Posts in category: nVidia AI

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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NVIDIA’s Jonah Alben Talks AI

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Demystifying AI with NVIDIA’s Will Ramey

One of NVIDIA’s best explainers, Will Ramey, provides an introduction to the AI boom and the key concepts behind it. Ramey is the senior director and global head of developer programs at NVIDIA.

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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.

NVIDIA Research’s latest AI model is a prodigy among generative adversarial networks. Using a fraction of the study material needed by a typical GAN, it can learn skills as complex as emulating renowned painters and recreating images of cancer tissue.

By applying a breakthrough neural network training technique to the popular NVIDIA StyleGAN2 model, NVIDIA researchers reimagined artwork based on fewer than 1,500 images from the Metropolitan Museum of Art. Using NVIDIA DGX systems to accelerate training, they generated new AI art inspired by the historical portraits.

The technique — called adaptive discriminator augmentation, or ADA — reduces the number of training images by 10-20x while still getting great results. The same method could someday have a significant impact in healthcare, for example by creating cancer histology images to help train other AI models.

“These results mean people can use GANs to tackle problems where vast quantities of data are too time-consuming or difficult to obtain,” said David Luebke, vice president of graphics research at NVIDIA. “I can’t wait to see what artists, medical experts and researchers use it for.”

The research paper behind this project is being presented this week at the annual Conference on Neural Information Processing Systems, known as NeurIPS. It’s one of a record 28 NVIDIA Research papers accepted to the prestigious conference.

This new method is the latest in a legacy of GAN innovation by NVIDIA researchers, who’ve developed groundbreaking GAN-based models for the AI painting app GauGAN, the game engine mimicker GameGAN, and the pet photo transformer GANimal. All are available on the NVIDIA AI Playground.

The Training Data Dilemma

Like most neural networks, GANs have long followed a basic principle: the more training data, the better the model. That’s because each GAN consists of two cooperating networks — a generator, which creates synthetic images, and a discriminator, which learns what realistic images should look like based on training data.

The discriminator coaches the generator, giving pixel-by-pixel feedback to help it improve the realism of its synthetic images. But with limited training data to learn from, a discriminator won’t be able to help the generator reach its full potential — like a rookie coach who’s experienced far fewer games than a seasoned expert.

It typically takes 50,000 to 100,000 training images to train a high-quality GAN. But in many cases, researchers simply don’t have tens or hundreds of thousands of sample images at their disposal.

With just a couple thousand images for training, many GANs would falter at producing realistic results. This problem, called overfitting, occurs when the discriminator simply memorizes the training images and fails to provide useful feedback to the generator.

In image classification tasks, researchers get around overfitting with data augmentation, a technique that expands smaller datasets using copies of existing images that are randomly distorted by processes like rotating, cropping or flipping — forcing the model to generalize better.

But previous attempts to apply augmentation to GAN training images resulted in a generator that learned to mimic those distortions, rather than creating believable synthetic images.

A GAN on a Mission

NVIDIA Research’s ADA method applies data augmentations adaptively, meaning the amount of data augmentation is adjusted at different points in the training process to avoid overfitting. This enables models like StyleGAN2 to achieve equally amazing results using an order of magnitude fewer training images.

As a result, researchers can apply GANs to previously impractical applications where examples are too scarce, too hard to obtain or too time-consuming to gather into a large dataset.

Different editions of StyleGAN have been used by artists to create stunning exhibits and produce a new manga based on the style of legendary illustrator Osamu Tezuka. It’s even been adopted by Adobe to power Photoshop’s new AI tool, Neural Filters.

With less training data required to get started, StyleGAN2 with ADA could be applied to rare art, such as the work by Paris-based AI art collective Obvious on African Kota masks.

Another promising application lies in healthcare, where medical images of rare diseases can be few and far between because most tests come back normal. Amassing a useful dataset of abnormal pathology slides would require many hours of painstaking labeling by medical experts.

Synthetic images created with a GAN using ADA could fill that gap, generating training data for another AI model that helps pathologists or radiologists spot rare conditions on pathology images or MRI studies. An added bonus: With AI-generated data, there are no patient data or privacy concerns, making it easier for healthcare institutions to share datasets.

NVIDIA Research at NeurIPS

The NVIDIA Research team consists of more than 200 scientists around the globe, focusing on areas including AI, computer vision, self-driving cars, robotics and graphics. Over two dozen papers authored by NVIDIA researchers will be highlighted at NeurIPS, the year’s largest AI research conference, taking place virtually from Dec. 6-12.

Check out the full lineup of NVIDIA Research papers at NeurIPS.

Main images generated by StyleGAN2 with ADA, trained on a dataset of fewer than 1,500 images from the Metropolitan Museum of Art Collection API.

Academic researchers are developing AI to solve challenging problems with everything from agricultural robotics to autonomous flying machines.

To help AI research like this make the leap from academia to commercial or government deployment, NVIDIA today announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments.

The program will initially focus on robotics and autonomous machines. Worldwide spending on robotics systems and drones is forecast to reach $241 billion by 2023, an 88 percent increase from the $128.7 billion in spending expected for 2020, according to IDC. The program will also extend to other domains such as Data Science, NLP, Speech and Conversational AI in the months ahead.

The new program will support researchers and the organizations they work with in rolling out the next generation of applications developed on NVIDIA AI platforms, including the Jetson developer kits and SDKs like DeepStream and Isaac.

Researchers working with sponsoring organizations will also gain support from NVIDIA through technical guidance, hardware grants, funding, grant application support, AI training programs, not to mention networking and marketing opportunities.

NVIDIA is now accepting applications to the program from researchers working to apply robotics and AI for automation in collaboration with enterprises seeking to deploy new technologies in the market.

Accelerating and Deploying AI Research

The NVIDIA Applied Research Accelerator Program’s first group of participants have already demonstrated AI capabilities meriting further development for agriculture, logistics and healthcare.

• The University of Florida is developing AI applications for smart sprayers used in agriculture, and working with Chemical Containers Inc. to deploy AI on machines running NVIDIA Jetson to reduce the amount of plant protection products applied to tree crops.
• The Institute for Factory Automation and Production Systems at Friedrich-Alexander-University Erlangen-Nuremberg, based in Germany, is working with materials handling company KION and the intralogistics research association IFL to design drones for warehouse autonomy using NVIDIA Jetson.
• The Massachusetts Institute of Technology is developing AI applications for disinfecting surfaces with UV-C light using NVIDIA Jetson. It’s also working with Ava Robotics to deploy autonomous disinfection on robots to minimize human supervision and additional risk of exposure to COVID-19.

Applied Research Accelerator Program Benefits  

NVIDIA offers hardware grants along with funding in some cases for academic researchers who can demonstrate AI feasibility in practical applications. The program also provides letters of support for third-party grant applications submitted by researchers.

Members will also have access to technical guidance on using NVIDIA platforms, including Jetson, as well as Isaac and DeepStream.

Membership in the new program includes access to training courses via the Deep Learning Institute to help researchers master a wide range of AI technologies.

NVIDIA also offers researchers opportunities to present and network at the GPU Technology Conferences.

Interested researchers can apply today for the Applied Research Accelerator Program.

Swedish startup Einride has rejigged the big rig for highways around the world.

The autonomous truck maker launched the next generation of its cab-less autonomous truck, known as the Pod, with new, advanced functionality and pricing. The AI vehicles, which will be commercially available worldwide, will be powered by the latest in high-performance, energy-efficient compute — NVIDIA DRIVE AGX Orin.

These scalable self-driving haulers will begin to hit the road in 2023, with a variety of models available to customers around the world.

Autonomous trucks are always learning, taking in vast amounts of data to navigate the unpredictability of the real world, from highways to crowded ports. This rapid processing requires centralized, high-performance AI compute.

With the power of AI, these vehicles can easily rise to the demands of the trucking industry. The vehicles can operate 24 hours a day, improving delivery times. And, with increased efficiency, they can slash the annual cost of logistics in the U.S. by 45 percent, according to experts at McKinsey.

Einride’s autonomous pods and trucks are built for every type of route. They can automate short, routine trips like the loading and unloading of containers on cargo ships and managing port operations, as well as autonomously drive on the highway, dramatically streamlining the shipping and logistics.

A New Pod Joins the Squad

The latest Einride Pod features a refined design that balances sleek features with the practical requirements of wide-scale production.

Its rounded edges give it an aerodynamic shape for greater efficiency and performance, without sacrificing cargo space. The Pod’s lighting system — which includes headlights, tail lights and indicators — provides a signature look while improving visibility for road users.

The cab-less truck comes in a range of variations, depending on use case. The AET 1 (Autonomous Electric Transport) model is purpose-built for closed facilities with dedicated routes — such as a port or loading bay. The AET 2 can handle fenced-in areas as well as short-distance public roads between destinations.

The AET 3 and AET 4 vehicles are designed for fully autonomous operation on backroads and highways, with speeds of up to 45 km per hour.

Einride is currently accepting reservations for AET 1 and AET 2, with others set to ship starting in 2022.

Trucking Ahead with Orin

The Einride Pod is able to achieve its scalability and autonomous functionality by leveraging the next generation in AI compute.

NVIDIA Orin is a system-on-a-chip born out of the data center, consisting of 17 billion transistors and the result of four years of R&D investment. It achieves 200 TOPS — nearly 7x the performance of the previous generation SoC Xavier — and is designed to handle the large number of applications and deep neural networks that run simultaneously in autonomous trucks, while achieving systematic safety standards such as ISO 26262 ASIL-D.

This massive compute capability ensures the Einride Pod is continuously learning, expanding the environments and situations in which it can operate autonomously.

These next-generation electric, self-driving freight transport vehicles built on NVIDIA DRIVE are primed to safely increase productivity, improve utilization, reduce emissions and decrease the world’s dependence on fossil fuels.

It’s time to bring krisp graphics to stick figure drawings.

Creative studio SoKrispyMedia, started by content creators Sam Wickert and Eric Leigh, develops short videos blended with high-quality visual effects. Since publishing one of their early works eight years ago on YouTube, Chalk Warfare 1, the team has regularly put out short films that showcase engaging visual effects and graphics — including Stick Figure Battle, which has nearly 25 million views.

Now, the Stick Figure saga continues with SoKrispyMedia’s latest, Stick Figure War, which relies on real-time rendering for photorealistic results, as well as improved creative workflows.

With real-time rendering, SoKrispyMedia worked more efficiently as they could see the final results quickly, and have more time for iterations so they could ensure the visuals looked exactly how they wanted — from stick figures piloting paper airplanes to robots fighting skeletons in textbooks.

The team enhanced their virtual production process by using Unreal Engine and a Dell Precision 7750 mobile workstation featuring an NVIDIA Quadro RTX 5000 GPU. Adding to this mix high-quality cameras and DaVinci Resolve software from Blackmagic Design, SoKrispyMedia produced a short film with higher quality than they ever thought possible.

Real-Time Rendering Sticks Out in Visual Effects

Integrating real-time rendering into their pipelines has allowed SoKrispyMedia to work faster and iterate more quickly. They no longer need to wait hundreds of hours for renders to preview — everything can be produced in real time.

“Looking back at our older videos and the technology we used, it feels like we were writing in pencil, and as the technology evolves, we’re adding more and more colors to our palette,” said Micah Malinics, producer at SoKrispyMedia.

For Stick Figure War, a lot of the elements in the video were drawn by hand, and then scanned and converted into 2D or 3D graphics in Unreal Engine. The creators also developed a stylized filter that allowed them to make certain elements look like cross-hatched drawings.

SoKrispyMedia used Unreal Engine to do real-time rendering for almost the entire film, which enabled them to explore more creative ideas and let their imaginations run wild without worrying about increased render times.

Pushing Creativity Behind the Scenes

While NVIDIA RTX and Unreal Engine have broadened the reach of real-time rendering, Blackmagic Design has made high-quality cameras more accessible so content creators can produce cinematic-quality work at a fraction of the cost.

For Stick Figure War, SoKrispyMedia used Blackmagic URSA Mini G2 for production, Pocket Cinema Camera for pick-up shots and Micro Studio Camera 4K for over-the-head VFX shots. With the cameras, the team could shoot videos at 4K resolution and crop footage without losing any resolution in post-production.

Editing workflows were accelerated as Blackmagic’s DaVinci Resolve utilized NVIDIA GPUs to dramatically speed up playback and performance.

“Five to 10 years ago, making this video would’ve been astronomically difficult. Now we’re able to simply plug the Blackmagic camera directly into Unreal and see final results in front of our eyes,” said Sam Wickert, co-founder of SoKrispyMedia. “Using the Resolve Live feature for interactive and collaborative color grading and editing is just so fast, easy and efficient. We’re able to bring so much more to life on screen than we ever thought possible.”

The SoKrispyMedia team was provided with a Dell Precision 7750 mobile workstation with an RTX 5000 GPU inside, allowing the content creators to work on the go and preview real-time renderings on set. And the Dell workstation’s display provided advanced color accuracy, from working in DaVinci Resolve to rendering previews and final images.

Learn more about the making of SoKrispyMedia’s latest video, Stick Figure War.

Cloud or on premises? That’s the question many organizations ask when building AI infrastructure.

Cloud computing can help developers get a fast start with minimal cost. It’s great for early experimentation and supporting temporary needs.

As businesses iterate on their AI models, however, they can become increasingly complex, consume more compute cycles and involve exponentially larger datasets. The costs of data gravity can escalate, with more time and money spent pushing large datasets from where they’re generated to where compute resources reside.

This AI development “speed bump” is often an inflection point where organizations realize there are opex benefits with on-premises or collocated infrastructure. Its fixed costs can support rapid iteration at the lowest “cost per training run,” complementing their cloud usage.

Conversely, for organizations whose datasets are created in the cloud and live there, procuring compute resources adjacent to that data makes sense. Whether on-prem or in the cloud, minimizing data travel — by keeping large volumes as close to compute resources as possible — helps minimize the impact of data gravity on operating costs.

‘Own the Base, Rent the Spike’ 

Businesses that ultimately embrace hybrid cloud infrastructure trace a familiar trajectory.

One customer developing an image recognition application immediately benefited from a fast, effortless start in the cloud.

As their database grew to millions of images, costs rose and processing slowed, causing their data scientists to become more cautious in refining their models.

At this tipping point — when a fixed cost infrastructure was justified — they shifted training workloads to an on-prem NVIDIA DGX system. This enabled an immediate return to rapid, creative experimentation, allowing the business to build on the great start enabled by the cloud.

The saying “own the base, rent the spike” captures this situation. Enterprise IT provisions on-prem DGX infrastructure to support the steady-state volume of AI workloads and retains the ability to burst to the cloud whenever extra capacity is needed.

It’s this hybrid cloud approach that can secure the continuous availability of compute resources for developers while ensuring the lowest cost per training run.

Delivering the AI Hybrid Cloud with DGX and Google Cloud’s Anthos on Bare Metal

To help businesses embrace hybrid cloud infrastructure, NVIDIA has introduced support for Google Cloud’s Anthos on bare metal for its DGX A100 systems.

For customers using Kubernetes to straddle cloud GPU compute instances and on-prem DGX infrastructure, Anthos on bare metal enables a consistent development and operational experience across deployments, while reducing expensive overhead and improving developer productivity.

This presents several benefits to enterprises. While many have implemented GPU-accelerated AI in their data centers, much of the world retains some legacy x86 compute infrastructure. With Anthos on bare metal, IT can easily add on-prem DGX systems to their infrastructure to tackle AI workloads and manage it the same familiar way, all without the need for a hypervisor layer.

Without the need for a virtual machine, Anthos on bare metal — now generally available — manages application deployment and health across existing environments for more efficient operations. Anthos on bare metal can also manage application containers on a wide variety of performance, GPU-optimized hardware types and allows for direct application access to hardware.

“Anthos on bare metal provides customers with more choice over how and where they run applications and workloads,” said Rayn Veerubhotla, Director of Partner Engineering at Google Cloud. “NVIDIA’s support for Anthos on bare metal means customers can seamlessly deploy NVIDIA’s GPU Device Plugin directly on their hardware, enabling increased performance and flexibility to balance ML workloads across hybrid environments.”

Additionally, teams can access their favorite NVIDIA NGC containers, Helm charts and AI models from anywhere.

With this combination, enterprises can enjoy the rapid start and elasticity of resources offered on Google Cloud, as well as the secure performance of dedicated on-prem DGX infrastructure.

Learn more about Google Cloud’s Anthos.

Learn more about NVIDIA DGX A100.