designers, creators and specialists in media and entertainment and manufacturing/product design; and Omniverse Kaolin for 3D deep learning researchers.

Early next year, we’ll release Omniverse Audio2Face, an AI-powered facial animation app; Omniverse Machinima for GeForce RTX gamers; and Isaac Sim 2021.1 for robotics development.

A fundamental breakthrough of Omniverse is the ability to easily work concurrently between software applications. Creators, designers and engineers can access several Omniverse Connectors to leading industry software applications like Autodesk Maya and Revit, Adobe Photoshop, Substance Designer, Substance Painter, McNeel Rhino, Trimble SketchUp and Epic Unreal Engine. Many more are in development, including those for Blender, Houdini, 3ds Max and Motion Builder.

Customers Reach New Heights with Omniverse

Over 500 creators and professionals across industries like architecture, manufacturing, product design, and media and entertainment have tested Omniverse through our early access program.

Global architectural firm Kohn Pedersen Fox used the platform to bring together its worldwide offices and have its people work simultaneously on projects.

“The future of architectural design will rely on the accessibility of all design data in one accurate visualization and simulation application,” said Cobus Bothma, director of applied research at KPF. “We’ve been testing NVIDIA Omniverse and it shows great potential to allow our entire design team to use a variety of applications to collaborate in real time — wherever they’re working.”

Ecoplants, a member of NVIDIA Inception, focuses on bringing real-world experience into the virtual world in an innovative way, offering high-quality real 3D models and materials.

“Omniverse innovatively fuses development and production workflows of different software packages and across multiple industries,” said Peng Cheng, CEO of Ecoplants. “We are testing how we will use Omniverse to be widely adopted and intensively applied across our company, delivering long-term business value and enabling us to improve efficiency and reduce rendering time from hours to seconds.”

Many industry software leaders are also integrating Omniverse into their applications so users can collaborate and work through graphics workflows.

One early partner for Omniverse is Adobe with its Substance by Adobe suite for texturing and material authoring. At the forefront of physically based rendering, and an early supporter of NVIDIA Material Definition Language, Substance has revolutionized the texturing workflow for real-time rendering.

“From its inception, we’ve been a strong believer in Omniverse and the vision behind application interoperability. We’re proud to be among the first to work with NVIDIA to integrate Substance by Adobe into early versions of the platform,” said Sébastien Deguy, vice president of 3D and Immersive at Adobe. “We can’t wait for our users to experience the power of real-time collaboration and unlock new, more powerful workflows.”

Reallusion is a software developer of animation tools, pipelines and assets for real-time production. Specializing in digital human character creation and animation, Reallusion software provides users with a rapid development solution for virtual characters.

“Since our early understanding of the platform, we have been working to connect our iClone character and animation capabilities through a live link to Omniverse.  We believe this open platform will enable artist, designers, movie and game makers to collaborate and design across multiple workflows in real-time with amazing fidelity,” said Charles Chen, CEO of Reallusion. “The ability for our users to move from world to world and leverage the power of our tools combined with other software tools in their workflows seamlessly and quickly will enable world building and real-world simulation.”

The Metaverse Begins

NVIDIA Omniverse has played a critical role in physically accurate virtual world simulation.

At GTC in October, we showed how DRIVE Sim leveraged the platform’s real-time, photoreal simulation capabilities for end-to-end, physically accurate autonomous vehicle virtual validation.

NVIDIA Isaac Sim 2021.1, releasing in February, is built entirely on Omniverse and meets the demand for accurate, reliable, easy-to-use simulation tools in robotics. Researchers and developers around the world can use the app within Omniverse to enhance their robotics simulation and training.

And in research, Omniverse is combining scientific visualization tools with high-quality computer graphics. NVIDIA showcased a simulation of COVID-19, visualized in Omniverse, where each spike on a coronavirus protein is represented with more than 1.8 million triangles, rendered by NVIDIA RTX GPUs.

NVIDIA Omniverse isn’t just a breakthrough in graphics — it’s a platform that’s setting the new standard for design and real-time collaboration across all industries.

Users can download the platform directly from NVIDIA and run on any NVIDIA RTX-enabled GPU. Learn more about NVIDIA Omniverse and download the open beta today at www.nvidia.com/omniverse.

NVIDIA researchers are defining ways to make faster AI chips in systems with greater bandwidth that are easier to program, said Bill Dally, NVIDIA’s chief scientist, in a keynote released today for a virtual GTC China event.

He described three projects as examples of how the 200-person research team he leads is working to stoke Huang’s Law — the prediction named for NVIDIA CEO Jensen Huang that GPUs will double AI performance every year.

“If we really want to improve computer performance, Huang’s Law is the metric that matters, and I expect it to continue for the foreseeable future,” said Dally, who helped direct research at NVIDIA in AI, ray tracing and fast interconnects.

An Ultra-Efficient Accelerator

Toward that end, NVIDIA researchers created a tool called MAGNet that generated an AI inference accelerator that hit 100 tera-operations per watt in a simulation. That’s more than an order of magnitude greater efficiency than today’s commercial chips.

MAGNet uses new techniques to orchestrate the flow of information through a device in ways that minimize the data movement that burns most of the energy in today’s chips. The research prototype is implemented as a modular set of tiles so it can scale flexibly.

A separate effort seeks to replace today’s electrical links inside systems with faster optical ones.

Firing on All Photons

“We can see our way to doubling the speed of our NVLink [that connects GPUs] and maybe doubling it again, but eventually electrical signaling runs out of gas,” said Dally, who holds more than 120 patents and chaired the computer science department at Stanford before joining NVIDIA in 2009.

The team is collaborating with researchers at Columbia University on ways to harness techniques telecom providers use in their core networks to merge dozens of signals onto a single optical fiber.

Called dense wavelength division multiplexing, it holds the potential to pack multiple terabits per second into links that fit into a single millimeter of space on the side of a chip, more than 10x the density of today’s interconnects.

Besides faster throughput, the optical links enable denser systems. For example, Dally showed a mockup (below) of a future NVIDIA DGX system with more than 160 GPUs.

In software, NVIDIA’s researchers have prototyped a new programming system called Legate. It lets developers take a program written for a single GPU and run it on a system of any size — even a giant supercomputer like Selene that packs thousands of GPUs.

Legate couples a new form of programming shorthand with accelerated software libraries and an advanced runtime environment called Legion. It’s already being put to the test at U.S. national labs.

Rendering a Vivid Future

The three research projects make up just one part of Dally’s keynote, which describes NVIDIA’s domain-specific platforms for a variety of industries such as healthcare, self-driving cars and robotics. He also delves into data science, AI and graphics.

“In a few generations our products will produce amazing images in real time using path tracing with physically based rendering, and we’ll be able to generate whole scenes with AI,” said Dally.

He showed the first public demonstration (below) that combines NVIDIA’s conversational AI framework called Jarvis with GauGAN, a tool that uses generative adversarial networks to create beautiful landscapes from simple sketches. The demo lets users instantly generate photorealistic landscapes using simple voice commands.

In an interview between recording sessions for the keynote, Dally expressed particular pride for the team’s pioneering work in several areas.

“All our current ray tracing started in NVIDIA Research with prototypes that got our product teams excited. And in 2011, I assigned [NVIDIA researcher] Bryan Catanzaro to work with [Stanford professor] Andrew Ng on a project that became CuDNN, software that kicked off much of our work in deep learning,” he said.

A First Foothold in Networking

Dally also spearheaded a collaboration that led to the first prototypes of NVLink and NVSwitch, interconnects that link GPUs running inside some of the world’s largest supercomputers today.

“The product teams grabbed the work out of our hands before we were ready to let go of it, and now we’re considered one of the most advanced networking companies,” he said.

With his passion for technology, Dally said he often feels like a kid in a candy store. He may hop from helping a group with an AI accelerator one day to helping another team sort through a complex problem in robotics the next.

“I have one of the most fun jobs in the company if not in the world because I get to help shape the future,” he said.

The keynote is just one of more than 220 sessions at GTC China. All the sessions are free and most are conducted in Mandarin.

Panel, Startup Showcase at GTC China

Following the keynote, a panel of senior NVIDIA executives will discuss how the company’s technologies in AI, data science, healthcare and other fields are being adopted in China.

The event also includes a showcase of a dozen top startups in China, hosted by NVIDIA Inception, an acceleration program for AI and data science startups.

Companies participating in GTC China include Alibaba, AWS, Baidu, ByteDance, China Telecom, Dell Technologies, Didi, H3C, Inspur, Kuaishou, Lenovo, Microsoft, Ping An, Tencent, Tsinghua University and Xiaomi.

Stuttgart’s supercomputer center has been cruising down the autobahn of high performance computing like a well-torqued coupe, and now it’s making a pitstop for some AI fuel.

Germany’s High-Performance Computing Center Stuttgart (HLRS), one of Europe’s largest supercomputing centers, has tripled the size of its staff and increased its revenues from industry collaborations 20x since Michael Resch became director in 2002. In the past year, much of the growth has come from interest in AI.

With demand for machine learning on the rise, HLRS signed a deal to add 192 NVIDIA Ampere architecture GPUs linked on NVIDIA Mellanox InfiniBand network to its Hawk supercomputer based on an Apollo system from Hewlett Packard Enterprise.

Hawk Flies to New Heights

The GPUs will propel what’s already ranked as the world’s 16th largest system to new heights. In preparation for the expansion, researchers are gearing up AI projects that range from predicting the path of the COVID-19 pandemic to the science behind building better cars and planes.

“Humans can create huge simulations, but we can’t always understand all the data — the big advantage of AI is it can work through the data and see its consequences,” said Resch, who also serves as a professor at the University of Stuttgart with a background in engineering, computer science and math.

The center made its first big leap into AI last year when it installed a Cray CS-Storm system with more than 60 NVIDIA GPUs. It is already running AI programs that analyze market data for Mercedes-Benz, investment portfolios for a large German bank and a music database for a local broadcaster.

“It turned out to be an extremely popular system because there’s a growing community of people who understand AI has a benefit for them,” Resch said of the system now running at near capacity. “By the middle of this year it was clear we had to expand to cover our growing AI requirements,” he added.

The New Math: HPC+AI

The future for the Stuttgart center, and the HPC community generally, is about hybrid computing where CPUs and GPUs work together, often to advance HPC simulations with AI.

“Combining the two is a golden bullet that propels us into a better future for understanding problems,” he said.

For example, one researcher at the University of Stuttgart will use data from as many as 2 billion simulations to train neural networks that can quickly and economically evaluate metal alloys. The AI model it spawns could run on a PC and help companies producing sheet metal choose the best alloys for, say, a car door.

“This is extremely helpful in situations where experimentation is difficult or costly,” he said.

And it’s an apropos app for the center situated in the same city that’s home to the headquarters of both Mercedes and Porsche.

In the Flow with Machine Learning

A separate project in fluid dynamics will take a similar approach.

A group from the university will train neural networks on data from highly accurate simulations to create an AI model that can improve analysis of turbulence. It’s a critical topic for companies such as Airbus that are collaborating with HLRS on efforts to mine the aerospace giant’s data on airflow.

The Stuttgart center also aims to use AI as part of a European research project to predict when hospital beds could fill up in intensive-care units amid the pandemic. The project started before the coronavirus hit, but it accelerated in the wake of COVID-19.

Tracking the Pandemic with AI

One of the project’s goals is to give policy makers a four-week window to respond before hospitals would reach their capacity.

“It’s a critical question with so many people dying — we’ve seen scenarios in places like Italy, New York and Wuhan where ICUs filled up in the first weeks of pandemic,” Resch said.

“So, we will conduct simulations and predictions of the outlook for the pandemic over the next weeks and months, and GPUs will be extremely helpful for that,” he added.

It’s perhaps the highest profile of many apps now in the pipeline for the GPU-enhanced engine that will propel Stuttgart’s researchers further down the road on their journey into AI.

It can take a puppy weeks to learn that certain kinds of behaviors will result in a yummy treat, extra cuddles or a belly rub — and that other behaviors won’t. With a system of positive reinforcement, a pet pooch will in time anticipate that chasing squirrels is less likely to be rewarded than staying by their human’s side.

Deep reinforcement learning, a technique used to train AI models for robotics and complex strategy problems, works off the same principle.

In reinforcement learning, a software agent interacts with a real or virtual environment, relying on feedback from rewards to learn the best way to achieve its goal. Like the brain of a puppy in training, a reinforcement learning model uses information it’s observed about the environment and its rewards, and determines which action the agent should take next.

To date, most researchers have relied on a combination of CPUs and GPUs to run reinforcement learning models. This means different parts of the computer tackle different steps of the process — including simulating the environment, calculating rewards, choosing what action to take next, actually taking action, and then learning from the experience.

But switching back and forth between CPU cores and powerful GPUs is by nature inefficient, requiring data to be transferred from one part of the system’s memory to another at multiple points during the reinforcement learning training process. It’s like a student who has to carry a tall stack of books and notes from classroom to classroom, plus the library, before grasping a new concept.

With Isaac Gym, NVIDIA developers have made it possible to instead run the entire reinforcement learning pipeline on GPUs — enabling significant speedups and reducing the hardware resources needed to develop these models.

Here’s what this breakthrough means for the deep reinforcement learning process, and how much acceleration it can bring developers.

Reinforcement Learning on GPUs: Simulation to Action 

When training a reinforcement learning model for a robotics task — like a humanoid robot that walks up and down stairs — it’s much faster, safer and easier to use a simulated environment than the physical world. In a simulation, developers can create a sea of virtual robots that can quickly rack up thousands of hours of experience at a task.

If tested solely in the real world, a robot in training could fall down, bump into or mishandle objects — causing potential damage to its own machinery, the object it’s interacting with or its surroundings. Testing in simulation provides the reinforcement learning model a space to practice and work out the kinks, giving it a head start when shifting to the real world.

In a typical system today, the NVIDIA PhysX simulation engine runs this experience-gathering phase of the reinforcement learning process on NVIDIA GPUs. But for other steps of the training application, developers have traditionally still used CPUs.

A key part of reinforcement learning training is conducting what’s known as the forward pass: First, the system simulates the environment, records a set of observations about the state of the world and calculates a reward for how well the agent did.

The recorded observations become the input to a deep learning “policy” network, which chooses an action for the agent to take. Both the observations and the rewards are stored for use later in the training cycle.

Finally, the action is sent back to the simulator so that the rest of the environment can be updated in response.

After several rounds of these forward passes, the reinforcement learning model takes a look back, evaluating whether the actions it chose were effective or not. This information is used to update the policy network, and the cycle begins again with the improved model.

GPU Acceleration with Isaac Gym 

To eliminate the overhead of transferring data back and forth from CPU to GPU during this reinforcement learning training cycle, NVIDIA researchers have developed an approach to run every step of the process on GPUs. This is Isaac Gym, an end-to-end training environment, which includes the PhysX simulation engine and a PyTorch tensor-based API.

Isaac Gym makes it possible for a developer to run tens of thousands of environments simultaneously on a single GPU. That means experiments that previously required a data center with thousands of CPU cores can in some cases be trained on a single workstation.

Decreasing the amount of hardware required makes reinforcement learning more accessible to individual researchers who don’t have access to large data center resources. It can also make the process a lot faster.

A simple reinforcement learning model tasked with getting a humanoid robot to walk can be trained in just a few minutes with Isaac Gym. But the impact of end-to-end GPU acceleration is most useful for more challenging tasks, like teaching a complex robot hand to manipulate a cube into a specific position.

This problem requires significant dexterity by the robot, and a simulation environment that involves domain randomization, a mechanism that allows the learned policy to more easily transfer to a real-world robot.

Research by OpenAI tackled this task with a cluster of more than 6,000 CPU cores plus multiple NVIDIA Tensor Core GPUs — and required about 30 hours of training for the reinforcement learning model to succeed at the task 20 times in a row using a feed-forward network model.

Using just one NVIDIA A100 GPU with Isaac Gym, NVIDIA developers were able to achieve the same level of success in around 10 hours — a single GPU outperforming an entire cluster by a factor of 3x.

To learn more about Isaac Gym, visit our developer news center.

Video above shows a cube manipulation task trained by Isaac Gym on a single NVIDIA A100 GPU and rendered in NVIDIA Omniverse.

Anima Anandkumar is setting a personal record this week with seven of her team’s research papers accepted to NeurIPS 2020.

The 34th annual Neural Information Processing Systems conference is taking place virtually from Dec. 6-12. The premier event on neural networks, NeurIPS draws thousands of the world’s best researchers every year.

Anandkumar, NVIDIA’s director of machine learning research and Bren professor at CalTech’s CMS Department, joined AI Podcast host Noah Kravitz to talk about what to expect at the conference, and to explain what she sees as the future of AI.

The papers that Anandkumar and her teams at both NVIDIA and CalTech will be presenting are focused on topics including how to design more robust priors that improve network perception and how to create useful benchmarks to evaluate where neural networks need to improve.

In terms of what Anandkumar is focused on going forward, she continues to work on the transition from supervised to unsupervised and self-supervised learning, which she views as the key to next-generation AI.

Key Points From This Episode:

• Anandkumar explains how her interest in AI grew from a love of math at a young age as well as influence from her family — her mother was an engineer and her grandfather a math teacher. Her family was also the first in their city to have a CNC machine — an automated machine, such as a drill or lathe, controlled by a computer — which sparked an interest in programming.
• Anandkumar was instrumental in spearheading the development of tensor algorithms, which are crucial in achieving massive parallelism in large-scale AI applications. That’s one reason for her enthusiasm for NeurIPS, which is not constrained by a particular domain but focused more on improving algorithm development.

Tweetables:

“How do we ensure that everybody in the community is able to get the best benefits from the current AI and can contribute in a meaningful way?” — Anima Anandkumar [2:44]

“Labs like NVIDIA Research are thinking about, ‘Okay, where do we go five to 10 years and beyond from here?’” — Anima Anandkumar [11:16]

“What I’m trying to do is bridge this gap [between academia and industry] so that my students and collaborators are getting the best of both worlds” — Anima Anandkumar [23:54]

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Whether talking with banks, cell phone providers or insurance companies, customers often encounter AI-powered voice interfaces to direct their calls to the right department.

But these interfaces typically are limited to understanding certain keywords. Onvego, an Israel-based startup, is working to make these systems understand what you say, no matter how you say it.

Before starting Onvego, the company’s founders created a mobile speech apps platform to assist blind people. Now they’re creating pre-built AI models for such use cases as accepting or requesting payments, scheduling appointments or booking reservations.

Onvego is a member of NVIDIA Inception, a program that accelerates AI and data science startups with go-to-market support, expertise and technology.

The company’s AI enables enterprises to easily build their own conversational interfaces in 10 different languages, with more on the way. Its technology already powers Israel’s toll road payment systems, enabling drivers to pay with their voice.

“Say the customer said, ‘I want to pay my bill.’ The system has to understand what that means,” said Alon Buchnik, CTO and co-founder of Onvego. “Once it does, it sends that information back to the speech machine, where logic is applied.”

The system then walks the driver through the payment process. Onvego’s AI also powers two emergency road services providers in Israel, providing AI-powered answers to drivers in need.

“The speech machine understands exactly what the problem is,” said Buchnik. “It understands if it needs to send a tow truck or just a technician.”

In Search of Ubiquity

Road-related applications are just the tip of the iceberg for Onvego. The company envisions its technology being inside everything from coffee machines to elevators. Along those lines, it’s forged partnerships with GE, GM, Skoda, Amazon and numerous other companies.

For instance, Onvego’s AI is being incorporated into a line of elevators, enabling the manufacturer to provide a conversational voice interface for users.

With the COVID-19 pandemic raging around the globe, Buchnik believes the company’s no-touch technology, such as activating elevators by voice only, can deliver an added benefit by reducing transmission of the virus.

But Onvego’s most-ambitious undertaking may be its contact call center technology. The company has developed an application, powered by NVIDIA GPUs, that’s designed to do the work of an entire call center operation.

It runs as a cloud-based service as well as enterprise on-premises solution that would provide real-time natural language call center support for IoT devices at the network’s edge, even where there’s no internet connectivity.

GPUs at the Core

Buchnik said that while it would be possible for the Onvego call center application to answer 50 simultaneous calls without GPUs, “it would require a huge CPU” to do so. “For the GPU, it’s nothing,” he said.

Onvego also uses a CUDA decoder so developers can access decoding capabilities on the GPU.

Training of the company’s automatic speech recognition models occurs on NVIDIA GPU-powered instances from AWS or Azure, which Onvego acquired through NVIDIA Inception.

Aside from its efforts to expand the use of its technology, Onvego is focused on the standalone container for locations at the edge or completely independent from the network. They plan to run it on an NVIDIA Jetson Nano.

The idea of providing intelligent natural language interfaces to people wherever they’re needed is providing Buchnik and his team with all the motivation they need.

“This is our vision,” he said. “This is where we want to be.”

Buchnik credits the NVIDIA Inception program for providing the company access to the best AI experts, top technical resources and support, and access to a large marketing program with strong positioning in different market verticals.

By using the NVIDIA resources and platforms, Onvego is hoping to promote its intelligent voice solutions to markets and industries that it has not yet reached.

Bill Dally — one of the world’s foremost computer scientists and head of NVIDIA’s research efforts — will deliver the keynote address during GTC China, the latest event in the world’s premier conference series focused on AI, deep learning and high performance computing.

Registration is not required to view the keynote, which will take place on Dec. 14, at 6 p.m. Pacific time (Dec. 15, 10 a.m. China Standard time). GTC China is a free, online event, running Dec. 15-19.

Tens of thousands of attendees are expected to join the event, with thousands more tuning in to hear Dally speak on the latest innovations in AI, graphics, HPC, healthcare, edge computing and autonomous machines. He will also share new research in the areas of AI inference, silicon photonics, and GPU cluster acceleration.

In a career spanning nearly four decades, Dally has pioneered many of the fundamental technologies underlying today’s supercomputer and networking architectures. As head of NVIDIA Research, he leads a team of more than 200 around the globe who are inventing technologies for a wide variety of applications, including AI, HPC, graphics and networking.

Prior to joining NVIDIA as chief scientist and senior vice president of research in 2009, he chaired Stanford University’s computer science department.

Dally is a member of the National Academy of Engineering and a fellow of the American Academy of Arts & Sciences, the Institute of Electrical and Electronics Engineers and the Association for Computing Machinery. He’s written four textbooks, published more than 250 papers and holds over 120 patents, and has received the IEEE Seymour Cray Award, ACM Eckert-Mauchly Award and ACM Maurice Wilkes Award.

Following Dally’s keynote, four senior NVIDIA executives will describe how the company’s latest breakthroughs in AI, data science and healthcare are being adopted in China. The panel discussion will take place on Monday, Dec. 14, at 7:10 p.m. Pacific (Dec. 15 at 11:10 a.m. CST).

GTC China Highlights

GTC is the premier conference for developers to strengthen their skills on a wide range of technologies. It will include 220+ live and on-demand sessions and enable attendees to ask questions and interact with experts.

Many leading organizations will participate, including Alibaba, AWS, Baidu, ByteDance, China Telecom, Dell Technologies, Didi, Hewlett Packard Enterprise, Inspur, Kuaishou, Lenovo, Microsoft, Ping An, Tencent, Tsinghua University and Xiaomi.

Certified instructors will provide virtual training for hundreds of participants in the NVIDIA Deep Learning Institute. DLI seats are currently sold out.

NVIDIA Inception, an acceleration program for AI and data science startups, will host 12 leading Chinese startups in the NVIDIA Inception Startup Showcase. Attendees will have the opportunity to see presentations from the 12 CXOs, whose companies were selected by winning a vote among more than 40 participating startups.

For more details and to register for GTC China at no charge, visit www.nvidia.cn/gtc/.

AI is the largest technology force of our time, with the most potential to transform industries. It will bring new intelligence to healthcare, education, automotive, retail and finance, creating trillions of dollars in a new AI economy.

As businesses look ahead to 2021 priorities, now’s a great time to look back at where the world stands on global AI adoption.

Retailers like Walmart and Tesco are mining new AI opportunities for product forecasting, supply chain management, intelligent store installations and predicting consumer buying trends. Healthcare players in the age of COVID are trying to speed scientific research and vaccine development.

Meantime, educators are employing AI to train a data-savvy workforce. And legions of businesses are examining how AI can help them adapt to remote work and distance collaboration.

Yet mainstream adoption of AI continues to skew toward big tech companies, automotive and retail, which are attempting to scale across their organizations instead of investing in skunkwork projects, according to a 2019 McKinsey global survey of about 2,000 organizations.

We asked some of the top experts at NVIDIA where they see the next big things in AI happening as companies parse big data and look for new revenue opportunities. NVIDIA works with thousands of AI-focused startups, ISVs, hardware vendors and cloud companies, as well as companies and research organizations around the world. These broad collaborations offer a bird’s eye view into what’s happening and where.

Here’s what our executives had to say:

CLEMENT FARABET
Vice President, NVIDIA AI Infrastructure

AI as a Compiler: As AI training algorithms get faster, more robust and with richer tooling, AI will become equivalent to a compiler — developers will organize their datasets as code, and use AI to compile them into models.The end state of this is a large ecosystem of tooling/platforms (just like today’s tools for regular software) to enable more and more non-experts to “program” AIs. We’re partially there, but I think the end state will look very different than where we are today — think compilation in seconds to minutes instead of days of training. And we’ll have very efficient tools to organize data, like we do for code via git today.

AI as a Driver: AI will be assisting most vehicles to move around the physical world and continuously learning from their environments and co-pilots (human drivers) to improve, on their way to becoming fully independent drivers. The value of this is there today and will only grow larger. The end state is commoditized level 4 autonomous vehicles, relying on cheap enough sensor platforms.

BRYAN CATANZARO
Vice President, NVIDIA Applied Deep Learning Research

Conversational AI: Chabots might seem like so-last-decade when it comes to video games designed to take advantage of powerful PC graphics cards and CPUs in today’s computers. AI for some time has been used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters. Conversational AI will take gameplay further by allowing real-time interaction via voice to flesh out character-driven approaches. When your in-game enemies start to talk and think like you, watch out.

Multimodal Synthesis: Can a virtual actor win an Academy Award? Advances in multimodal synthesis — the AI-driven art of creating speech and facial expressions from data — will be able to create characters that look and sound as real as a Meryl Streep or Dwayne Johnson.

Remote Work: AI solutions will make working from home easier and more reliable (and perhaps more pleasant) through better videoconferencing, audio quality and auto-transcription capabilities.

ANIMA ANANDKUMAR
Director of ML Research, NVIDIA, and Bren Professor at Caltech

Embodied AI: The mind and body will start coming together. We will see greater adaptivity and agility in our robots as we train them to do more complex and diverse tasks.The role of high fidelity simulations is critical here to overcome the dearth of real data.

AI4Science: AI will continue to get integrated into scientific applications at scale. Traditional solvers and pipelines will be ultimately completely replaced with AI to achieve as high as a 1000x increase in speed. This will require combining deep learning with domain-specific knowledge and constraints.

ALISON LOWNDES
Artificial Intelligence, NVIDIA Developer Relations

Democratized AI: The more people who have access to the dataset, and who are trained in how to mine it, the more innovations that will emerge. Nations will begin to solidify AI strategies, while universities and colleges will work in partnership with private industry to create more end-user mobile applications and scientific breakthroughs.

Simulation AI: “What does (insert AI persona here) think? The AI-based simulation increasingly will mimic human intelligence, with the ability to reason, problem solve and make decisions. You’ll see increased use here for both AI research and design and engineering.

AI for Earth Observation (AI4EO): It may be a small world after all, but there’s still a lot we don’t know about Mother Earth. A global AI framework would process satellite data in orbit and on the ground for rapid, if not real-time, actionable knowledge. It could create new monitoring solutions, especially for climate change, disaster response and biodiversity loss.

KIMBERLY POWELL
Vice President & General Manager, NVIDIA Healthcare

Federated Learning: The clinical community will increase their use of federated learning approaches to build robust AI models across various institutions, geographies, patient demographics and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with. As an added bonus, researchers can collaborate on AI model creation without sharing confidential patient information. Federated learning is also beneficial for building AI models for areas where data is scarce, such as for pediatrics and rare diseases.

AI-Driven Drug Discovery: The COVID-19 pandemic has put a spotlight on drug discovery, which encompasses microscopic viewing of molecules and proteins, sorting through millions of chemical structures, in-silico methods for screening, protein-ligand interactions, genomic analysis, and assimilating data from structured and unstructured sources. Drug development typically takes over 10 years, however, in the wake of COVID, pharmaceutical companies, biotechs and researchers realize that acceleration of traditional methods is paramount. Newly created AI-powered discovery labs with GPU-accelerated instruments and AI models will expedite time to insight — creating a computing time machine.

Smart Hospitals: The need for smart hospitals has never been more urgent. Similar to the experience at home, smart speakers and smart cameras help automate and inform activities. The technology, when used in hospitals, will help scale the work of nurses on the front lines, increase operational efficiency and provide virtual patient monitoring to predict and prevent adverse patient events.

CHARLIE BOYLE
Vice President & General Manager, NVIDIA DGX Systems

Shadow AI: Managing AI across an organization will be a hot-button internal issue if data science teams implement their own AI platforms and infrastructure without IT involvement. Avoiding shadow AI requires a centralized enterprise IT approach to infrastructure, tools and workflow, which ultimately enables faster, more successful deployments of AI applications.

AI Center of Excellence: Companies have scrambled over the past 10 years to snap up highly paid data scientists, yet their productivity has been lower than expected because of a lack of supportive infrastructure. More organizations will speed the investment return on AI by building centralized, shared infrastructure at supercomputing scale. This will facilitate the grooming and scaling of data science talent, the sharing of best practices and accelerate the solving of complex AI problems.

Hybrid Infrastructure: The Internet of Things will lead decision-makers to adopt a mixed AI approach, using the public cloud (AWS, Azure, Oracle Cloud,Google Cloud) and private clouds (on-premises servers) to deliver applications faster (with lower latency, in industry parlance) to customers and partners while maintaining security by limiting the amount of sensitive data shared across networks. Hybrid approaches will also become more popular as governments adopt strict data protection laws governing the use of personal information.

KEVIN DEIERLING
Senior Vice President, NVIDIA Networking

Accelerating Change in the Data Center: Security and management will be offloaded from CPUs into GPUs, SmartNICs and programmable data processing units to deliver expanded application acceleration to all enterprise workloads and provide an extra layer of security. Virtualization and scalability will be faster, while CPUs will run apps faster and offer accelerated services.

AI as a Service: Companies that are reluctant to spend time and resources investing in AI, whether for financial reasons or otherwise, will begin turning to third-party providers for experimentation. AI platform companies and startups will become key partners by providing access to software, infrastructure and potential partners.

Transformational 5G: Companies will begin defining what “the edge” is. Autonomous driving is essentially a data center in the car, allowing the AI to make instantaneous decisions, while also being able to report back for training. You’ll see the same thing with robots in the warehouse and the workplace, where there will be inference learning at the edge and training at the core. Just like 4G spawned transformational change in transportation with Lyft and Uber, 5G will bring transformational deals and capabilities. It won’t happen all at once, but you’ll start to see the beginnings of companies seeking to take advantage of the confluence of AI, 5G and new computing platforms.

SANJA FIDLER
Director AI, NVIDIA and Professor Vector Institute for Artificial Intelligence

AI for 3D Content Creation: AI will revolutionize the content creation process, offering smart tools to reduce mundane work and to empower creativity. In particular, creating 3D content for architecture, gaming, films and VR/AR has been very laborious: games like Call of Duty take at least a year to make, even with hundreds of people involved and millions budgeted.

With AI, one will be able to build virtual cities by describing them in words, and see virtual characters come to life to converse and behave in desired ways without needing to hard code the behavior. Creating a 3D asset will  become as easy as snapping a photo, and modernizing and restyling old games will happen with the click of a button.

AI for Robotics Simulation: Testing robots in simulated environments is key for safety-critical applications such as self-driving cars or operating robots. Deep learning will bring simulation to the next level, by learning to mimic the world from data, both in terms of creating 3D environments, simulating diverse behaviors, simulating and re-simulating new or observed road scenarios, and simulating the sensors in ways that are closer to reality.

An Opportunity for Reinvention

To accomplish any or all of these tasks, organizations will have to move more quickly for internal alignment. For example, 72 percent of big AI adopters in the McKinsey survey say their companies’ AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies. Similarly, 65 percent of the high performers report having a clear data strategy that supports and enables AI, compared with 20 percent from other companies.

Even as the global pandemic creates uncertainty around the world, 2021 will be a time of reinvention as players large and small leverage AI to improve on their business models. More companies will operationalize AI as early results prove promising enough to commit more resources to their efforts.

What are the fundamental laws that govern our universe? How did the matter in the universe today get there? What exactly is dark matter?

The questions may be eternal, but no human scientist has an eternity to answer them.

Now, thanks to NVIDIA technology and cutting-edge AI, the more than 1,000 collaborators from 26 countries working on the Belle II particle physics experiment are able to learn more about these big questions, faster.

The Belle II detector, based just north of Tokyo, reproduces the particles created during the early universe by smashing high-energy electrons and anti-electrons together.

These collisions generate a serious amount of data. Researchers will make high-precision recordings of hundreds of billions of collisions over the experiment’s lifetime. Sifting through all this data, without sacrificing the detailed information needed for high-precision measurements, is a daunting task.

To reconstruct the way individual particles, detected at Belle II, decayed from larger groups of particles, researchers turned to AI, says James Kahn from the Karlsruhe Institute of Technology, or KIT, a Belle II researcher and AI consultant with Helmholtz AI, a German public research platform for applied AI.

“Given the successes of AI and its ability to learn by example on large volumes of data, this is the perfect place to apply it,” Kahn said.

And to accelerate that AI, they’re using the NVIDIA Ampere architecture’s multi-instance GPU technology, built into the NVIDIA A100 GPU.

Physics Meets the A100

Kahn’s team was able to get early access to the “fresh out of the oven” NVIDIA DGX A100, a compact system packing 5 petaflops of AI computing power.

It’s among the first in Europe, and the first connected via InfiniBand high-speed interconnect technology. It was installed at KIT thanks to the high-performance computing operations team at the Steinbuch Center for Computing.

This close connection among the AI consultant team, international scientists and the HPC operations team will be a benefit for future research.

“We are really happy to see that only a few hours after we had the DGX A100 up and running, scientific analyses were already being performed,” said Jennifer Buchmüller, HPC core facility leader at KIT.

There’s more to come: HoreKa, the next supercomputer at KIT, will be equipped with more than 740 NVIDIA A100 GPUs.

A New View on Particle Decays

All of this helps Kahn and his team accelerate a new approach developed at KIT in collaboration with researchers from the nearby University of Strasbourg.

By designing a new representation of particle decays, or how unstable subatomic particles fall apart, Kahn’s team has been able to use a specialized neural network, known as a graph neural network, to automate the reconstruction of the particle decays from the individual particles detected by Belle II.

“We realized we could re-express particle decays in terms of the detected particles’ relations alone,” said Kahn. “This was the key ingredient to enable a full, end-to-end AI solution.”

The team has already demonstrated this technique’s success on a selection of specially designed simulations of particle decays, and recently scaled up to simulations of the interactions occurring at Belle II.

Scaling up, however, required resources that could handle both the volume of data and the large neural networks trained on it.

To do so they split up the GPUs using the multi-instance GPU technology — which allows a single GPU to perform multiple tasks simultaneously — to perform a spread-and-search of the network hyperparameters.

“Architecture searches which took days could now be completed in a matter of hours,” Kahn said.

The result: more time for more science, and for more of those eternal questions to be asked, and answered.