Posts in category: nVidia AI

AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting.

A large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets.

Large language models are among the most successful applications of transformer models. They aren’t just for teaching AIs human languages, but for understanding proteins, writing software code, and much, much more.

In addition to accelerating natural language processing applications — like translation, chatbots and AI assistants — large language models are used in healthcare, software development and use cases in many other fields.

What Are Large Language Models Used For?

Language is used for more than human communication.

Code is the language of computers. Protein and molecular sequences are the language of biology. Large language models can be applied to such languages or scenarios in which communication of different types is needed.

These models broaden AI’s reach across industries and enterprises, and are expected to enable a new wave of research, creativity and productivity, as they can help to generate complex solutions for the world’s toughest problems.

For example, an AI system using large language models can learn from a database of molecular and protein structures, then use that knowledge to provide viable chemical compounds that help scientists develop groundbreaking vaccines or treatments.

Large language models are also helping to create reimagined search engines, tutoring chatbots, composition tools for songs, poems, stories and marketing materials, and more.

How Do Large Language Models Work?

Large language models learn from huge volumes of data. As its name suggests, central to an LLM is the size of the dataset it’s trained on. But the definition of “large” is growing, along with AI.

Now, large language models are typically trained on datasets large enough to include nearly everything that has been written on the internet over a large span of time.

Such massive amounts of text are fed into the AI algorithm using unsupervised learning — when a model is given a dataset without explicit instructions on what to do with it. Through this method, a large language model learns words, as well as the relationships between and concepts behind them. It could, for example, learn to differentiate the two meanings of the word “bark” based on its context.

And just as a person who masters a language can guess what might come next in a sentence or paragraph — or even come up with new words or concepts themselves — a large language model can apply its knowledge to predict and generate content.

Large language models can also be customized for specific use cases, including through techniques like fine-tuning or prompt-tuning, which is the process of feeding the model small bits of data to focus on, to train it for a specific application.

Thanks to its computational efficiency in processing sequences in parallel, the transformer model architecture is the building block behind the largest and most powerful LLMs.

Top Applications for Large Language Models

Large language models are unlocking new possibilities in areas such as search engines, natural language processing, healthcare, robotics and code generation.

The popular ChatGPT AI chatbot is one application of a large language model. It can be used for a myriad of natural language processing tasks.

The nearly infinite applications for LLMs also include:

• Retailers and other service providers can use large language models to provide improved customer experiences through dynamic chatbots, AI assistants and more.
• Search engines can use large language models to provide more direct, human-like answers.
• Life science researchers can train large language models to understand proteins, molecules, DNA and RNA.
• Developers can write software and teach robots physical tasks with large language models.
• Marketers can train a large language model to organize customer feedback and requests into clusters, or segment products into categories based on product descriptions.
• Financial advisors can summarize earnings calls and create transcripts of important meetings using large language models. And credit-card companies can use LLMs for anomaly detection and fraud analysis to protect consumers.
• Legal teams can use large language models to help with legal paraphrasing and scribing.

Running these massive models in production efficiently is resource-intensive and requires expertise, among other challenges, so enterprises turn to NVIDIA Triton Inference Server, software that helps standardize model deployment and deliver fast and scalable AI in production.

Where to Find Large Language Models

In June 2020, OpenAI released GPT-3 as a service, powered by a 175-billion-parameter model that can generate text and code with short written prompts.

In 2021, NVIDIA and Microsoft developed Megatron-Turing Natural Language Generation 530B, one of the world’s largest models for reading comprehension and natural language inference, which eases tasks like summarization and content generation.

And HuggingFace last year introduced BLOOM, an open large language model that’s able to generate text in 46 natural languages and over a dozen programming languages.

Another LLM, Codex, turns text to code for software engineers and other developers.

NVIDIA offers tools to ease the building and deployment of large language models:

• NVIDIA NeMo LLM service provides a fast path to customizing large language models and deploying them at scale using NVIDIA’s managed cloud API, or through private and public clouds.
• NVIDIA NeMo Megatron, part of the NVIDIA AI platform, is a framework for easy, efficient, cost-effective training and deployment of large language models. Designed for enterprise application development, NeMo Megatron provides an end-to-end workflow for automated distributed data processing, training large-scale, customized GPT-3, T5 and multilingual T5 models, and deploying models for inference at scale.
• NVIDIA BioNeMo is a domain-specific managed service and framework for large language models in proteomics, small molecules, DNA and RNA. It’s built on NVIDIA NeMo Megatron for training and deploying large biomolecular transformer AI models at supercomputing scale.

Challenges of Large Language Models

Scaling and maintaining large language models can be difficult and expensive.

Building a foundational large language model often requires months of training time and millions of dollars.

And because LLMs require a significant amount of training data, developers and enterprises can find it a challenge to access large-enough datasets.

Due to the scale of large language models, deploying them requires technical expertise, including a strong understanding of deep learning, transformer models and distributed software and hardware.

Many leaders in tech are working to advance development and build resources that can expand access to large language models, allowing consumers and enterprises of all sizes to reap their benefits.

Learn more about large language models.

GeForce NOW RTX 4080 SuperPODs are rolling out now, bringing RTX 4080-class performance and features to Ultimate members — including support for NVIDIA Ada Lovelace GPU architecture technologies like NVIDIA DLSS 3. 

This GFN Thursday brings updates to some of GeForce NOW’s hottest games that take advantage of these amazing technologies, all from the cloud.

Plus, RTX 4080 SuperPOD upgrades are nearly finished in the London data center, expanding the number of regions where Ultimate members can experience the most powerful cloud gaming technology on the planet. Look for updates on Twitter once the upgrade is complete and be sure to check back each week to see which cities light up next on the map.

Members can also look for six more supported games in the GeForce NOW library this week. 

AI-Powered Performance

NVIDIA DLSS has revolutionized graphics rendering, using AI and GeForce RTX Tensor Cores to boost frame rates while delivering crisp, high-quality images that rival native resolution.

Powered by new hardware capabilities of the Ada Lovelace architecture, DLSS 3 generates entirely new high-quality frames, rather than just pixels. It combines DLSS Super Resolution technology and DLSS Frame Generation to reconstruct seven-eighths of the displayed pixels, accelerating performance.

DLSS 3 games are backwards compatible with DLSS 2 technology — when developers integrate DLSS 3, DLSS 2, aka DLSS Super Resolution, is supported by default. Additionally, integrations of DLSS 3 include NVIDIA Reflex, reducing system latency for all GeForce RTX users and making games more responsive.

Support for DLSS 3 is growing, and soon GeForce NOW Ultimate members can experience this technology in new updates to HITMAN World of Assassination and Marvel’s Midnight Suns.

A Whole New ‘World of Assassination’

The critically acclaimed HITMAN 3 from IOI transforms into HITMAN World of Assassination, an upgrade that includes content from HITMAN 1, HITMAN 2 and HITMAN 3. With DLSS 3 support, streaming from the cloud in 4K looks better than ever, even with ray tracing and settings cranked to the max.

Become legendary assassin Agent 47 and use creativity and improvisation to execute ingenious, spectacular eliminations in sprawling sandbox locations all around the globe. Stick to the shadows to stalk and eliminate targets — or take them out in plain sight.

Along with DLSS 3 support, Ultimate members can enjoy ray-traced opaque reflections and shadows in the world of HITMAN as they explore open-world missions with multiple ways to succeed. 

Deadpool Does DLSS 3

Marvel’s Midnight Suns’ first downloadable content, The Good, The Bad, and the Undead, adds Deadpool to the team roster, along with new story missions, new enemies and more. Add in DLSS 3 support coming soon, and Ultimate members have a lot to look forward to.

Launched last month to critical acclaim, VGC awarded Marvel’s Midnight Suns with a five-out-of-five rating, calling it a “modern strategy classic.” PC Gamer said it was “completely brilliant” and scored it an 88 out of 100, and Rock Paper Shotgun called it “one of the best superhero games full stop.”

Ultimate members can explore the abbey grounds and get to know the Merc with a Mouth at up to 4K resolutions and 120 frames per second, or immerse themselves in their mission with ultrawide resolutions at up to 3840 x 1600 at 120 frames per second — plus many other popular formats including 3440 x 1440 and 2560 x 1080. 

GeForce NOW members can also take their games and save data with them wherever they go, from underpowered PCs to Macs, Samsung and LG TVs, mobile devices and Chromebooks.

Game On

Get ready to game: Six more games join the supported list in the GeForce NOW library this week:

• Tom Clancy’s Ghost Recon: Breakpoint (New release on Steam, Jan. 23)
• Oddballers (New release on Ubisoft Connect, Jan. 26)
• Watch Dogs: Legion (New release on Steam, Jan. 26)
• Cygnus Enterprises (Steam)
• Rain World (Steam)
• The Eternal Cylinder (Steam)

There’s only one question left to kick off a weekend full of gaming in the cloud. Let us know on Twitter or in the comments below.

What’s your favorite game to play in the cloud with #RTXON?

— NVIDIA GeForce NOW (@NVIDIAGFN) January 25, 2023

As its name suggests, Orbital Sidekick is creating technology that acts as a buddy in outer space, keeping an eye on the globe using satellites to help keep it safe and sustainable.

The San Francisco-based startup, a member of the NVIDIA Inception program, enables commercial and government users to optimize sustainable operations and security with hyperspectral intelligence — information collected from across the electromagnetic spectrum.

“Space-based hyperspectral intelligence basically breaks up the spectrum of light so it’s possible to see what’s happening at a chemical level without needing an aircraft,” said Kaushik Bangalore, vice president of payload engineering at Orbital Sidekick, or OSK.

Founded in 2016, OSK is among the first to use hyperspectral intelligence to detect hydrocarbon or gas leaks. These are some of the world’s most pressing energy issues — 6,000 U.S. pipeline incidents from 2002-2021 resulted in over $11 billion in damages.

“Previous industry-standard ways of detecting such issues were unreliable as they used small aircraft and pilots looking out the window for leaks, depending on the trained eye rather than sensors or other technologies,” said Bangalore.

OSK operates a constellation of satellites that collect hyperspectral imagery from space. That data is processed and analyzed in real time using the NVIDIA Jetson edge AI platform. Then, insights — like the type of leak at a GPS point, its size and its urgency — can be viewed on a screen by users of OSK’s SIGMA Monitor platform.

The technology accomplishes what a pilot would, but much more quickly, objectively and with higher accuracy, Bangalore said.

Sustainable Operations

OSK technologies have so far monitored more than 20,000 kilometers of pipelines for various customers, according to Tushar Prabhakar, its founder and chief operating officer.

The platform has detected nearly 100 suspected methane leaks, 200 suspected liquid hydrocarbon leaks or contamination issues, and more than 300 intrusive events related to digging or construction activities, Prabhakar added. OSK helped eliminate the potential for these events to become serious energy crises.

“We’re taking hyperspectral intelligence to the finest commercial resolution that the world has ever seen to make the Earth a more sustainable place,” Bangalore said. “The biggest challenge with hyperspectral imagery is dealing with huge amounts of data, which can be up to 400x the size of 2D visual data. NVIDIA technology helps process this data in real time.”

OSK uses the NVIDIA Jetson AGX Xavier module as an AI engine at the satellites’ edge to process the hyperspectral data collected from various sensors and crunch algorithms for leak detection.

The module, along with the NVIDIA CV-CUDA and CUDA Python software toolkits, have sped up OSK’s analysis by 5x, according to Bangalore. This acceleration enhances the platform’s ability to detect and recognize anomalies from space — then project the data back to Earth.

“There are around 15 sun-synchronous orbits per day,” Bangalore said. “With NVIDIA Jetson AGX Xavier, we can process all the data taken onboard a satellite in an orbit within that same orbit, enabling continuous data capture.”

In 2018, OSK’s previous-generation system was launched on the International Space Station. Its data was analyzed using the NVIDIA Jetson TX2 module.

In addition, OSK uses the next-generation NVIDIA Jetson AGX Orin module for an aerial version of the platform that collects hyperspectral imagery from airplanes. Compared to the previous-generation module, the Jetson AGX Orin — with upgraded memory and speed — can run larger amounts of map data streamed in real time to pilots, Bangalore said.

“We chose the NVIDIA Jetson platform because it offers off-the-shelf products for industrial applications with extended shock, vibration and temperature, and software that has been optimized for the NVIDIA GPU architecture,” Bangalore said.

And as a member of NVIDIA Inception, a free, global program for cutting-edge startups, OSK received technical support to optimize the team’s use of such safety features and SDK acceleration.

Versatile Use Cases

Hyperspectral intelligence offers a multitude of applications. For this reason, the OSK platform is deployed across a broad range of customers, including the U.S. Department of Defense and energy sector.

Energy Transfer, a major pipeline operator, will use OSK’s GHOSt constellation for asset monitoring.

For the commercial oil and gas industry, OSK technology helps detect gas and hydrocarbon leaks, allowing pipeline operators to quickly halt work and fix issues.

To accelerate the energy transition, the platform can enhance exploration of lithium, cobalt and more, display a hyperspectral index of areas on a map that have signals of the elements, and differentiate between these materials and soil.

Creating sustainable supply chains for battery materials like lithium is key to advancing the global energy transition and scaling electric vehicle adoption, as lithium-ion batteries power the majority of EVs. The EV battery market is projected to reach over $218 billion in 2027, and EV sales are estimated to reach up to 50 million units by 2030.

“Our tech can help discover lithium, and prevent methane or greenhouse gasses from being let out into the atmosphere,” Bangalore said. “It’s a very direct impact, and it’s what the planet needs.”

Read more about innovative energy startups, including MinervaCQ, which is using speech AI to coach contact-center agents in retail energy, and Skycatch, which is building digital twins to make mining and construction sites safer, more efficient and sustainable.

Learn more about NVIDIA’s work in energy and apply to join NVIDIA Inception.

More than 600 entrepreneurs, developers, researchers and executives from across the Nordics flocked Tuesday to Stockholm’s sleek Sergel Hub conference center in a further sign of the strength of the region’s AI ecosystem.

The highlight: a far-reaching conversation between NVIDIA founder and CEO Jensen Huang and Swedish industrialist Marcus Wallenberg exploring the intersections of AI, green computing, and Scandinavia’s broader tech scene.

“This generative AI phenomenon is creating a whole slew of new startups, new ideas, new video editing, image editing, new text,” Huang said. “It can achieve capabilities that previous computing platforms cannot.”

The Berzelius supercomputer, named for Jöns Jacob Berzelius, one of the fathers of modern chemistry, has just been upgraded to 94 NVIDIA DGX A100 AI computing systems, delivering nearly half an exaflop of AI performance, placing it among the world’s 100 fastest AI supercomputers.

“Years ago, Marcus and I started talking about a new way of doing computer science. Having a key instrument, like Berzelius, would be a fundamental instrument of future science,” Huang told the audience. “The work that is done on this instrument would make tremendous impacts to life sciences, material sciences, physical sciences and computer science.”

Maximum Efficiency, Minimal Impact

The rising use of electricity is one of the causes of global warming, and powerful, energy-efficient computers are crucial to fighting climate change through green computing.

Huang explained that whether for data centers or the latest smartphone, computer chips, systems and software must be designed and used to maximize energy efficiency and minimize environmental impact.

“Companies large and small have to sign up for the carbon footprint that we use to build the work that we do,” said Huang. “If there’s an opportunity for us to help accelerate workloads and reduce energy use and improve energy efficiency, we will.”

Sweden’s Role in AI

The upgrade comes as AI is powering change in every industry across the globe, with leaders from across the Nordics accelerating the growth of some of the world’s most powerful AI solutions, explained Wallenberg.

“From the perspective of the foundations, we’re trying to work for the betterment of Sweden by promoting the areas of research, technology and medicine,” said Wallenberg, whose family has for generations been deeply involved across the nation’s economy. “We are working together as a team to create possibilities and the foundations for more work to be done.”

The Berzelius system was used for training the first Swedish large language model. Increasing in size 10x every year for the last few years, large language models are just one state-of-the-art AI technology that promises transformation through learned knowledge.

Neural networks trained with massive datasets on powerful systems, LLMs are accelerating discoveries across industries such as healthcare and climate science with software frameworks like NVIDIA BioNeMo. Models like ChatGPT are making a name for themselves as a new way to use AI.

“You can connect models together to retrieve new information so that models like ChatGPT could report on the news today, who won that game, or the latest weather,” Huang said. “The combination of these capabilities means not only the ability to respond and answer questions and write stories, but it can also write programs and solve problems.”

Knowledge From Data

Solving problems requires reliable, physically accurate data. The industrial metaverse, where digital twins of real factories, rail networks or retail stores can be created, is already being used by large companies like Amazon, BMW, Ericsson and Siemens.

Following the conversation between Huang and Wallenberg, Staffan Truvé, CTO and co-founder of cybersecurity company Recorded Future, talked about how data can be used to model intelligence as a digital twin to get an end-to-end view of threats and targets.

“Today, there are three major converging threat areas. Physical, cyber and influence, which is the threat to our brains,” Truvé explained. “By creating an intelligence graph, we’re building a full picture of a threat.”

Digital twins are not the only way to gather valuable insights when developing for the future. Sara Mazur, vice executive director of the Knut and Alice Wallenberg Foundation and chair of the Wallenberg AI Autonomous Systems and Software Program, highlighted the importance of collaboration between academia and industry.

Sweden is outfitting its AI supercomputer for a journey to the cutting edge of machine learning, robotics and healthcare.

It couldn’t ask for a better guide than Anders Ynnerman (above). His signature blue suit, black spectacles and gentle voice act as calm camouflage for a pioneering spirit.

Early on, he showed a deep interest in space, but his career took a different direction. He established the country’s first network of supercomputing centers and went on to pioneer scientific visualization technologies used in hospitals and museums around the world.

Today, he leads Sweden’s largest research effort, WASP — the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program — focused on AI innovation.

The Big Picture

“This is a year when people are turning their focus to sustainability challenges we face as a planet,” said the Linköping University professor. “Without advances in AI and other innovations, we won’t have a sustainable future.”

To supercharge environmental efforts and more, Sweden will upgrade its Berzelius supercomputer. Based on the NVIDIA DGX SuperPOD, it will deliver nearly half an exaflop of AI performance, placing it among the world’s 100 fastest AI supercomputers.

“A machine like Berzelius is fundamental not only for the results it delivers, but the way it catalyzes expertise in Sweden,” he said. “We’re a knowledge-driven nation, so our researchers and companies need access to the latest technology to compete.”

AI Learns Swedish

In June, the system trained GPT-SW3, a family of large language models capable of drafting a speech or answering questions in Swedish.

Today, a more powerful version sports 20 billion parameters, a popular measure of a neural network’s smarts. It can help developers write software and handle other complex tasks.

Long term, researchers aim to train a version with a whopping 175 billion parameters that’s also fluent in Nordic languages like Danish and Norwegian.

One of Sweden’s largest banks is already exploring use of the latest GPT-SW3 variant for a chatbot and other applications.

A Memory Boost

To build big AIs, Berzelius will add 34 NVIDIA DGX A100 systems to its cluster of 60 that makeup the SuperPOD. The new units will sport GPUs with 80GB of memory each.

“Having really fat nodes with large memory is important for some of these models,” Ynnerman said. Atos, the system integrator, is providing “a very smooth ride getting the whole process set up,” he added.

Seeking a Cure for Cancer

In healthcare, a data-driven life sciences program, funded by the Wallenberg Foundation, will be a major Berzelius user. The program spans 10 universities and will, among other applications, employ AI to understand protein folding, fundamentally important to understanding diseases like cancer.

Others will use Berzelius to improve detection of cancer cells and navigate the massive mounds of data in human genomes.

Some researchers are exploring tools such as NVIDIA Omniverse Avatar Cloud Engine and NVIDIA BotMaker to create animated patients. Powered by GPT-SW3, they could help doctors practice telemedicine skills.

Robots in Zero Gravity

Sweden’s work in image and video recognition will get a boost from Berzelius. Such algorithms advance work on the autonomous systems used in modern factories and warehouses.

One project is exploring how autonomous systems act in space and undersea. It’s a topic close to the heart of a recent addition to WASP, researcher Christer Fuglesang, who was named Sweden’s first astronaut in 1992.

Fuglesang went to the International Space Station in 2006 and 2008. Later, as a professor of physics at Sweden’s Royal Institute of Technology, he collaborated with Ynnerman on live shows about life in space, presented in the WISDOME dome theater at the Visualization Center C Ynnerman founded and directs.

Thanks to his expertise in visualization, “I can go to Mars whenever I want,” Ynnerman quipped.

He took NVIDIA founder and CEO Jensen Huang and Marcus Wallenberg — scion of Sweden’s leading industrial family — on a tour of outer space at the dome to mark the Berzelius upgrade. The dome can show the Martian surface in 8K resolution at 120 frames per second, thanks to its use of 12 NVIDIA Quadro RTX 8000 GPUs.

Inspiring the Next Generation

Ynnerman’s algorithms have touched millions who’ve seen visualizations of Egyptian mummies at the British Museum.

“That makes me even more proud than some of my research papers because many are young people we can inspire with a love for science and technology,” he said.

A passion for science and technology has attracted more than 400 active Ph.D. candidates so far to WASP, which is on the way to exceeding its goal of 600 grads by 2031.

But even a visualization specialist can’t be everywhere. So Ynnerman’s pet project will use AI to create a vibrant, virtual museum guide.

“I think we can provide more people a ‘wow’ experience — I want a copilot when I’m navigating the universe,” he said.

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. We’re also deep diving on new GeForce RTX 40 Series GPU features, technologies and resources, and how they dramatically accelerate content creation.

Artist Ducky 3D creates immersive experiences through vibrant visuals and beautiful 3D environments in the alien-inspired animation Stylized Alien Landscape — this week In the NVIDIA Studio.

Ducky 3D is a modern Renaissance man who works with musicians from around the world, creating tour packages and visual art related to the music industry. As a 3D fanatic who specializes in Blender, he often guides emerging and advanced 3D artists to new creative heights. It’s no surprise that his weekly YouTube tutorials have garnered over 400,000 subscribers.

Stylized Alien Landscape is uniquely individualistic and was built entirely in Blender using geometry nodes, or geo nodes.

The use of geo nodes in Blender has recently skyrocketed. That’s because they virtually make modeling a completely procedural process — allowing for non-linear, non-destructive workflows and the instancing of objects — to create incredibly detailed scenes using small amounts of data. Geo nodes can also organically modify all types of geometry, including meshes, curves, volumes, instances and more. Many of these were edited in the making of Stylized Alien Landscape.

Ducky 3D opened a new scene, created a simple cube and applied several popular geo nodes, including random value, triangulate and dual mesh. By simple trial and error with numeric values, he was able to create a provocative, alien-inspired visual.

“I use geometry nodes to take advantage of the dual mesh, which creates organic shapes by manipulating with simple deformations,” he said.

Simply adding a transform node to the mix got the animation going. Ducky 3D then copied all nodes and scaled the duplicated render to create two animations rotating simultaneously.

Next, Ducky 3D turned his focus to lighting the object, selecting the Blender Cycles renderer to do so.

“Rendering lighting is drastically better in Cycles, but you do you,” he said with candor.

Blender Cycles RTX-accelerated OptiX ray tracing in the viewport unlocks interactive, photoreal rendering for modeling and animation work.

Here, Ducky 3D can quickly create more realism in two ways: adding depth of field by playing with distance options and the flat shaded view, and bringing the background out of focus and the object into focus.

Volume “just makes things look cool,” Ducky 3D added. Selecting the world and clicking principled volume made the scene nearly photorealistic.

With the help of geo nodes, Ducky 3D refined the texture to his desired effect, using the bump node, color ramp and noise texture.

For more on the making of Stylized Alien Landscape, check out the video below.

“I needed my viewport to perform well enough to see detail through the added volume,” he said. “Thank goodness for the AI-powered NVIDIA OptiX ray tracing API that my GeForce RTX 4090 GPU enables.”

Ducky 3D accomplished the slightly odd atmosphere that he wanted for his piece through the addition of fog.

“Fog is tough to render, and the GPU helped me see my viewport clearly,” he said.

For more Blender tutorials, check out Ducky 3D’s YouTube channel or the NVIDIA Studio Blender playlist.

Enter the #NewYearNewArt Challenge 

A new year comes with new art, and we’d love to see yours! Use the hashtag #NewYearNewArt and tag @NVIDIAStudio to show off your most recent creations for a chance to be featured on our channels.

A new year means new art!

Join our Jan-Feb #NewYearNewArt challenge by sharing any new or relatively new art you’ve created for a chance to be featured on our channels!

Be sure to tag #NewYearNewArt and thanks to @AOi__Pan for sharing their new art. pic.twitter.com/lXiFLROhQh

— NVIDIA Studio (@NVIDIAStudio) January 10, 2023

There have been stunning animations like this lively work from the amazing @stillmanvisual.

Synapses (2023).

Little collaboration with @NVIDIAStudio for their #NewYearNewArt challenge pic.twitter.com/ZI6vkBLNGC

— 𝑺𝑻𝑰𝑳𝑳𝑴𝑨𝑵 (@stillmanvisual) January 23, 2023

There’s also explosive new content from @TheRealYarnHub featuring some action-packed, historically-based battles.

New video coming out tomorrow on @YouTube @NVIDIAStudio #NewYearNewArt @UnrealEngine @RoyalNavy @TeamYouTube @YouTubeCreators pic.twitter.com/iEdZGbrUGI

— Yarnhub (@TheRealYarnHub) January 19, 2023

Catch even more #NewYearNewArt entries from other creators on the NVIDIA Studio Instagram stories.

Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter.

Add AI to the list of defenses against identity attacks, one of the most common and hardest breach to prevent.

More than 40% of all data compromises involved stolen credentials, according to the 2022 Verizon Data Breach Investigations Report. And a whopping 80% of all web application breaches involved credential abuse.

“Credentials are the favorite data type of criminal actors because they are so useful for masquerading as legitimate users on the system,” the report said.

In today’s age of zero trust, security experts say it’s not a matter of if but when they’ll experience an identity attack.

A Response From R&D

The director of cybersecurity engineering and R&D at NVIDIA, Bartley Richardson, articulates the challenge simply.

“We need to look for when Bartley is not acting like Bartley,” he said.

Last year, his team described a concept called digital fingerprinting. In the wake of highly publicized attacks in February, he came up with a simple but ambitious idea for implementing it.

A Big Ask

He called a quick meeting with his two tech leads to share the idea. Richardson told them he wanted to create a deep learning model for every account, server, application and device on the network.

The models would learn individual behavior patterns and alert security staff when an account was acting in an uncharacteristic way. That’s how they would deter attacks.

The tech leads thought it was a crazy idea. It was computationally impossible, they told him, and no one was even using GPUs for security yet.

Richardson listened to their concerns and slowly convinced them it was worth a try. They would start with just a model for every account.

Everybody’s Problem

Security managers know it’s a big-data problem.

Companies collect terabytes of data on network events every day. That’s just a fraction of the petabytes of events a day companies could log if they had the resources, according to Daniel Rohrer, NVIDIA’s vice president of software product security.

The fact that it’s a big-data problem is also good news, Rohrer said in a talk at GTC in September (watch free with registration). “We’re already well on the way to combining our cybersecurity and AI efforts,” he said.

Starting With a Proof of Concept

By mid-March, Richardson’s team was focused on ways to run thousands of AI models in tandem. They used NVIDIA Morpheus, an AI security software library announced a year earlier, to build a proof of concept in two months.

Once an entire, albeit crude, product was done, they spent another two months optimizing each portion.

Then they reached out to about 50 NVIDIANs to review their work — security operations and product security teams, and IT folks who would be alpha users.

An Initial Deployment

Three months later, in early October, they had a solution NVIDIA could deploy on its global networks — security software for AI-powered digital fingerprinting.

The software is a kind of LEGO kit, an AI framework anyone can use to create a custom cybersecurity solution.

Version 2.0 is running across NVIDIA’s networks today on just four NVIDIA A100 Tensor Core GPUs. IT staff can create their own models, changing aspects of them to create specific alerts.

Tested and Released

NVIDIA is making these capabilities available in a digital fingerprinting AI workflow included with NVIDIA AI Enterprise 3.0 announced in December.

For identity attackers, “the models Bartley’s team built have anomaly scores that are off the charts, and we’re able to visualize events so we can see things in new ways,” said Jason Recla, NVIDIA’s senior director of information security.

As a result, instead of facing a tsunami of 100 million network events a week, an IT team may have just 8-10 incidents to investigate daily. That cuts the time to detect certain attack patterns from weeks to minutes.

Tailoring AI for Small Events

The team already has big ideas for future versions.

“Our software works well on major identity attacks, but it’s not every day you have an incident like that,” Richardson said. “So, now we’re tuning it with other models to make it more applicable to everyday vanilla security incidents.”

Meanwhile, Richardson’s team used the software to create a proof of concept for a large consulting firm.

“They wanted it to handle a million records in a tenth of a second. We did it in a millionth of a second, so they’re fully on board,” Richardson said.

The Outlook for AI Security

Looking ahead, the team has ideas for applying AI and accelerated computing to secure digital identities and generate hard-to-find training data.

Richardson imagines passwords and multi-factor authentication will be replaced by models that know how fast a person types, with how many typos, what services they use and when they use them. Such detailed digital identities will prevent attackers from hijacking accounts and pretending they are legitimate users.

Data on network events is gold for building AI models that harden networks, but no one wants to share details of real users and break-ins. Synthetic data, generated by a variant of digital fingerprinting, could fill the gap, letting users create what they need to fit their use case.

In the meantime, Recla has advice security managers can act on now.

“Get up to speed on AI,” he said. “Start investing in AI engineering and data science skills — that’s the biggest thing.”

Digital fingerprinting is not a panacea. It’s one more brick in an ever-evolving digital wall that a community of security specialists is building against the next big attack.

You can try this AI-powered security workflow live on NVIDIA LaunchPad starting Jan. 23. And you can watch the video below to learn more about digital fingerprinting.

For the past 500 years, the National Library of Sweden has collected virtually every word published in Swedish, from priceless medieval manuscripts to present-day pizza menus.

Thanks to a centuries-old law that requires a copy of everything published in Swedish to be submitted to the library — also known as Kungliga biblioteket, or KB — its collections span from the obvious to the obscure: books, newspapers, radio and TV broadcasts, internet content, Ph.D. dissertations, postcards, menus and video games. It’s a wildly diverse collection of nearly 26 petabytes of data, ideal for training state-of-the-art AI.

“We can build state-of-the-art AI models for the Swedish language since we have the best data,” said Love Börjeson, director of KBLab, the library’s data lab.

Using NVIDIA DGX systems, the group has developed more than two dozen open-source transformer models, available on Hugging Face. The models, downloaded by up to 200,000 developers per month, enable research at the library and other academic institutions.

“Before our lab was created, researchers couldn’t access a dataset at the library — they’d have to look at a single object at a time,” Börjeson said. “There was a need for the library to create datasets that enabled researchers to conduct quantity-oriented research.”

With this, researchers will soon be able to create hyper-specialized datasets — for example, pulling up every Swedish postcard that depicts a church, every text written in a particular style or every mention of a historical figure across books, newspaper articles and TV broadcasts.

Turning Library Archives Into AI Training Data

The library’s datasets represent the full diversity of the Swedish language — including its formal and informal variations, regional dialects and changes over time.

“Our inflow is continuous and growing — every month, we see more than 50 terabytes of new data,” said Börjeson. “Between the exponential growth of digital data and ongoing work digitizing physical collections that date back hundreds of years, we’ll never be finished adding to our collections.”

Soon after KBLab was established in 2019, Börjeson saw the potential for training transformer language models on the library’s vast archives. He was inspired by an early, multilingual, natural language processing model by Google that included 5GB of Swedish text.

KBLab’s first model used 4x as much — and the team now aims to train its models on at least a terabyte of Swedish text. The lab began experimenting by adding Dutch, German and Norwegian content to its datasets after finding that a multilingual dataset may improve the AI’s performance.

NVIDIA AI, GPUs Accelerate Model Development 

The lab started out using consumer-grade NVIDIA GPUs, but Börjeson soon discovered his team needed data-center-scale compute to train larger models.

“We realized we can’t keep up if we try to do this on small workstations,” said Börjeson. “It was a no-brainer to go for NVIDIA DGX. There’s a lot we wouldn’t be able to do at all without the DGX systems.”

The lab has two NVIDIA DGX systems from Swedish provider AddPro for on-premises AI development. The systems are used to handle sensitive data, conduct large-scale experiments and fine-tune models. They’re also used to prepare for even larger runs on massive, GPU-based supercomputers across the European Union — including the MeluXina system in Luxembourg.

“Our work on the DGX systems is critically important, because once we’re in a high-performance computing environment, we want to hit the ground running,” said Börjeson. “We have to use the supercomputer to its fullest extent.”

The team has also adopted NVIDIA NeMo Megatron, a PyTorch-based framework for training large language models, with NVIDIA CUDA and the NVIDIA NCCL library under the hood to optimize GPU usage in multi-node systems.

“We rely to a large extent on the NVIDIA frameworks,” Börjeson said. “It’s one of the big advantages of NVIDIA for us, as a small lab that doesn’t have 50 engineers available to optimize AI training for every project.”

Harnessing Multimodal Data for Humanities Research

In addition to transformer models that understand Swedish text, KBLab has an AI tool that transcribes sound to text, enabling the library to transcribe its vast collection of radio broadcasts so that researchers can search the audio records for specific content.

KBLab is also starting to develop generative text models and is working on an AI model that could process videos and create automatic descriptions of their content.

“We also want to link all the different modalities,” Börjeson said. “When you search the library’s databases for a specific term, we should be able to return results that include text, audio and video.”

KBLab has partnered with researchers at the University of Gothenburg, who are developing downstream apps using the lab’s models to conduct linguistic research — including a project supporting the Swedish Academy’s work to modernize its data-driven techniques for creating Swedish dictionaries.

“The societal benefits of these models are much larger than we initially expected,” Börjeson said.

Images courtesy of Kungliga biblioteket

The abacus, sextant, slide rule and computer. Mathematical instruments mark the history of human progress.

They’ve enabled trade and helped navigate oceans, and advanced understanding and quality of life.

The latest tool propelling science and industry is AI computing.

AI Computing Defined

AI computing is the math-intensive process of calculating machine learning algorithms, typically using accelerated systems and software. It can extract fresh insights from massive datasets, learning new skills along the way.

It’s the most transformational technology of our time because we live in a data-centric era, and AI computing can find patterns no human could.

For example, American Express uses AI computing to detect fraud in billions of annual credit card transactions. Doctors use it to find tumors, finding tiny anomalies in mountains of medical images.

Three Steps to AI Computing

Before getting into the many use cases for AI computing, let’s explore how it works.

First, users, often data scientists, curate and prepare datasets, a stage called extract/transform/load, or ETL. This work can now be accelerated on NVIDIA GPUs with Apache Spark 3.0, one of the most popular open source engines for mining big data.

Second, data scientists choose or design AI models that best suit their applications.

Some companies design and train their own models from the ground up because they are pioneering a new field or seeking a competitive advantage. This process requires some expertise and potentially an AI supercomputer, capabilities NVIDIA offers.

Many companies choose pretrained AI models they can customize as needed for their applications. NVIDIA provides dozens of pretrained models and tools for customizing them on NGC, a portal for software, services, and support.

Third, companies sift their data through their models. This key step, called inference, is where AI delivers actionable insights.

The three-step process involves hard work, but there’s help available, so everyone can use AI computing.

For example, NVIDIA TAO Toolkit can collapse the three steps into one using transfer learning, a way of tailoring an existing AI model for a new application without needing a large dataset. In addition, NVIDIA LaunchPad gives users hands-on training in deploying models for a wide variety of use cases.

Inside an AI Model

AI models are called neural networks because they’re inspired by the web-like connections in the human brain.

If you slice into one of these AI models, it might look like a mathematical lasagna, made up of layers of linear algebra equations. One of the most popular forms of AI is called deep learning because it uses many layers.

If you zoom in, you’d see each layer is made up of stacks of equations. Each represents the likelihood that one piece of data is related to another.

AI computing multiplies together every stack of equations in every layer to find patterns. It’s a huge job that requires highly parallel processors sharing massive amounts of data on fast computer networks.

GPU Computing Meets AI

GPUs are the de facto engines of AI computing.

NVIDIA debuted the first GPU in 1999 to render 3D images for video games, a job that required massively parallel calculations.

GPU computing soon spread to use in graphics servers for blockbuster movies. Scientists and researchers packed GPUs into the world’s largest supercomputers to study everything from the chemistry of tiny molecules to the astrophysics of distant galaxies.

When AI computing emerged more than a decade ago, researchers were quick to embrace NVIDIA’s programmable platform for parallel processing. The video below celebrates this brief history of the GPU.

The History of AI Computing

The idea of artificial intelligence goes back at least as far as Alan Turing, the British mathematician who helped crack coded messages during WWII.

“What we want is a machine that can learn from experience,” Turing said in a 1947 lecture in London.

Acknowledging his insights, NVIDIA named one of its computing architectures for him.

Turing’s vision became a reality in 2012 when researchers developed AI models that could recognize images faster and more accurately than humans could. Results from the ImageNet competition also greatly accelerated progress in computer vision.

Today, companies such as Landing AI, founded by machine learning luminary Andrew Ng, are applying AI and computer vision to make manufacturing more efficient. And AI is bringing human-like vision to sports, smart cities and more.

AI Computing Starts Up Conversational AI

AI computing made huge inroads in natural language processing after the invention of the transformer model in 2017. It debuted a machine-learning technique called “attention” that can capture context in sequential data like text and speech.

Today, conversational AI is widespread. It parses sentences users type into search boxes. It reads text messages when you’re driving, and lets you dictate responses.

These large language models are also finding applications in drug discovery, translation, chatbots, software development, call center automation and more.

AI + Graphics Create 3D Worlds

Users in many, often unexpected, areas are feeling the power of AI computing.

The latest video games achieve new levels of realism thanks to real-time ray tracing and NVIDIA DLSS, which uses AI to deliver ultra-smooth game play on the GeForce RTX platform.

That’s just the start. The emerging field of neural graphics will speed the creation of virtual worlds to populate the metaverse, the 3D evolution of the internet.

To kickstart that work, NVIDIA released several neural graphics tools in August.

Use Cases for AI Computing

Cars, Factories and Warehouses

Car makers are embracing AI computing to deliver a smoother, safer driving experience and deliver smart infotainment capabilities for passengers.

Mercedes-Benz is working with NVIDIA to develop software-defined vehicles. Its upcoming fleets will deliver intelligent and automated driving capabilities powered by an NVIDIA DRIVE Orin centralized computer. The systems will be tested and validated in the data center using DRIVE Sim software, built on NVIDIA Omniverse, to ensure they can safely handle all types of scenarios.

At CES, the automaker announced it will also use Omniverse to design and plan manufacturing and assembly facilities at its sites worldwide.

BMW Group is also among many companies creating AI-enabled digital twins of factories in NVIDIA Omniverse, making plants more efficient. It’s an approach also adopted by consumer giants such as PepsiCo for its logistic centers as shown in the video below.

Inside factories and warehouses, autonomous robots further enhance efficiency in manufacturing and logistics. Many are powered by the NVIDIA Jetson edge AI platform and trained with AI in simulations and digital twins using NVIDIA Isaac Sim.

In 2022, even tractors and lawn mowers became autonomous with AI.

In December, Monarch Tractor, a startup based in Livermore, Calif., released an AI-powered electric vehicle to bring automation to agriculture. In May, Scythe, based in Boulder, Colo., debuted its M.52 (below), an autonomous electric lawn mower packing eight cameras and more than a dozen sensors.

Securing Networks, Sequencing Genes

The number and variety of use cases for AI computing are staggering.

Cybersecurity software detects phishing and other network threats faster with AI-based techniques like digital fingerprinting.

In healthcare, researchers broke a record in January 2022 sequencing a whole genome in well under eight hours thanks to AI computing. Their work (described in the video below) could lead to cures for rare genetic diseases.

AI computing is at work in banks, retail shops and post offices. It’s used in telecom, transport and energy networks, too.

For example, the video below shows how Siemens Gamesa is using AI models to simulate wind farms and boost energy production.

As today’s AI computing techniques find new applications, researchers are inventing newer and more powerful methods.

Another powerful class of neural networks, diffusion models, became popular in 2022 because they could turn text descriptions into fascinating images. Researchers expect these models will be applied to many uses, further expanding the horizon for AI computing.

Robots are finally getting a grip.

Developers have been striving to close the gap on robotic gripping for the past several years, pursuing applications for multibillion-dollar industries. Securely gripping and transferring fast-moving items on conveyor belts holds vast promise for businesses.

Soft Robotics, a Bedford, Mass., startup, is harnessing NVIDIA Isaac Sim to help close the sim to real gap for a handful of robotic gripping applications. One area is perfecting gripping for pick and placement of foods for packaging.

Food packaging and processing companies are using the startup’s mGripAI system, which combines soft grasping with 3D vision and AI to grasp delicate foods such as proteins, produce and bakery items without damage.

“We’re selling the hands, the eyes and the brains of the picking solution,” said David Weatherwax, senior director of software engineering at Soft Robotics.

Unlike other industries that have adopted robotics, the $8 trillion food market has been slow to develop robots to handle variable items in unstructured environments, says Soft Robotics.

The company, founded in 2013, recently landed $26 million in Series C funding from Tyson Ventures, Marel and Johnsonville Ventures.

Companies such as Tyson Foods and Johnsonville are betting on adoption of robotic automation to help improve safety and increase production in their facilities. Both companies rely on Soft Robotics technologies.

Soft Robotics is a member of the NVIDIA Inception program, which provides companies with GPU support and AI platforms guidance.

Getting a Grip With Synthetic Data

Soft Robotics develops unique models for every one of its gripping applications, each requiring specific datasets. And picking from piles of wet, slippery chicken and other foods can be a tricky challenge.

Utilizing Omniverse and Isaac Sim, the company can create 3D renderings of chicken parts with different backgrounds, like on conveyor belts or in bins, and with different lighting scenarios.

The company taps into Isaac Replicator to develop synthetic data, generating hundreds of thousands of images per model and distributing that among an array of instances in the cloud. Isaac Replicator is a set of tools, APIs and workflows for generating synthetic data using Isaac Sim.

It also runs pose estimation models to help its gripping system see the angle of the item to pick.

NVIDIA A100 Tensor Core GPUs on site enable Soft Robotics to run split-second inference with the unique models for each application in these food-processing facilities. Meanwhile, simulation and training in Isaac Sim offers access to NVIDIA A100 GPUs for scaling up workloads.

“Our current setup is fully synthetic, which allows us to rapidly deploy new applications,” said Weatherwax. “We’re all in on Omniverse and Isaac Sim, and that’s been working great for us.”

Solving Issues With Occlusion, Lighting 

A big challenge at Soft Robotics is solving issues with occlusion for an understanding of how different pieces of chicken stack up and overlap one another when dumped into a pile. “How those form can be pretty complex,” he said.

Glares on wet chicken can potentially throw off detection models. “A key thing for us is the lighting, so the NVIDIA RTX-driven ray tracing is really important,” he added.

But where it really gets interesting is modeling it all in 3D and figuring out in a split second which item is the least obstructed in a pile and most accessible for a robot gripper to pick and place.

Building synthetic data sets with physics-based accuracy, Omniverse enables Soft Robotics to create such environments. “One of the big challenges we have is how all these amorphous objects form into a pile.”

Boosting Production Line Pick Accuracy

Production lines in food processing plants can move fast. But robots deployed with application-specific models promise to handle as many as 100 picks per minute.

Still a work in progress, success in such tasks hinges on accurate representations of piles of items, supported by training datasets that consider every possible way items can fall into a pile.

The objective is to provide the robot with the best available pick from a complex and dynamic environment. If food items fall off the conveyor belt or otherwise become damaged, then it is considered waste, which directly impacts yield.

Driving Production Gains 

Meat-packing companies rely on lines of people for processing chicken, but like so many other industries they have faced employee shortages. Some that are building new plants for food processing can’t even attract enough workers at launch, said Weatherwax.

“They are having a lot of staffing challenges, so there’s a push to automate,” he said.

The Omniverse-driven work for food processing companies has delivered a more than 10x increase in its simulation capacity, accelerating deployments times for AI picking systems from months to days.

And that’s enabling Soft Robotics customers to get a grip on more than just deploying automated chicken-picking lines — it’s ensuring that they’re covered for an employment challenge that has hit many industries, especially those with increased injury and health risks.

“Handling raw chicken is a job better suited for a robot,” he said.

Download Isaac Sim here to use the  Replicator features.