Posts in category: nVidia AI

Robotaxis are on their way to delivering safer transportation, driving across various landscapes and through starry nights.

This week, Silicon Valley-based self-driving startup Pony.ai announced its next-generation autonomous computing platform, built on NVIDIA DRIVE Orin for high-performance and scalable compute. The centralized system will serve as the brain for a robotaxi fleet of Toyota Sienna multipurpose vehicles (MPVs), marking a major leap forward for the nearly six-year-old company.

The AI compute platform enables multiple configurations for scalable autonomous driving development, all the way to level 4 self-driving vehicles.

“By leveraging the world-class NVIDIA DRIVE Orin SoC, we’re demonstrating our design and industrialization capabilities and ability to develop and deliver a powerful mass-production platform at an unprecedented scale,” said James Peng, co-founder and CEO of Pony.ai, which is developing autonomous systems for both robotaxis and trucks.

The transition to DRIVE Orin has significantly accelerated the company’s plans to deploy safer, more efficient robotaxis, with road testing set to begin this year in China and commercial rollout planned for 2023.

State-of-the-Art Intelligence

DRIVE Orin serves as the brain of autonomous fleets, enabling them to perceive their environment and continuously improve over time.

Born out of the data center, DRIVE Orin achieves 254 trillions of operations per second, or TOPS. It’s 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.

Pony.ai’s DRIVE Orin-based autonomous computing unit features low latency, high performance and high reliability. It also incorporates a robust sensor solution that contains more than 23 sensors, including solid-state lidars, near-range lidars, radars and cameras.

This next-generation, automotive-grade system incorporates redundancy and diversity, maximizing safety while increasing performance and reducing weight and cost over previous iterations.

A Van for All Seasons

The Toyota Sienna MPV is a prime candidate for robotaxi services as it offers flexibility and ride comfort in a sleek package.

Toyota and Pony.ai began co-developing Sienna vehicles purpose-built for robotaxi services in 2019. The custom vehicles feature a dual-redundancy system and better control performance for level 4 autonomous driving capabilities.

The vehicles also debut new concept design cues, including rooftop signaling units that employ different colors and lighting configurations to communicate the robotaxi’s status and intentions.

This dedicated, future-forward design combined with the high-performance compute of NVIDIA DRIVE Orin lays a strong foundation for the coming generation of safer, more efficient robotaxi fleets.

With a new year underway, NVIDIA is helping enterprises worldwide add modern workloads to their mainstream servers using the latest release of the NVIDIA AI Enterprise software suite.

NVIDIA AI Enterprise 1.1 is now generally available. Optimized, certified and supported by NVIDIA, the latest version of the software suite brings new updates including production support for containerized AI with the NVIDIA software on VMware vSphere with Tanzu, which was previously only available on a trial basis. Now, enterprises can run accelerated AI workloads on vSphere, running in both Kubernetes containers and virtual machines with NVIDIA AI Enterprise to support advanced AI development on mainstream IT infrastructure.

Enterprise AI Simplified with VMware vSphere with Tanzu, Coming Soon to NVIDIA LaunchPad

Among the top customer-requested features in NVIDIA AI Enterprise 1.1 is production support for running on VMware vSphere with Tanzu, which enables developers to run AI workloads on both containers and virtual machines within their vSphere environments. This new milestone in the AI-ready platform curated by NVIDIA and VMware provides an integrated, complete stack of containerized software and hardware optimized for AI, all fully managed by IT.

NVIDIA will soon add VMware vSphere with Tanzu support to the NVIDIA LaunchPad program for NVIDIA AI Enterprise, available at nine Equinix locations around the world. Qualified enterprises can test and prototype AI workloads at no charge through curated labs designed for the AI practitioner and IT admin. The labs showcase how to develop and manage common AI workloads like chatbots and recommendation systems, using NVIDIA AI Enterprise and VMware vSphere, and soon with Tanzu.

“Organizations are accelerating AI and ML development projects and VMware vSphere with Tanzu running NVIDIA AI Enterprise easily empowers AI development requirements with modern infrastructure services,” said Matt Morgan, vice president of Product Marketing, Cloud Infrastructure Business Group at VMware. “This announcement marks another key milestone for VMware and NVIDIA in our sustained efforts to help teams leverage AI across the enterprise.”

Growing Demand for Containerized AI Development

While enterprises are eager to use containerized development for AI, the complexity of these workloads requires orchestration across many layers of infrastructure. NVIDIA AI Enterprise 1.1 provides an ideal solution for these challenges as an AI-ready enterprise platform.

“AI is a very popular modern workload that is increasingly favoring deployment in containers. However, deploying AI capabilities at scale within the enterprise can be extremely complex, requiring enablement at multiple layers of the stack, from AI software frameworks, operating systems, containers, VMs, and down to the hardware,” said Gary Chen, research director, Software Defined Compute at IDC. “Turnkey, full-stack AI solutions can greatly simplify deployment and make AI more accessible within the enterprise.”

Domino Data Lab MLOps Validation Accelerates AI Research and Data Science Lifecycle

The 1.1 release of NVIDIA AI Enterprise also provides validation for the Domino Data Lab Enterprise MLOps Platform with VMware vSphere with Tanzu. This new integration enables more companies to cost-effectively scale data science by accelerating research, model development, and model deployment on mainstream accelerated servers.

“This new phase of our collaboration with NVIDIA further enables enterprises to solve the world’s most challenging problems by putting models at the heart of their businesses,” said Thomas Robinson, vice president of Strategic Partnerships at Domino Data Lab. “Together, we are providing every company the end-to-end platform to rapidly and cost-effectively deploy models enterprise-wide.”

New OEMs and Integrators Offering NVIDIA-Certified Systems for NVIDIA AI Enterprise

Amidst the new release of NVIDIA AI Enterprise, the industry ecosystem is expanding with the first NVIDIA-Certified Systems from Cisco and Hitachi Vantara, as well as a growing roster of newly qualified system integrators offering solutions for the software suite.

The first Cisco system to be NVIDIA-Certified for NVIDIA AI Enterprise is the Cisco UCS C240 M6 rack server with NVIDIA A100 Tensor Core GPUs. The two-socket, 2RU form factor can power a wide range of storage and I/O-intensive applications, such as big data analytics, databases, collaboration, virtualization, consolidation and high-performance computing.

“At Cisco we are helping simplify customers’ hybrid cloud and cloud-native transformation. NVIDIA-Certified Cisco UCS servers, powered by Cisco Intersight, deliver the best-in-class AI workload experiences in the market,” said Siva Sivakumar, vice president of product management at Cisco. “The certification of the Cisco UCS C240 M6 rack server for NVIDIA AI Enterprise allows customers to add AI using the same infrastructure and management software deployed throughout their data center.”

The first NVIDIA-Certified System from Hitachi Vantara compatible with NVIDIA AI Enterprise is the Hitachi Advanced Server DS220 G2 with NVIDIA A100 Tensor Core GPUs. The general-purpose, dual-processor server is optimized for performance and capacity, and delivers a balance of compute and storage with the flexibility to power a wide range of solutions and applications.

“For many enterprises, cost is an important consideration when deploying new technologies like AI-powered quality control, recommender systems, chatbots and more,” said Dan McConnell, senior vice president, Product Management at Hitachi Vantara. “Accelerated with NVIDIA A100 GPUs and now certified for NVIDIA AI Enterprise, Hitachi Unified Compute Platform (UCP) solutions using the Hitachi Advanced Server DS220 G2 gives customers an ideal path for affordably integrating powerful AI-ready infrastructure to their data centers.”

A broad range of additional server manufacturers offer NVIDIA-Certified Systems for NVIDIA AI Enterprise. These include Atos, Dell Technologies, GIGABYTE, H3C, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro, all of whose systems feature NVIDIA A100, NVIDIA A30 or other NVIDIA GPUs. Customers can also choose to deploy NVIDIA AI Enterprise on their own servers or on as-a-service bare metal infrastructure from Equinix Metal across nine regions globally.

AMAX, Colfax International, Exxact Corporation and Lambda are the newest system integrators qualified for NVIDIA AI Enterprise, joining a global ecosystem of channel partners that includes Axians, Carahsoft Technology Corp., Computacenter, Insight Enterprises, NTT, Presidio, Sirius, SoftServe, SVA System Vertrieb Alexander GmbH, TD SYNNEX, Trace3 and World Wide Technology.

Enterprises interested in experiencing development with NVIDIA AI Enterprise can apply for instant access to no cost using curated labs via the NVIDIA LaunchPad program, which also features labs using NVIDIA Fleet Command for edge AI, as well as NVIDIA Base Command for demanding AI development workloads.

Browse through MORF Gallery — virtually or at an in-person exhibition — and you’ll find robots that paint, digital dreamscape experiences, and fine art brought to life by visual effects.

The gallery showcases cutting-edge, one-of-a-kind artwork from award-winning artists who fuse their creative skills with AI, machine learning, robotics and neuroscience.

Scott Birnbaum, CEO and co-founder of MORF Gallery, a Silicon Valley startup, spoke with NVIDIA AI Podcast host Noah Kravitz about digital art, non-fungible tokens, as well as ArtStick, a plug-in device that turns any TV into a premium digital art gallery.

Key Points From This Episode:

• Artists featured by MORF Gallery create fine art using cutting-edge technology. For example, robots help with mundane tasks like painting backgrounds. Visual effects add movement to still paintings. And machine learning can help make NeoMasters — paintings based on original works that were once lost but resurrected or recreated with AI’s help.
• The digital art space offers new and expanding opportunities for artists, technologists, collectors and investors. For one, non-fungible tokens, Birnbaum says, have been gaining lots of attention recently. He gives an overview of NFTs and how they authenticate original pieces of digital art.

Tweetables:

Paintbrushes, cameras, computers and AI are all technologies that “move the art world forward … as extensions of human creativity.” — Scott Birnbaum [8:27]

“Technology is enabling creative artists to really push the boundaries of what their imaginations can allow.” — Scott Birnbaum [13:33]

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Art(ificial) Intelligence: Pindar Van Arman Builds Robots That Paint

Pindar Van Arman, an American artist and roboticist, designs painting robots that explore the differences between human and computational creativity. Since his first system in 2005, he has built multiple artificially creative robots. The most famous, Cloud Painter, was awarded first place at Robotart 2018.

Real or Not Real? Attorney Steven Frank Uses Deep Learning to Authenticate Art

Steven Frank is a partner at the law firm Morgan Lewis, specializing in intellectual property and commercial technology law. He’s also half of the husband-wife team that used convolutional neural networks to authenticate artistic masterpieces, including Da Vinci’s Salvador Mundi, with AI’s help.

Researchers Chris Downum and Leszek Pawlowicz Use Deep Learning to Accelerate Archaeology

Researchers in the Department of Anthropology at Northern Arizona University are using GPU-based deep learning algorithms to categorize sherds — tiny fragments of ancient pottery.

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Online commerce has rocketed to trillions of dollars worldwide in the past decade, serving billions of consumers. Behind the scenes of this explosive growth in online sales is personalization driven by recommender engines.

Recommenders make shopping deeply personalized. While searching for products on e-commerce sites, they find you. Or suggestions can just appear. This wildly delightful corner of the internet is driven by ever more massive datasets and models.

NVIDIA Merlin is the rocket fuel of recommenders. Boosting training and inference, it enables businesses of all types to better harness data to build recommenders accelerated by NVIDIA GPUs.

The stakes are higher than ever for online businesses. Online sales in 2021 were expected to reach nearly $5 trillion worldwide, according to eMarketer, up nearly 17 percent from the prior year.

On some of the world’s largest online sites, even a 1 percent gain in relevance accuracy of recommendations can yield billions more sales.

Investment in recommender systems has become one of the biggest competitive advantages of internet giants today.

The market for recommenders is expected to reach $15.13 billion by 2026, up from $2.12 billion in 2020, according to Mordor Intelligence. The largest and fastest growing segment of the market for recommender engines is in the Asia Pacific region, according to the research firm.

But an industry challenge is that improved relevance requires more data and processing. This data consists of trillions of user-product interactions — clicks, views,  — on billions of products and consumer profiles.

Data of this scale can take days to train models. Yet the faster you can spin out new models informed by more data, the better your relevance.

The Merlin collection of models, methods, and libraries, includes tools for building deep learning-based systems capable of handling terabytes of data that can provide better predictions and increase clicks.

SNAP Taps Merlin and GPUs for Inference Upside

U.S. digital advertising is expected to reach $191.1 billion in 2021, up 25.5 percent from the year before, according to eMarketer.

Snap, parent company to social media app Snapchat, is based in Santa Monica, Calif., and has more than 300 million daily active users. It creates ad revenue from its social photo and video messaging service.

“We will continue to focus on delivering strong results for our advertising partners and innovating to expand the capabilities of our platform and better serve our community,” said Snap CEO Evan Spiegel in its third-quarter earnings statement.

The technical hurdle for Snap is that it seeks to continue to develop its workload’s higher-cost ranking models and expand into more complex models while reducing costs.

The company used NVIDIA GPUs and Merlin to boost its content ranking capabilities.

“Snap used NVIDIA GPUs and Merlin software to improve machine learning inference cost efficiency by 50 percent and decrease serving latency by 2x, providing the compute headroom to experiment and deploy heavier, more accurate ad and content ranking models,” said Nima Khajehnouri, VP of engineering at Snap.

Tencent Boosts Model Training With Merlin’s HugeCTR

Entertainment giant Tencent, which operates the enormously popular messaging service WeChat and payments platform WeChat Pay, is China’s largest company by market capitalization.

Its engineers need to rapidly iterate on models for its advertising recommendation system, putting increasing demands on its training performance.

“The advertising business is a relatively important business inside Tencent and the recommendation system is used to increase the overall advertising revenue,” said Xiangting Kong, expert engineer at Tencent.

The problem is that accuracy of advertising recommendation can only be improved by training more sample data, including more sample features, but this leads to longer training times that affect model update frequency.

“HugeCTR, as a recommendation training framework, is integrated into the advertising recommendation training system to make the update frequency of model training faster, and more samples can be trained to improve online effects,” he said.

After the training model performance is improved, more data can be trained to improve the accuracy of the model, increasing advertising revenue, he added.

Meituan Reduces Costs With NVIDIA A100 GPUs

Meituan’s business is at a crowded intersection of food, entertainment and on-demand services, among its 200 service categories. The Chinese internet giant has more than 667 million active users and 8.3 million active merchants.

Jun Huang, a senior technical expert at Meituan, said that if his team can greatly improve performance, it usually prefers to train more samples and more complex models.

The problem for Meituan was that as its models became more and more complex, it became difficult to optimize the training framework deeply, said Huang.

“We are working on integrating NVIDIA HugeCTR into our training system based on A100 GPUs. The cost is also greatly reduced. This is a preliminary optimization result, and there is still much room to optimize in the future,” he said.

Meituan recently reported its average number of transactions per transacting users increased to 32.8 for the trailing 12 months of the second quarter of 2021, compared with 25.7 for the trailing 12 months of the second quarter of 2020.

Learn more about NVIDIA Merlin. Learn more about NVIDIA Triton.

Editor’s note: This post is one in a series that features individual creators and developers who use NVIDIA Omniverse to boost their artistic processes.

Growing up in the Philippines, award-winning filmmaker Jae Solina says he turned to movies for a reminder that the world was much larger than himself and his homeland.

He started the popular YouTube channel JSFILMZ a decade ago as a way to share home videos he made for fun.

Since then, he’s expanded the channel to showcase his computer graphics-based movies, which have won the Best Animation and Best Super Short Film awards at the Las Vegas Independent Film Festival.

He also posts tutorials for virtual filmmaking with tools, including NVIDIA Omniverse — a physically accurate 3D design collaboration platform exclusively available with NVIDIA RTX GPUs and part of the NVIDIA Studio suite of creator tools.

Making tutorials is a way of paying it forward for Solina, as he is self-taught, gaining his computer graphics skills from other artists’ YouTube videos.

Solina now lives in Las Vegas with his wife and two kids, balancing filmmaking with part-time school and a full-time job.

“The only thing stopping you from creating something is your effort and imagination,” he said. “There are so many free tools like Blender or Omniverse that are readily available, enabling us to create what we want.”

Virtual Film Production

Solina creates computer graphics-based animation films, which can usually take large amounts of time and money, he said. NVIDIA Omniverse eases this process.

“With Omniverse, I don’t have to wait a full week to render a 30-second animation,” Solina said. “The rendering speed in Omniverse is superb and saves me a lot of time, which is important when balancing my filmmaking, non-creative work and family.”

Solina uses an NVIDIA GeForce RTX 3060 GPU, as well as Omniverse apps like Audio2Face, Create and Machinima to create his films virtually.

He also uses Omniverse Connectors for 3D applications like Blender and Autodesk Maya, as well as Reallusion’s iClone and Character Creator, with which he edits motion-capture data.

As a solo filmmaker, Solina said his main challenge is finding virtual assets — like characters and environments — that are photorealistic enough to use for movies.

“My process can definitely be a bit backwards, since the ideal method would be to write a script and then find the assets to make the story come alive,” he said. “But when I’m limited in my resources, I have to think of a storyline that fits a character or an environment I find.”

New support for the Omniverse ecosystem provided by 3D marketplaces and digital asset libraries helps solve this challenge — with thousands of Omniverse-ready assets for creators, all based on Universal Scene Description format.

Looking forward, Solina plans to create a short film entirely inside Omniverse.

Explore the NVIDIA Omniverse Instagram, gallery, forums and Medium channel. Check out Omniverse tutorials on Twitter and YouTube, and join our Discord server and Twitch channel to chat with the community.

At the National Retail Federation’s annual trade show, conversations tend to touch on recurring themes: “Will we be able to stock must-have products for next Christmas?,” “What incentives can I offer to loyal workers?” and “What happens to my margins if Susie Consumer purchases three of the same dresses online and returns two?”

The $26 trillion global retail industry is undergoing accelerated change, brought on by the pandemic and rapidly changing consumer habits. Now, it’s looking for accelerated problem solving using NVIDIA AI to address increasingly acute labor, logistics and supply chain challenges that are accompanying those changes.

Working with an ecosystem of more than 100 startups, equipment providers and software partners, NVIDIA offers an AI Enterprise platform for retailers and quick-service restaurants that helps speed the creation of intelligent stores, AI-driven forecasting, interactive chatbots, voice-enabled order taking and hyperpersonalized recommendations, and logistics and store optimization using digital twin technologies for simulation.

A Labor Crisis

Labor shortages have become a critical issue. In September and October, accommodation and food services businesses lost 1.6 million, or 6.2 percent, of their workforce, while 1.4 million people quit their retail jobs, according to the U.S. Bureau of Labor Statistics.

One way to address the problem is by creating autonomous shopping experiences. AiFi, AWM and Trigo’s autonomous shopping platforms, each shown at NRF, provide a seamless store checkout process. Customers can walk into a store, grab the items they want and pay with their mobile phone on their way out. Beyond addressing labor shortages, these autonomous stores provide live inventory management and prevent shrink.

Store associates are the face of retail organizations, so it makes sense to reduce the time they spend on tasks that aren’t customer facing, such as performing inventory counts or scanning for out-of-stock items. Spacee is using computer vision and AI to help retailers handle these basic, repetitive tasks.

NVIDIA partners Everseen and Graymatics provide asset protection applications at the point of sale to reduce shrinkage and provide customers a faster self-checkout experience. Deep North’s store analytics application is used for queue management, to optimize labor scheduling and store merchandising, resulting in increased sales.

All these startups are using the NVIDIA AI platform to deliver real-time recommendations in stores and distribution centers.

NVIDIA Tokkio conversational AI avatars and the NVIDIA Riva conversational AI framework, as well as recommendation engines based on the NVIDIA Merlin application framework, also help improve the customer experience and solve labor shortages by allowing for automated order taking and upsell based on customer shopping history.

Vistry.AI is delivering drive-thru automated order taking with a speech and recommendation engine, as well as computer vision applications for queue prediction, to predict when orders are ready, ensure food freshness and accelerate curbside pickup.

A Broken Supply Chain

The supply chain is the lifeblood of the retail industry; it was on life support for many in 2021 as attempts to recover from pandemic-related shutdowns around the world were stymied by trucker and dock worker shortages, inclement weather and persistent shortfalls in key food and electronics components.

According to a November report from Adobe Digital Insights, online shoppers in October were met with more than 2 billion out-of-stock messages — double the rate reported in October 2020.

With consumers more likely than not to go — and possibly stay with — a competitor, retailers are investing heavily in predictive analytics to gain real-time insights for forecasting and ordering from point of embarkation through to individual store shelves and distribution centers.

Dematic, a global materials handling company, and startups Kinetic Vision and Osaro are other key companies that use the NVIDIA AI platform to develop edge AI applications that add intelligence to automated warehouse systems. From computer vision AI applications to autonomous forklifts to pick-and-place robots, these AI applications improve distribution center throughput and reduce equipment downtime. And with NVIDIA Fleet Command, these solutions can be remotely deployed and managed securely and at scale in hundreds of distribution centers.

Improving Logistics

To help the $9 trillion logistics industry efficiently route goods from distribution centers to stores and from stores to homes, NVIDIA in November announced its NVIDIA ReOpt AI software.

NVIDIA ReOpt is an accelerated solver for machine learning that optimizes vehicle route planning and logistics in real time. Working with the NVIDIA ReOpt team, Domino’s Pizza implemented a real-time predictive system that helps it meet important delivery standards for customers eager for dinner.

Retail Goes AI 

The NVIDIA AI Enterprise platform is helping retailers weather challenges expected to continue well beyond 2022. With consumers increasingly demanding what the industry calls an omnichannel experience, one that lets them order online and pick up at curbside or have items delivered speedily to their homes, balancing supply with demand has increased the need for fast, actionable insights.

As consumers move from seeking goods and services to experiences, the depth and breadth of interaction between customers and retailers is requiring AI to complement human interaction. It’s a shift that has moved from wishlist to deployment.

Addressing a growing labor crisis among quick-service restaurants, startup Vistry is harnessing AI to automate the process of taking orders.

The company will share its story at the NRF Big Show, the annual industry gathering of the National Retail Federation in New York, starting Jan. 16.

“They’re closing restaurants because there is not enough labor,” said Atif Kureishy, CEO of Vistry, which is a member of the NVIDIA Inception startup accelerator program.

At the same time, customers are placing orders in more ways than ever: for pickup, in drive-throughs and via delivery services, as well as in dining rooms.

“There are new store formats, new configurations, new digital capabilities,” Kureishy said.

To help restaurants keep up, Kureishy, a veteran of both NASA and Booz Allen Hamilton, assembled a team that includes veterans of the semiconductor industry and Ivy League neuroscience programs.

While restaurant labor shortages are grabbing headlines, Vistry is tackling an opportunity driven by a labor shortage demographers have been predicting for decades.

As a result, the quick-service dining industry, which does $300 billion in sales each year in the United States alone, is just one of the industries that will need to find ways to get more done with fewer people over the long term.

To address this, Vistry is working to build an AI-enabled automated order-taking solution. It’s harnessing the latest natural language processing for menu understanding and speech and recommendation systems to deliver faster, more accurate order-taking and more relevant, personalized offers.

The system relies on NVIDIA Riva, a collection of technologies for building speech AI applications. It includes natural language understanding and speech recognition and synthesis capabilities. It also uses computer vision technology optimized with the NVIDIA Metropolis application framework.

Vistry’s platform, powered by the NVIDIA Jetson edge AI platform and NVIDIA A2 Tensor Core GPUs, goes beyond just an automated order-taking kiosk.

Vistry’s computer vision applications also help restaurants automate curbside check-ins. It can speed up drive-throughs and better predict how long it will take for customer orders to be ready. And it will track and trace orders for customers relying on food delivery services, Kureishy explains.

“Buyer behaviors are changing — the guest experience is not solely in the dining room anymore,” Kureishy said.

“Pandemic uncertainty continues to impact dining, along with consumer expectations of a seamless, operationally excellent experience on every platform and touchpoint,” said Susan Beardslee, principal analyst with ABI Research. “Behind the scenes, providers must enable integrated, near real-time digital solutions to address everything from supplies to staffing to delivery optimization.”

Vistry promises its solutions will be easy to deploy, fully integrated with existing restaurant systems, secure and private. They’ll also provide sophisticated real-time dashboards, so restaurant operators can better understand a growing number of sales channels — from drive-through lines to dining rooms to delivery services.

“All the expectations have changed, all of us want food faster, and we want to make sure the quality is preserved,” Kureishy said.

Vistry is helping quick-service restaurants reduce drive-through lines, predict when customers’ orders are ready, ensure food freshness, deliver curbside orders faster and optimize restaurant performance using AI and its analytics dashboard.

Who’s hungry?

Learn more about NVIDIA’s AI solutions for quick-service restaurants. 

Starting next week, Fortnite on GeForce NOW will launch in a limited-time closed beta for mobile, all streamed through the Safari web browser on iOS and the GeForce NOW Android app.

The beta is open for registration for all GeForce NOW members, and will help test our server capacity, graphics delivery and new touch controls performance. Members will be admitted to the beta in batches over the coming weeks.

‘Fortnite’ Streaming Gameplay Comes to Mobile Through iOS Safari and Android With Touch Inputs

Alongside the amazing team at Epic Games, we’ve been working to enable a touch-friendly version of Fortnite for mobile delivered through the cloud. While PC games in the GeForce NOW library are best experienced on mobile with a gamepad, the introduction of touch controls built by the GeForce NOW team offers more options for players, starting with Fortnite.

Beginning today, GeForce NOW members can sign up for a chance to join the Fortnite limited-time closed beta for mobile devices. Not an existing member? No worries. Register for a GeForce NOW membership and sign up to become eligible for the closed beta once the experience starts rolling out next week. Upgrade to a Priority or RTX 3080 membership to receive priority access to gaming servers. A paid GeForce NOW membership is not required to participate.

For tips on gameplay mechanics or a refresher on playing Fortnite with touch controls, check out Fortnite’s Getting Started page.

More Touch Games

And we’re just getting started. Cloud-to-mobile gaming is a great opportunity for publishers to get their games into more gamers’ hands with touch-friendly versions of their games. PC games or game engines, like Unreal Engine 4, which support Windows touch events can easily enable mobile touch support on GeForce NOW.

We’re working with additional publishers to add more touch-enabled games to GeForce NOW. And look forward to more publishers streaming full PC versions of their games to mobile devices with built-in touch support — reaching millions through the Android app and iOS Safari devices.

GFN Thursday Releases

GFN Thursday always means more games. Members can find these and more streaming on the cloud this week:

• The Anacrusis (New release on Steam and Epic Games Store, Jan. 13)
• Supraland Six Inches Under (New release on Steam, Jan. 14)
• Galactic Civilizations 3 (Free on Epic Games Store, Jan. 13 – 20)
• Ready or Not (Steam)

We make every effort to launch games on GeForce NOW as close to their release as possible, but, in some instances, games may not be available immediately.

What are you planning to play this weekend? Let us know on Twitter or in the comments below.

Cutting down the time needed to sequence and analyze a patient’s whole genome from days to hours isn’t just about clinical efficiency — it can save lives.

By accelerating every step of this process — from collecting a blood sample to sequencing the whole genome to identifying variants linked to diseases — a research team led by Stanford University took just hours to find a pathogenic variant and make a definitive diagnosis in a three-month-old infant with a rare seizure-causing genetic disorder. A traditional gene panel analysis ordered at the same time took two weeks to return results.

This ultra-rapid sequencing method, detailed today in the New England Journal of Medicine, helped clinicians manage the epilepsy case by providing insight about the infant’s seizure types and treatment response to anti-seizure medications.

The method set the first Guinness World Record for fastest DNA sequencing technique: five hours and 2 minutes. It was developed by researchers from Stanford University, NVIDIA, Oxford Nanopore Technologies, Google, Baylor College of Medicine and the University of California at Santa Cruz.

The researchers accelerated both base calling and variant calling using NVIDIA GPUs on Google Cloud. Variant calling, the process of identifying the millions of variants in a genome, was also sped up with NVIDIA Clara Parabricks, a computational genomics application framework.

Euan Ashley, MB ChB, DPhil, the paper’s corresponding author and a professor of medicine, of genetics and of biomedical data science at Stanford University School of Medicine, will be speaking at NVIDIA GTC, which runs online March 21-24.

Racing Against Time, Making Clinical Impact

Identifying genetic variants associated with a specific disease is a classic needle-in-the-haystack problem, often requiring researchers to comb through a person’s genome of 3 billion base pairs to find a single change that causes the disease.

It’s a lengthy process: A typical whole human genome sequencing diagnostic test takes six to eight weeks. Even rapid turnaround tests take two or three days. In many cases, this can be too slow to make a difference in treatment of a critically ill patient.

By optimizing the diagnosis pipeline to take only 7-10 hours, clinicians can more quickly identify genetic clues that inform patient care plans. In this pilot project, genomes were sequenced for a dozen patients, most of them children, at Stanford Health Care and Lucile Packard Children’s Hospital Stanford.

In five of the cases, the team found diagnostic variants that were reviewed by physicians and used to inform clinical decisions including heart transplant and drug prescription.

“Genomic information can provide rich insights and enable a clearer picture to be built,” said Gordon Sanghera, CEO of Oxford Nanopore Technologies. “A workflow which could deliver this information in near real time has the potential to provide meaningful benefits in a variety of settings in which rapid access to information is critical.”

AI Calls It: Identifying Variants with Clara Parabricks

The researchers found ways to optimize every step of the pipeline, including speeding up sample preparation and using nanopore sequencing on Oxford Nanopore’s PromethION Flow Cells to generate more than 100 gigabases of data per hour.

This sequencing data was sent to NVIDIA Tensor Core GPUs in a Google Cloud computing environment for base calling — the process of turning raw signals from the device into a string of A, T, G and C nucleotides —  and alignment in near real time. Distributing the data across cloud GPU instances helped minimize latency.

Next, the scientists had to find tiny variations within the DNA sequence that could cause a genetic disorder. Known as variant calling, this stage was sped up with Clara Parabricks using a GPU-accelerated version of PEPPER-Margin-DeepVariant, a pipeline developed in a collaboration between Google and UC Santa Cruz’s Computational Genomics Laboratory.

DeepVariant uses convolutional neural networks for highly accurate variant calling. The GPU-accelerated DeepVariant Germline Pipeline software in Clara Parabricks provides results at 10x the speed of native DeepVariant instances, decreasing the time to identify disease-causing variants.

“Together with our collaborators and some of the world’s leaders in genomics, we were able to develop a rapid sequencing analysis workflow that has already shown tangible clinical benefits,” said NVIDIA’s Mehrzad Samadi, who co-led the creation of Parabricks and co-authored the New England Journal of Medicine article. “These are the kinds of high-impact problems we live to solve.”

Read the full publication in the New England Journal of Medicine and get started with a 90-day trial of NVIDIA Clara Parabricks, which can help analyze a whole human genome in under 30 minutes.

Subscribe to NVIDIA healthcare news here. 

SHIELD Software Experience Upgrade 9.0 is rolling out to all NVIDIA SHIELD TVs, delivering the Android 11 operating system and more.

An updated Gboard — the Google Keyboard — allows people to use their voices and the Google Assistant to discover content in all search boxes.

Additional permissions let users customize privacy across apps, including a new “only this time” option to grant temporary, one-time permissions.

SHIELD also adds support for aptX-compatible Bluetooth headsets. This, in addition to existing support for LDAC headsets, gives customers more options for higher quality listening options.

Browse the release notes for a complete list of upgrades and look for the upgrade notification, which will hit SHIELD TV homescreens starting today.

Stream On

Exciting new app releases and updates bring new and improved content to SHIELD TV-powered home theaters.

Google Play Movies & TV adds stunning Dolby Vision HDR for unparalleled cinematic experiences on SHIELD TV.

SHIELD owners can connect digital movie catalogs from Amazon, Apple TV and VUDU using Movies Anywhere and watch from a single place on Google Play Movies & TV.

Stream limitless entertainment from popular apps like IMDb TV and Apple TV on SHIELD TV, at up to 4K HDR.

IMDb TV delivers thousands of movies, binge-worthy TV shows and IMDb Originals like Leverage: Redemption and Alex Rider. Best of all, they’re always free.

With Apple TV, stream a robust library of Apple Originals in 4K Dolby Vision and Dolby Atmos. Buy or rent over 100,000 movies and shows — including Ted Lasso and The Morning Show — from the largest selection of 4K HDR titles.

Browse the Google Play Store app for the latest updates from Disney+, Paramount+, YouTube TV and Peloton for thousands of live and on-demand workouts.

The Next Generation of GeForce NOW

Gamers around the globe are upgrading their SHIELD TVs into a powerful GeForce RTX 3080-class gaming rig, unlocking extraordinary 4K HDR graphics exclusively on SHIELD, as well as immersive 7.1 surround sound, with the new GeForce NOW RTX 3080 membership.

GeForce NOW Founders members are eligible for an exclusive discount on the RTX 3080 membership. They can retain their Founders benefits — including “Founders for Life” pricing — if they decide to revert back to their original membership. Find more information and sign up at geforcenow.com.

The SHIELD update provides all GeForce NOW members with new benefits. Twitch has been updated to enable simultaneous gaming and streaming in high quality. Support for additional Bluetooth keyboards and mice has been added as well.

SHIELD TV pairs with Xbox One and Series X, Sony PlayStation DualSense and DualShock, and Scuf controllers, allowing for a bring-your-own-controller cloud gaming experience on GeForce NOW.

Bonus Streaming

Google is offering new, U.S.-based SHIELD TV owners six months of Peacock Premium at no additional cost. Unlock everything Premium has to offer. Watch movies and shows like The Office and Parks and Recreation, exclusive originals such as Yellowstone and AP Bio, and live sports including WWE and English Premier League soccer.

To redeem this offer, new SHIELD owners must set up a new Google account or log into a preexisting one, subscribe through the Peacock Premium banner on the For You or Apps tab, and provide a valid form of payment.

It’s an exciting time for SHIELD owners. What are you looking forward to most? Let us know in the comments or on Twitter.