Posts in category: nVidia AI

Vehicle appraisals are getting souped up with a GPU-accelerated AI overhaul.

ProovStation, a four-year-old startup based in Lyon, France, is taking on the ambitious computer-vision quest of automating vehicle inspection and repair estimates, aiming AI-driven super-high-resolution stations at businesses worldwide.

It recently launched three of its state-of-the-art vehicle inspection scanners at French retail giant Carrefour’s Montesson, Vénissieux and Aix-en-Provence locations. The ProovStation drive-thru vehicle scanners are deployed at Carrefour parking lots for drivers to pull in to experience the free service.

The self-serve stations are designed for users to provide vehicle info and ride off with a value report and repair estimate in under two minutes. It also enables drivers to obtain a dealer offer to buy their car as quickly as within just seconds — which holds promise for consumers, as well as used car dealers and auctioneers.

Much is at play across cameras and sensors, high-fidelity graphics, multiple damage detection models, and models and analytics to turn damage detection into repair estimates and purchase offers.

Tapping into NVIDIA GPUs and NVIDIA Metropolis software development kits enables ProovStation to scan 5GB of image and sensor data per car and apply multiple vision AI detection models simultaneously, among other tasks.

ProovStation uses the NVIDIA DeepStream SDK to build its sophisticated vision AI pipeline and optimizes AI inference throughput using Triton Inference Server.

The setup enables ProovStation to run inference for the quick vehicle analysis turnarounds on this groundbreaking industrial edge AI application.

Driving Advances: Bernard Groupe Dealerships 

ProovStation is deploying its stations at a quick clip. That’s been possible because founder Gabriel Tissandier in the early stages connected with an ideal ally in Cedric Bernard, whose family’s Groupe Bernard car dealerships and services first invested in 2017 to boost its own operations.

Groupe Bernard has collected massive amounts of image data from its own businesses for ProovStation prototypes. Bernard left the family business to join Tissandier as the startup’s co-founder and CEO, and co-founder Anton Komyza joined them, and it’s been a wild ride of launches since.

ProovStation is a member of NVIDIA Inception, a program that accelerates cutting-edge startups with access to hardware and software platforms, technical training, as well as AI ecosystem support.

“People often ask me how I’ve gotten so much AI going in this, and I tell them it’s because I work with NVIDIA Inception,” said Tissandier, general manager and chief product officer at ProovStation.

Launching AI Stations Across Markets

ProovStation has deployed 35 scanning stations into operation so far, and it expects to double that number next year. It has launched its powerful edge AI-driven stations in Europe and the United States.

Early adopters include Groupe Bernard, U.K. vehicle sales site BCA Marketplace, OK Mobility car rentals in Spain and Germany’s Sixt car rentals. It also works with undisclosed U.S. automakers and a major online vehicle seller.

Car rental service Sixt has installed a station at Lyon Saint-Exupery Airport with the aim of making car pickups and returns easier.

“Sixt wants to really change the experience of renting a car,” said Tissandier.

Creating an ‘AI Super Factory’ for Damage Datasets

ProovStation has built up data science expertise and a dedicated team to handle its many specialized datasets for the difficult challenge of damage detection.

“To go from a damage review to a damage estimate can sometimes be really tricky,” said Tissandier.

ProovStation has a team of 10 experts in its AI Super Factory dedicated to labeling data with its own specialized software. They have processed more than 2 million images with labels so far, defining a taxonomy of more than 100 types of damages and more than 100 types of parts.

“We knew we needed this level of accuracy to make it reliable and efficient for businesses. Labeling images is super important, especially for us, so we invented some ways to label specific damages,” he said.

Tissandier said that the data science team members and others are brought up to speed on AI with courses from the NVIDIA Deep Learning Institute.

Delivering Data Collection With NVIDIA Industrial Edge AI

ProovStation scans a vehicle with 10 different cameras in its station and takes 300 images — or 5GB of data — for running on its detection models. NVIDIA GPUs enable ProovStation’s AI inference pipeline in 90 seconds to provide detection, assessment of damages, localization, measurements and estimates. Wheels are scanned with an electromagnetic frequency device from tire company Michelin for wear estimates. All of it runs on the NVIDIA edge AI system.

Using two NVIDIA GPUs in a station allows ProovStation to process all of this in high-resolution image analysis for improved accuracy. That data is also transferred to the cloud so ProovStation’s data science team can use it for further training.

Cameras, lighting and positioning are big issues. Detection models can be thrown off by things like glares on glass-shiney cars. ProovStation uses a defectometry model, which allows it to run detection while projecting lines onto vehicle surfaces, highlighting spots where problems appear in the lines.

It’s a challenging problem to solve that leads to business opportunities.

“All of the automotive industry is inspecting cars to provide services — to sell you new tires, to repair your car or windshield, it always starts with an inspection,”  said Tissandier.

As a civil engineer, Scott Ashford used explosives to make the ground under Japan’s Sendai airport safer in an earthquake. Now, as the dean of the engineering college at Oregon State University, he’s at ground zero of another seismic event.

In its biggest fundraising celebration in nearly a decade, Oregon State announced plans today for a $200 million center where faculty and students can plug into resources that will include one of the world’s fastest university supercomputers.

The 150,000-square-foot center, due to open in 2025, will accelerate work at Oregon State’s top-ranked programs in agriculture, computer sciences, climate science, forestry, oceanography, robotics, water resources, materials sciences and more with the help of AI.

A Beacon in AI, Robotics

In honor of a $50 million gift to the OSU Foundation from NVIDIA’s founder and CEO and his wife — who earned their engineering degrees at OSU and met in one of its labs — it will be named the Jen-Hsun and Lori Huang Collaborative Innovation Complex (CIC).

“The CIC and new supercomputer will help Oregon State be recognized as one of the world’s leading universities for AI, robotics and simulation,” said Ashford, whose engineering college includes more than 10,000 of OSU’s 35,000 students.

“We discovered our love for computer science and engineering at OSU,” said Jen-Hsun and Lori Huang. “We hope this gift will help inspire future generations of students also to fall in love with technology and its capacity to change the world.

“AI is the most transformative technology of our time,” they added. “To harness this force, engineering students need access to a supercomputer, a time machine, to accelerate their research. This new AI supercomputer will enable OSU students and researchers to make very important advances in climate science, oceanography, materials science, robotics and other fields.”

A Hub for Students

With an extended-reality theater, robotics and drone playground and a do-it-yourself maker space, the new complex is expected to attract students from across the university. “It has the potential to transform not only the college of engineering, but the entire university, and have a positive economic and environmental impact on the state and the nation,” Ashford said.

The three-story facility will include a clean room, as well as labs for materials scientists, environmental researchers and more.

Ashford expects that over the next decade the center will attract top researchers, as well as research projects potentially worth hundreds of millions of dollars.

“Our donors and university leaders are excited about investing in a collaborative, transdisciplinary approach to problem solving and discovery — it will revitalize our engineering triangle and be an amazing place to study and conduct research,” he said.

A Forest of Opportunities

He gave several examples of the center’s potential. Among them:

• Environmental and electronics researchers may collaborate to design and deploy sensors and use AI to analyze their data, finding where in the ocean or forest hard-to-track endangered species are breeding so their habitats can be protected.
• Students can use augmented reality to train in simulated clean rooms on techniques for making leading-edge chips. Federal and Oregon state officials aim to expand workforce development for the U.S. semiconductor industry, Ashford said.
• Robotics researchers could create lifelike simulations of their drones and robots to accelerate training and testing. (Cassie, a biped robot designed at OSU, just made Guinness World Records for the fastest 100-meter dash by a bot.)
• Students at OSU and its sister college in Germany, DHBW-Ravensburg, could use NVIDIA Omniverse — a platform for building and operating metaverse applications and connecting their 3D pipelines — to enhance design of their award-winning, autonomous, electric race cars.

Building AI Models, Digital Twins

Such efforts will be accelerated with NVIDIA AI and Omniverse, software that can expand the building’s physical labs with simulations and digital twins so every student can have a virtual workbench.

OSU will get state-of-the-art NVIDIA DGX SuperPOD and OVX SuperPOD clusters once the complex’s data center is ready. With an eye on energy efficiency, water that cooled computer racks will then help heat more than 500,000 square feet of campus buildings.

The SuperPOD will likely include a mix of about 60 DGX and OVX systems — powered by next-generation CPUs, GPUs and networking — creating a system powerful enough to train the largest AI models and perform complex digital twin simulations. Ashford notes OSU won a project working with the U.S. Department of Energy because its existing computer center has a handful of DGX systems.

Advancing Diversity, Inclusion

At the Oct. 14 OSU Foundation event announcing the naming of the new complex, Oregon State officials thanked donors and kicked off a university-wide fundraising campaign. OSU has requested support from the state of Oregon for construction of the building and seeks additional philanthropic investments to expand its research and support its hiring and diversity goals.

OSU’s president, Jayathi Murthy, said the complex provides an opportunity to advance diversity, equity and inclusion in the university’s STEM education and research. OSU’s engineering college is already among the top-ranked U.S. schools for tenured or tenure-track engineering faculty who are women.

AI Universities Sprout

Oregon State also is among a small but growing set of universities accelerating their journeys in AI and high performance computing.

A recent whitepaper described efforts at University of Florida to spread AI across its curriculum as part of a partnership with NVIDIA that enabled it to install HiPerGator, a DGX SuperPOD based on NVIDIA DGX A100 systems with NVIDIA A100 Tensor Core GPUs.

Following Florida’s example, Southern Methodist University announced last fall its plans to make the Dallas area a hub of AI development around its new DGX SuperPOD.

“We’re seeing a lot of interest in the idea of AI universities from Asia, Europe and across the U.S.,” said Cheryl Martin, who leads NVIDIA’s efforts in higher education research.

When it comes to reimagining the next generation of automotive, NIO is thinking outside the car.

This month, the China-based electric vehicle maker introduced its lineup to four new countries in Europe — Denmark, Germany, the Netherlands and Sweden — along with an innovative subscription-based ownership model. The countries join NIO’s customer base in China and Norway.

The models launching in the European market are all built on the NIO Adam supercomputer, which uses four NVIDIA DRIVE Orin systems-on-a-chip to deliver software-defined AI features.

These intelligent capabilities, which will gradually enable automated driving on expressways and urban areas, as well as autonomous parking and battery swap, are just the start of NIO’s fresh take on the vehicle ownership experience.

As the automaker expands its footprint, it is emphasizing membership rather than pure ownership. NIO vehicles are available via flexible subscription models, and customers can access club spaces, called NIO Houses, that offer a wide array of amenities.

International Supermodels

NIO’s lineup sports a premium model for every type of driver.

The flagship ET7 sedan boasts a spacious interior, with more than 620 miles of battery range and an impressive 0-to-60 miles per hour in under four seconds. For the mid-size segment, the ET5 is an EV that’s as agile as it is comfortable, borrowing the same speed and immersive interior as its predecessor in a more compact package.

Finally, the ES7 — renamed the EL7 for the European market  — is an electric SUV for rugged and urban drivers alike. The intelligent EV sports 10 driving modes, including a camping mode for off-road adventures.

All three models run on the high-performance, centralized Adam supercomputer. With more than 1,000 trillion operations per second of performance provided by four DRIVE Orin SoCs, Adam can power a wide range of intelligent features, with enough headroom to add new capabilities over the air.

Using multiple SoCs, Adam integrates the redundancy and diversity necessary for safe autonomous operation. The first two SoCs process the 8GB of data produced every second by the vehicle’s sensor set.

The third Orin serves as a backup to ensure the system can operate safely in any situation. And the fourth enables local training, improving the vehicle with fleet learning and personalizing the driving experience based on individual user preferences.

While NIO’s models vary in size and design, they all share the same intelligent DNA, so every customer has access to the cutting edge in AI transportation.

A NIO Way Forward

The NIO experience doesn’t end when the drive is over — it aims to create an entire lifestyle.

Customers in new markets won’t be buying the vehicles, they’ll sign for leases as long as 60 months or as short as one month. These subscriptions include insurance, maintenance, winter tires, a courtesy car, battery swapping and the option to upgrade battery services.

The purpose of the business model is to offer the utmost flexibility, so customers always have access to the best vehicle for their needs, whatever they may be and however often they may change.

Additionally, every customer has access to the NIO House. This community space offers co-working areas, cafes, workout facilities, playrooms for children and more. NIO Houses exist in more than 80 places around the world, with locations planned for Amsterdam, Berlin, Copenhagen, Düsseldorf, Frankfurt, Gothenburg, Hamburg, Rotterdam and Stockholm.

Deliveries to the expanded European markets are scheduled to start with the ET7 sedan on Sunday, Oct. 16, with the EL7 and ET5 set to ship in January and March of 2023, respectively.

NVIDIA and Oracle are teaming to make the power of AI accessible to enterprises across industries. These include healthcare, financial services, automotive and a broad range of natural language processing use cases driven by large language models, such as chatbots, personal assistants, document summarization and article completion.

Join NVIDIA and Oracle experts at Oracle CloudWorld, running Oct. 17-20 in Las Vegas, to learn more about technology breakthroughs and steps companies can take to unlock the potential of enterprise data with AI.

Attend the fireside chat featuring NVIDIA founder and CEO Jensen Huang and Oracle CEO Safra Catz taking place on Tuesday, Oct. 18, at 9 a.m. PT to learn how NVIDIA AI is being enabled for enterprises globally on Oracle.

NVIDIA and Oracle bring together all the key ingredients for speeding AI adoption for enterprises: the ability to securely access and manage data within Oracle’s Enterprise Data Management platforms; on-demand access to the massive computational power of NVIDIA-accelerated infrastructure at scale to build and train AI models using this data; and an NVIDIA AI developer performance-optimized stack that simplifies and accelerates building and deploying AI-enabled enterprise products and services at scale.

The NVIDIA AI platform, combined with Oracle Cloud Infrastructure, paves the way to an AI-powered enterprise, regardless of where a business is in its AI adoption journey. The platform offers GPU-accelerated deep learning frameworks, pretrained AI models, enterprise-grade software development kits and application-specific frameworks for various use cases.

Register for Oracle CloudWorld and dive into these sessions and demos to learn more:

• Keynote Fireside Address: Driving Impactful Business Results — featuring Huang and Catz, in conversation with leaders of global brands, discovering how they solve complex problems by working with Oracle. This session takes place on Tuesday, Oct. 18, from 9-10:15 a.m. PT.
• Oracle Making AI Approachable for Everyone — featuring Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA, and Elad Ziklik, vice president of AI and data science services at Oracle. This session takes place on Tuesday, Oct. 18, from 11-11:45 a.m. PT.
• Achieving Database Operational Excellence Using Events and Functions at NVIDIA — featuring Chris May, senior manager at NVIDIA, and Riaz Kapadia, enterprise cloud architect at Oracle. This session takes place on Tuesday, Oct. 18, from 12:15 -1 p.m. PT.      
• MLOps at Scale With Kubeflow on Oracle Cloud Infrastructure — featuring Richard Wang, senior cloud and machine learning solutions architect at NVIDIA, and Sesh Dehalisan, distinguished cloud architect at Oracle. This session takes place on Tuesday, Oct. 18, from 12:15-1 p.m. PT.
• Next-Generation AI Empowering Human Expertise — featuring Bryan Catanzaro, vice president of applied deep learning research at NVIDIA; Erich Elsen, co-founder and head of machine learning at Adept AI; and Rich Clayton, vice president of product strategy for analytics at Oracle. This session takes place on Tuesday, Oct. 18, from 12:30-1 p.m. PT.        
• NVIDIA’s Migration From On-Premises to MySQL HeatWave — featuring Chris May, senior manager at NVIDIA; Radha Chinnaswamy, consultant at NVIDIA; and Sastry Vedantam, MySQL master principal solution engineer at Oracle. This session takes place on Tuesday, Oct. 18, from 4-4:45 p.m. PT.
• Scale Large Language Models With NeMo Megatron — featuring Richard Wang, senior cloud and machine learning solutions architect at NVIDIA; Anup Ojah, senior manager of cloud engineering at Oracle; and Tanina Cadwell, solutions architect at Vyasa Analytics. This session takes place on Wednesday, Oct. 19, from 11:30 a.m. to 12:15 p.m. PT.
• Serve ML Models at Scale With Triton Inference Server on OCI — featuring Richard Wang, senior cloud and machine learning solutions architect at NVIDIA, and Joanne Lei, master principal cloud architect at Oracle. This session takes place on Wednesday, Oct. 19, from 1:15-2 p.m. PT.
• Accelerating Java on the GPU — featuring Ken Hester, solutions architect director at NVIDIA, and Paul Sandoz, Java architect at Oracle. This session takes place on Thursday, Oct. 20, from 10:15-10:45 a.m. PT.
• NVIDIA AI Software for Business Outcomes: Integrating NVIDIA AI Into Your Applications — featuring Kari Briski, vice president of software product management for AI and high-performance computing software development kits at NVIDIA. This session takes place on demand.

Visit NVIDIA’s Oracle CloudWorld showcase page to discover more about NVIDIA and Oracle’s collaboration and innovations for cloud-based solutions.

Alien invasions. Gritty dystopian megacities. Battlefields swarming with superheroes. As one of Hollywood’s top concept artists, Drew Leung can visualize any world you can think of, except one where AI takes his job.

He would know. He’s spent the past few months trying to make it happen, testing every AI tool he could. “If your whole goal is to use AI to replace artists, you’ll find it really disappointing,” Leung said.

Pros and amateurs alike, however, are finding these new tools intriguing. For amateur artists — who may barely know which way to hold a paintbrush — AI gives them almost miraculous capabilities.

Thanks to AI tools such as Midjourney, OpenAI’s Dall·E, DreamStudio, and open-source software such as Stable Diffusion, AI-generated art is everywhere, spilling out across the globe through social media such as Facebook and Twitter, the tight-knit communities on Reddit and Discord, and image-sharing services like Pinterest and Instagram.

The trend has sparked an uproarious discussion in the art community. Some are relying on AI to accelerate their creative process — doing in minutes what used to take a day or more, such as instantly generating mood boards with countless iterations on a theme.

Others, citing issues with how the data used to train these systems is collected and managed, are wary. “I’m frustrated because this could be really exciting if done right,” said illustrator and concept artist Karla Ortiz, who currently refuses to use AI for art altogether.

NVIDIA’s creative team provided a taste of what these tools can do in the hands of a skilled artist during NVIDIA founder and CEO Jensen Huang’s keynote at the most recent NVIDIA GTC technology conference.

Highlights included a woman representing AI created in the drawing style of Leonardo da Vinci and an image of 19th-century English mathematician Ada Lovelace, considered by many the first computer programmer, holding a modern game controller.

More Mechanical Than Magical

After months of experimentation, Leung — known for his work on more than a score of epic movies including Black Panther and Captain America: Civil War, among other blockbusters — compares AI art tools to a “kaleidoscope” that combines colors and shapes in unexpected ways with a twist of your wrist.

Used that way, some artists say AI is most interesting when an artist pushes it hard enough to break. AI can instantly reveal visual clichés — because it fails when asked to do things it hasn’t seen before, Leung said.

And because AI tools are fed by vast quantities of data, AI can expose biases across collections of millions of images — such as poor representation of people of color — because it struggles to produce images outside a narrow ideal.

New Technologies, OId Conversations

Such promises and pitfalls put AI at the center of conversations about the intersections of technology and technique, automation and innovation, that have been going on long before AI, or even computers, existed.

After Louis-Jacques-Mandé Daguerre invented photography in 1839, painter Charles Baudelaire declared photography “art’s most mortal enemy.”

With the motto, “You push the button, we do the rest,” George Eastman’s affordable handheld cameras made photography accessible to anyone in 1888. It took years for 19th-century promoter and photographer Alfred Stieglitz, who played a key role transforming photography into an accepted art form, to come around.

Remaking More Than Art

Over the next century new technologies, like color photography, offset printmaking and digital art, inspired new movements from expressionism to surrealism, pop art to post-modernism.

The emergence of AI art continues the cycle. And the technology driving it, called transformers, like the technologies that led to past art movements, is driving changes far outside the art world.

First introduced in 2017, transformers are a type of neural network that learns context and, thus, meaning, from data. They’re now among the most vibrant areas for research in AI.

A single pretrained model can perform amazing feats — including text generation, translation and even software programming — and is the basis of the new generation of AI that can turn text into detailed images.

The diffusion models powering AI image tools, such as Dall·E and Dall·E 2, are transformer-based generative models that refine and rearrange pixels again and again until the image matches a user’s text description.

More’s coming. NVIDIA GPUs — the parallel processing engines that make modern AI possible — are being fine-tuned to support ever more powerful applications of the technology.

Introduced earlier this year, the Hopper FP8 Transformer Engine in NVIDIA’s latest GPUs will soon be embedded across vast server farms, in autonomous vehicles and in powerful desktop GPUs.

Intense Conversations

All these possibilities have sparked intense conversations.

Artist Jason Allen ignited a worldwide controversy by winning a contest at the Colorado State Fair with an AI-generated painting.

Attorney Steven Frank has renewed old conversations in art history by using AI to reassess the authenticity of some of the world’s most mysterious artworks, such as “Salvator Mundi,” left, a painting now attributed to da Vinci.

Philosophers, ethicists and computer scientists such as Ahmed Elgammal at Rutgers University are debating if it’s possible to separate techniques that AI can mimic with the intentions of the human artists who created them.

Ortiz is among a number raising thorny questions about how the data used to train AI is collected and managed. And once an AI is trained on an image, it can’t unlearn what it’s been trained to do, Ortiz says.

Some, such as New York Times writer Kevin Roose, wonder if AI will eventually start taking away jobs from artists.

Others, such as Jason Scott, an artist and archivist at the Internet Archive, dismiss AI art as “no more dangerous than a fill tool.”

Such whirling conversations — about how new techniques and technologies change how art is made, why art is made, what it depicts, and how art, in turn, remakes us — have always been an element of art. Maybe even the most important element.

“Art is a conversation we are all invited to,” American author Rachel Hartman once wrote.

Ortiz says this means we should be thoughtful. “Are these tools assisting the artist, or are they there to be the artist?” she asked.

It’s a question all of us should ponder. Controversially, anthropologist Eric Gans connects the first act of imbuing physical objects with a special significance or meaning — the first art — to the origin of language itself.

In this context, AI will, inevitably, reshape some of humanity’s oldest conversations. Maybe even our very oldest conversation. The stakes could not be higher.

Featured image: Portrait of futuristic Ada Lovelace, playing video games, editorial photography style by NVIDIA’s creative team, using Midjourney. 

High-end PC gaming arrives on more devices this GFN Thursday.

GeForce NOW RTX 3080 members can now stream their favorite PC games at up to 1600p and 120 frames per second in a Chrome browser. No downloads, no installs, just victory.

Even better, NVIDIA has worked with Google to support the newest Chromebooks, which are the first laptops custom built for cloud gaming, with gorgeous 1600p resolution 120Hz+ displays. They come with a free three-month GeForce NOW RTX 3080 membership, the highest performance tier.

On top of these new ways to play, this GFN Thursday brings hordes of fun with 11 new titles streaming from the cloud — including the Warhammer 40,000: Darktide closed beta, available Oct. 14-16.

High-Performance PC Gaming, Now on Chromebooks

Google’s newest Chromebooks are the first built for cloud gaming, and include GeForce NOW right out of the box.

These new cloud gaming Chromebooks — the Acer Chromebook 516 GE, the ASUS Chromebook Vibe CX55 Flip and the Lenovo Ideapad Gaming Chromebook — all include high refresh rate, high-resolution displays, gaming keyboards, fast WiFi 6 connectivity and immersive audio. And with the GeForce NOW RTX 3080 membership, gamers can instantly stream 1,400+ PC games from the GeForce NOW library at up to 1600p at 120 FPS.

That means Chromebook gamers can jump right into over 100 free-to-play titles, including major franchises like Fortnite, Genshin Impact and League of Legends. RTX 3080 members can explore the worlds of Cyberpunk 2077, Control and more with RTX ON, only through GeForce NOW. Compete online with ultra-low latency and other features perfect for playing.

The GeForce NOW app comes preinstalled on these cloud gaming Chromebooks, so users can jump straight into the gaming — just tap, search, launch and play. Plus, pin games from GeForce NOW right to the app shelf to get back into them with just a click.

For new and existing members, every cloud gaming Chromebook includes a free three-month RTX 3080 membership through the Chromebook Perks program.

Stop! Warhammer Time

Fatshark leaps thousands of years into the future to bring gamers Warhammer 40,000: Darktide on Tuesday, Nov. 30.

Gamers who’ve preordered on Steam can get an early taste of the game with a closed beta period, running Oct. 14-16.

Head to the industrial city of Tertium to combat the forces of Chaos, using Vermintide 2’s lauded melee system and a range of deadly Warhammer 40,000 weapons. Personalize your play style with a character-creation system and delve deep into the city to put a stop to the horrors that lurk.

The fun doesn’t stop there. Members can look for these new titles streaming this week:

• Asterigos: Curse of the Stars (New release on Steam)
• Kamiwaza: Way of the Thief (New release on Steam)
• LEGO Bricktales (New release on Steam and Epic Games)
• Ozymandias: Bronze Age Empire Sim (New release on Steam)
• PC Building Simulator 2 (New release on Epic Games)
• The Last Oricru (New release on Steam, Oct. 13)
• Rabbids: Party of Legends (New release on Ubisoft, Oct. 13)
• The Darkest Tales (New release on Steam,Oct. 13)
• Scorn (New Release on Steam and Epic Games, Oct. 14)
• Warhammer 40,000: Darktide Closed Beta (New release on Steam, available from Oct. 14th, 7 AM PT to Oct. 17th 1 AM PT)
• Dual Universe (Steam)

Finally, we have a question for you – we promise not to tattle. Let us know your answer on Twitter or in the comments below.

Be honest, how many times have you played games on GFN with chrome browser in class or a meeting?

— NVIDIA GeForce NOW (@NVIDIAGFN) October 12, 2022

The age of electric vehicles has arrived and, with it, an entirely new standard for premium SUVs.

Polestar, the performance EV brand spun out from Volvo Cars, launched its third model today in Copenhagen. With the Polestar 3, the automaker has taken SUV design back to the drawing board, building a vehicle as innovative as the technology it features.

The EV premieres a new aerodynamic profile from the brand, in addition to sustainable materials and advanced active and passive safety systems. The Polestar 3 also maintains some attributes of a traditional SUV, including a powerful and wide stance.

It features a 14.5-inch center display for easily accessible infotainment, in addition to 300 miles of battery range to tackle trips of any distance.

The Polestar 3 is the brand’s first SUV, as well as its first model to run on the high-performance, centralized compute of the NVIDIA DRIVE platform. This software-defined architecture lends the Polestar 3 its cutting-edge personality, making it an SUV that tops the list in every category.

Reigning Supreme

The crown jewel of a software-defined vehicle is its core compute — and the Polestar 3 is built with top-of-the-line hardware and software.

The NVIDIA DRIVE high-performance AI compute platform processes data from the SUV’s multiple sensors and cameras to enable advanced driver-assistance safety (ADAS) features and driver monitoring.

This ADAS system combines technology from Zenseact, Luminar and Smart Eye that integrates seamlessly thanks to the centralized computing power of NVIDIA DRIVE.

By running on a software-defined architecture, these automated driving features will continue to gain new functionality via over-the-air updates and eventually perform autonomous highway driving.

The Polestar 3 customers’ initial purchase won’t remain the same years or even months later — it will be constantly improving and achieving capabilities not yet even dreamed of.

Charging Ahead

The Polestar 3 kicks off a new phase for the automaker, which is accelerating its product and international growth plans.

The SUV will begin deliveries late next year. Starting with the Polestar 3, the automaker expects to launch a new car every year for the next three years and aims to expand its presence to at least 30 global markets by the end of 2023.

The automaker is targeting 10x growth in global sales, to reach 290,000 vehicles sold by the end of 2025 from about 29,000 in 2021.

And with its future-forward SUV, Polestar is adding a dazzling jewel to its already star-studded crown.

Everyone wants green computing.

Mobile users demand maximum performance and battery life. Businesses and governments increasingly require systems that are powerful yet environmentally friendly. And cloud services must respond to global demands without making the grid stutter.

For these reasons and more, green computing has evolved rapidly over the past three decades, and it’s here to stay.

What Is Green Computing?

Green computing, or sustainable computing, is the practice of maximizing energy efficiency and minimizing environmental impact in the ways computer chips, systems and software are designed and used.

Also called green information technology, green IT or sustainable IT, green computing spans concerns across the supply chain, from the raw materials used to make computers to how systems get recycled.

In their working lives, green computers must deliver the most work for the least energy, typically measured by performance per watt.

Why Is Green Computing Important?

Green computing is a significant tool to combat climate change, the existential threat of our time.

Global temperatures have risen about 1.2°C over the last century. As a result, ice caps are melting, causing sea levels to rise about 20 centimeters and increasing the number and severity of extreme weather events.

The rising use of electricity is one of the causes of global warming. Data centers represent a small fraction of total electricity use, about 1% or 200 terawatt-hours per year, but they’re a growing factor that demands attention.

Powerful, energy-efficient computers are part of the solution. They’re advancing science and our quality of life, including the ways we understand and respond to climate change.

What Are the Elements of Green Computing?

Engineers know green computing is a holistic discipline.

“Energy efficiency is a full-stack issue, from the software down to the chips,” said Sachin Idgunji, co-chair of the power working group for the industry’s MLPerf AI benchmark and a distinguished engineer working on performance analysis at NVIDIA.

For example, in one analysis he found NVIDIA DGX A100 systems delivered a nearly 5x improvement in energy efficiency in scale-out AI training benchmarks compared to the prior generation.

“My primary role is analyzing and improving energy efficiency of AI applications at everything from the GPU and the system node to the full data center scale,” he said.

Idgunji’s work is a job description for a growing cadre of engineers building products from smartphones to supercomputers.

What’s the History of Green Computing?

Green computing hit the public spotlight in 1992, when the U.S. Environmental Protection Agency launched Energy Star, a program for identifying consumer electronics that met standards in energy efficiency.

A 2017 report found nearly 100 government and industry programs across 22 countries promoting what it called green ICTs, sustainable information and communication technologies.

One such organization, the Green Electronics Council, provides the Electronic Product Environmental Assessment Tool, a registry of systems and their energy-efficiency levels. The council claims it’s saved nearly 400 million megawatt-hours of electricity through use of 1.5 billion green products it’s recommended to date.

Work on green computing continues across the industry at every level.

For example, some large data centers use liquid-cooling while others locate data centers where they can use cool ambient air. Schneider Electric recently released a whitepaper recommending 23 metrics for determining the sustainability level of data centers.

A Pioneer in Energy Efficiency

Wu Feng, a computer science professor at Virginia Tech, built a career pushing the limits of green computing. It started out of necessity while he was working at the Los Alamos National Laboratory.

A computer cluster for open science research he maintained in an external warehouse had twice as many failures in summers versus winters. So, he built a lower-power system that wouldn’t generate as much heat.

He demoed the system, dubbed Green Destiny, at the Supercomputing conference in 2001. Covered by the BBC, CNN and the New York Times, among others, it sparked years of talks and debates in the HPC community about the potential reliability as well as efficiency of green computing.

Interest rose as supercomputers and data centers grew, pushing their boundaries in power consumption. In November 2007, after working with some 30 HPC luminaries and gathering community feedback, Feng launched the first Green500 List, the industry’s benchmark for energy-efficient supercomputing.

A Green Computing Benchmark

The Green500 became a rallying point for a community that needed to reign in power consumption while taking performance to new heights.

“Energy efficiency increased exponentially, flops per watt doubled about every year and a half for the greenest supercomputer at the top of the list,” said Feng.

By some measures, the results showed the energy efficiency of the world’s greenest systems increased two orders of magnitude in the last 14 years.

Feng attributes the gains mainly to the use of accelerators such as GPUs, now common among the world’s fastest systems.

“Accelerators added the capability to execute code in a massively parallel way without a lot of overhead — they let us run blazingly fast,” he said.

He cited two generations of the Tsubame supercomputers in Japan as early examples. They used NVIDIA Kepler and Pascal architecture GPUs to lead the Green500 list in 2014 and 2017, part of a procession of GPU-accelerated systems on the list.

“Accelerators have had a huge impact throughout the list,” said Feng, who will receive an award for his green supercomputing work at the Supercomputing event in November.

“Notably, NVIDIA was fantastic in its engagement and support of the Green500 by ensuring its energy-efficiency numbers were reported, thus helping energy efficiency become a first-class citizen in how supercomputers are designed today,” he added.

AI and Networking Get More Efficient

Today, GPUs and data processing units (DPUs) are bringing greater energy efficiency to AI and networking tasks, as well as HPC jobs like simulations run on supercomputers and enterprise data centers.

AI, the most powerful technology of our time, will become a part of every business. McKinsey & Co. estimates AI will add a staggering $13 trillion to global GDP by 2030 as deployments grow.

NVIDIA estimates data centers could save a whopping 19 terawatt-hours of electricity a year if all AI, HPC and networking offloads were run on GPU and DPU accelerators (see the charts below). That’s the equivalent of the energy consumption of 2.9 million passenger cars driven for a year.

It’s an eye-popping measure of the potential for energy efficiency with accelerated computing.

AI Benchmark Measures Efficiency

Because AI represents a growing part of enterprise workloads, the MLPerf industry benchmarks for AI have been measuring performance per watt on submissions for data center and edge inference since February 2021.

“The next frontier for us is to measure energy efficiency for AI on larger distributed systems, for HPC workloads and for AI training — it’s similar to the Green500 work,” said Idgunji, whose power group at MLPerf includes members from six other chip and systems companies.

The public results motivate participants to make significant improvements with each product generation. They also help engineers and developers understand ways to balance performance and efficiency across the major AI workloads that MLPerf tests.

“Software optimizations are a big part of work because they can lead to large impacts in energy efficiency, and if your system is energy efficient, it’s more reliable, too,” Idgunji said.

Green Computing for Consumers

In PCs and laptops, “we’ve been investing in efficiency for a long time because it’s the right thing to do,” said Narayan Kulshrestha, a GPU power architect at NVIDIA who’s been working in the field nearly two decades.

For example, Dynamic Boost 2.0 uses deep learning to automatically direct power to a CPU, a GPU or a GPU’s memory to increase system efficiency. In addition, NVIDIA created a system-level design for laptops, called Max-Q, to optimize and balance energy efficiency and performance.

Building a Cyclical Economy

When a user replaces a system, the standard practice in green computing is that the old system gets broken down and recycled. But Matt Hull sees better possibilities.

“Our vision is a cyclical economy that enables everyone with AI at a variety of price points,” said Hull, the vice president of sales for data center AI products at NVIDIA.

So he aims to find the system a new home with users in developing countries who find it useful and affordable. It’s a work in progress seeking the right partner and writing a new chapter in an existing lifecycle management process.

Green Computing Fights Climate Change

Energy-efficient computers are among the sharpest tools fighting climate change.

Scientists in government labs and universities have long used GPUs to model climate scenarios and predict weather patterns. Recent advances in AI, driven by NVIDIA GPUs, can now help model weather forecasting 100,000x quicker than traditional models. Watch the following video for details:

In an effort to accelerate climate science, NVIDIA announced plans to build Earth-2, an AI supercomputer dedicated to predicting the impacts of climate change. It will use NVIDIA Omniverse, a 3D design collaboration and simulation platform, to build a digital twin of Earth so scientists can model climates in ultra-high resolution.

In addition, NVIDIA is working with the United Nations Satellite Centre to accelerate climate-disaster management and train data scientists across the globe in using AI to improve flood detection.

Meanwhile, utilities are embracing machine learning to move toward a green, resilient and smart grid. Power plants are using digital twins to predict costly maintenance and model new energy sources, such as fusion-reactor designs.

What’s Ahead in Green Computing?

Feng sees the core technology behind green computing moving forward on multiple fronts.

In the short term, he’s working on what’s called energy proportionality, that is, ways to make sure systems get peak power when they need peak performance and scale gracefully down to zero power as they slow to an idle, like a modern car engine that slows its RPMs and then shuts down at a red light.

Long term, he’s exploring ways to minimize data movement inside and between computer chips to reduce their energy consumption. And he’s among many researchers studying the promise of quantum computing to deliver new kinds of acceleration.

It’s all part of the ongoing work of green computing, delivering ever more performance at ever greater efficiency.

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. In the coming weeks, we’ll deep dive on new GeForce RTX 40 Series GPU features, technologies and resources, and how they dramatically accelerate content creation.

Creators can now pick up the GeForce RTX 4090 GPU, available from top add-in card providers including ASUS, Colorful, Gainward, Galaxy, GIGABYTE, INNO3D, MSI, Palit, PNY and ZOTAC, as well as from system integrators and builders worldwide.

Fall has arrived, and with it comes the perfect time to showcase the beautiful, harrowing video, Old Abandoned Haunted Mansion, created by 3D artist and principal lighting expert Pasquale Scionti this week In the NVIDIA Studio.

Artists like Scionti can create at the speed of light with the help of RTX 40 Series GPUs alongside 110 RTX-accelerated apps, the NVIDIA Studio suite of software and dedicated Studio Drivers.

A Quantum Leap in Creative Performance

The new GeForce RTX 4090 GPU brings an extraordinary boost in performance, third-generation RT Cores, fourth-generation Tensor Cores, an eighth-generation NVIDIA Dual AV1 Encoder and 24GB of Micron G6X memory capable of reaching 1TB/s bandwidth.

3D artists can now build scenes in fully ray-traced environments with accurate physics and realistic materials — all in real time, without proxies. DLSS 3 technology uses the AI-powered RTX Tensor Cores and a new Optical Flow Accelerator to generate additional frames and dramatically increase frames per second (FPS). This improves smoothness and speeds up movement in the viewport. NVIDIA is working with popular 3D apps Unity and Unreal Engine 5 to integrate DLSS 3.

DLSS 3 will also benefit workflows in the NVIDIA Omniverse platform for building and connecting custom 3D pipelines. New Omniverse tools such as NVIDIA RTX Remix for modders, which was used to create Portal with RTX, will be game changers for 3D content creation.

Video and live-streaming creative workflows are also turbocharged as the new AV1 encoder delivers 40% increased efficiency, unlocking higher resolution and crisper image quality. Expect AV1 integration in OBS Studio, DaVinci Resolve and Adobe Premiere Pro (though the Voukoder plugin) later this month.

The new dual encoders capture up to 8K resolution at 60 FPS in real time via GeForce Experience and OBS Studio, and cut video export times nearly in half. These encoders will be enabled in popular video-editing apps including Blackmagic Design’s DaVinci Resolve, the Voukoder plugin for Adobe Premiere Pro, and Jianying Pro — China’s top video-editing app — later this month.

State-of-the-art AI technology, like AI image generators and new video-editing tools in DaVinci Resolve, is ushering in the next step in the AI revolution, delivering up to a 2x increase in performance over the previous generation.

To break technological barriers and expand creative possibilities, pick up the GeForce RTX 4090 GPU today. Check out this product finder for retail availability.

Haunted Mansion Origins

The visual impact of Old Abandoned Haunted Mansion is nothing short of remarkable, with photorealistic details for lighting and shadows and stunningly accurate textures.

However, it’s Scionti’s intentional omission of specific detail that allows viewers to construct their own narrative, a staple of his work.

Scionti highlighted additional mysterious features he created within the haunted mansion: a painting with a specter on the stairs, knocked-over furniture, a portrait of a woman who might’ve lived there and a mirror smashed in the middle as if someone struck it.

“Perhaps whatever happened is still in these walls,” mused Scionti. “Abandoned, reclaimed by nature.”

Scionti said he finds inspiration in the works of H.R. Giger, H.P. Lovecraft and Edgar Allan Poe, and often dreams of the worlds he aspires to build before bringing them to life in 3D. He stressed, however, “I don’t have a dark side! It just appears in my work!”

For Old Abandoned Haunted Mansion, the artist began by creating a moodboard featuring abandoned places. He specifically included structures that were reclaimed by nature to create a warm mood with the sun filtering in from windows, doors and broken walls.

Scionti then modeled the scene’s objects, such as the ceiling lamp, painting frames and staircase, using Autodesk 3ds Max. By using a GeForce RTX 3090 GPU and selecting the default Autodesk Arnold renderer, he deployed RTX-accelerated AI denoising, resulting in interactive renders that were easy to edit while maintaining photorealism.

The versatile Autodesk 3ds Max software supports third-party GPU-accelerated renderers such as V-Ray, OctaneRender and Redshift, giving RTX owners additional options for their creative workflows.

When it comes time to export the renders, Scionti will soon be able to use GeForce RTX 40 Series GPUs to do so up to 80% faster than the previous generation.

Scionti imported the models, like the ceiling lamp and various paintings, into Adobe Substance 3D Painter to apply unique textures. The artist used RTX-accelerated light and ambient occlusion to bake his assets in mere seconds.

Modeling techniques for the curtains, the drape on the armchair and the ghostly figure were created using Marvelous Designer, a realistic cloth-making program for 3D artists. In a system-requirements page, the Marvelous Designer team recommends using GeForce RTX 30 and other NVIDIA RTX GPU class GPUs, as well as downloading the latest NVIDIA Studio Driver.

Additional objects like the wooden ceiling were created using Quixel Mixer, an all-in-one texturing and material-creation tool designed to be intuitive and extremely fast.

Scionti then searched Quixel Megascans, the largest and fastest growing 3D can library, to acquire the remaining assets to round out the piece.

With the composition in place, Scionti applied final details in Unreal Engine 5.

RTX ON in Unreal Engine 5

Scionti used Unreal Engine 5, activating hardware-accelerated RTX ray tracing for high-fidelity, interactive visualization of 3D designs. He was further aided by NVIDIA DLSS, which uses AI to upscale frames rendered at lower resolution while retaining high-fidelity detail. The artist then constructed the scene rich with beautiful lighting, shadows and textures.

The new GeForce RTX 40 Series GPU lineup will use DLSS 3 — coming soon to UE5 — with AI Frame Generation to further enhance interactivity in the viewport.

Scionti perfected his lighting with Lumen, UE5’s fully dynamic global illumination and reflections system, supported by GeForce RTX GPUs.

“Nanite meshes were useful to have high polygons for close up details,” noted Scionti. “For lighting, I used the sun and sky, but to add even more light, I inserted rectangular light sources outside each opening, like the windows and the broken wall.”

To complete the video, Scionti added a deliberately paced, instrumental score which consists of a piano, violin, synthesizer and drum. The music injects an unexpected emotional element to the piece.

Scionti reflected on his creative journey, which he considers a relentless pursuit of knowledge and perfecting his craft. “The pride of seeing years of commitment and passion being recognized is incredible, and that drive has led me to where I am today,” he said.

To embark on an Unreal Engine 5-powered creative journey through desert scenes, alien landscapes, abandoned towns, castle ruins and beyond, check out the latest NVIDIA Studio Standout featuring some of the most talented 3D artists, including Scionti.

For more, explore Scionti’s Instagram.

Join the #From2Dto3D challenge

Scionti brought Old Abandoned Haunted Mansion from 2D beauty into 3D realism — and the NVIDIA Studio team wants to see more 2D to 3D progress.

Join the #From2Dto3D challenge this month for a chance to be featured on the NVIDIA Studio social media channels, like @juliestrator, whose delightfully cute illustration is elevated in 3D:

Here’s another example for inspiration…

Credit: @juliestrator pic.twitter.com/RsY6nLg8pT

— NVIDIA Studio (@NVIDIAStudio) October 3, 2022

Entering is quick and easy. Simply post a 2D piece of art next to a 3D rendition of it on Instagram, Twitter or Facebook. And be sure to tag #From2Dto3D to enter.

Get creativity-inspiring updates directly to your inbox by subscribing to the NVIDIA Studio newsletter.

Back in 2018, BERT got people talking about how machine learning models were learning to read and speak. Today, large language models, or LLMs, are growing up fast, showing dexterity in all sorts of applications.

They’re, for one, speeding drug discovery, thanks to research from the Rostlab at Technical University of Munich, as well as work by a team from Harvard, Yale and New York University and others. In separate efforts, they applied LLMs to interpret the strings of amino acids that make up proteins, advancing our understanding of these building blocks of biology.

It’s one of many inroads LLMs are making in healthcare, robotics and other fields.

A Brief History of LLMs

Transformer models — neural networks, defined in 2017, that can learn context in sequential data — got LLMs started.

Researchers behind BERT and other transformer models made 2018 “a watershed moment” for natural language processing, a report on AI said at the end of that year. “Quite a few experts have claimed that the release of BERT marks a new era in NLP,” it added.

Developed by Google, BERT (aka Bidirectional Encoder Representations from Transformers) delivered state-of-the-art scores on benchmarks for NLP. In 2019, it announced BERT powers the company’s search engine.

Google released BERT as open-source software, spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs.

For instance, Meta created an enhanced version called RoBERTa, released as open-source code in July 2017. For training, it used “an order of magnitude more data than BERT,” the paper said, and leapt ahead on NLP leaderboards. A scrum followed.

Scaling Parameters and Markets

For convenience, score is often kept by the number of an LLM’s parameters or weights, measures of the strength of a connection between two nodes in a neural network. BERT had 110 million, RoBERTa had 123 million, then BERT-Large weighed in at 354 million, setting a new record, but not for long.

In 2020, researchers at OpenAI and Johns Hopkins University announced GPT-3, with a whopping 175 billion parameters, trained on a dataset with nearly a trillion words. It scored well on a slew of language tasks and even ciphered three-digit arithmetic.

“Language models have a wide range of beneficial applications for society,” the researchers wrote.

Experts Feel ‘Blown Away’

Within weeks, people were using GPT-3 to create poems, programs, songs, websites and more. Recently, GPT-3 even wrote an academic paper about itself.

“I just remember being kind of blown away by the things that it could do, for being just a language model,” said Percy Liang, a Stanford associate professor of computer science, speaking in a podcast.

GPT-3 helped motivate Stanford to create a center Liang now leads, exploring the implications of what it calls foundational models that can handle a wide variety of tasks well.

Toward Trillions of Parameters

Last year, NVIDIA announced the Megatron 530B LLM that can be trained for new domains and languages. It debuted with tools and services for training language models with trillions of parameters.

“Large language models have proven to be flexible and capable … able to answer deep domain questions without specialized training or supervision,” Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, said at that time.

Making it even easier for users to adopt the powerful models, the NVIDIA Nemo LLM service debuted in September at GTC. It’s an NVIDIA-managed cloud service to adapt pretrained LLMs to perform specific tasks.

Transformers Transform Drug Discovery

The advances LLMs are making with proteins and chemical structures are also being applied to DNA.

Researchers aim to scale their work with NVIDIA BioNeMo, a software framework and cloud service to generate, predict and understand biomolecular data. Part of the NVIDIA Clara Discovery collection of frameworks, applications and AI models for drug discovery, it supports work in widely used protein, DNA and chemistry data formats.

NVIDIA BioNeMo features multiple pretrained AI models, including the MegaMolBART model, developed by NVIDIA and AstraZeneca.

LLMs Enhance Computer Vision

Transformers are also reshaping computer vision as powerful LLMs replace traditional convolutional AI models. For example, researchers at Meta AI and Dartmouth designed TimeSformer, an AI model that uses transformers to analyze video with state-of-the-art results.

Experts predict such models could spawn all sorts of new applications in computational photography, education and interactive experiences for mobile users.

In related work earlier this year, two companies released powerful AI models to generate images from text.

OpenAI announced DALL-E 2, a transformer model with 3.5 billion parameters designed to create realistic images from text descriptions. And recently, Stability AI, based in London, launched Stability Diffusion,

Writing Code, Controlling Robots

LLMs also help developers write software. Tabnine — a member of NVIDIA Inception, a program that nurtures cutting-edge startups — claims it’s automating up to 30% of the code generated by a million developers.

Taking the next step, researchers are using transformer-based models to teach robots used in manufacturing, construction, autonomous driving and personal assistants.

For example, DeepMind developed Gato, an LLM that taught a robotic arm how to stack blocks. The 1.2-billion parameter model was trained on more than 600 distinct tasks so it could be useful in a variety of modes and environments, whether playing games or animating chatbots.

“By scaling up and iterating on this same basic approach, we can build a useful general-purpose agent,” researchers said in a paper posted in May.

It’s another example of what the Stanford center in a July paper called a paradigm shift in AI. “Foundation models have only just begun to transform the way AI systems are built and deployed in the world,” it said.

Learn how companies around the world are implementing LLMs with NVIDIA Triton for many use cases.