Kategorie: KI Trends

The quest for knowledge at work can feel like searching for a needle in a haystack. But what if the haystack itself could reveal where the needle is?

That’s the promise of large language models, or LLMs, the subject of this week’s episode of the NVIDIA AI Podcast featuring DeeDee Das and Eddie Zhou, founding engineers at Silicon Valley-based startup Glean, in conversation with our host, Noah Kravitz.

With LLMs, the haystack can become a source of intelligence, helping guide knowledge workers on what they need to know.

Glean is focused on providing better tools for enterprise search by indexing everything employees have access to in the company, including Slack, Confluence, GSuite and much more. The company raised a series C financing round last year, valuing the company at $1 billion.

Large language models can provide a comprehensive view of the enterprise and its data, which makes finding the information needed to get work done easier.

In the podcast, Das and Zhou discuss the challenges and opportunities of bringing LLMs into the enterprise, and how this technology can help people spend less time searching and more time working.

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Sequoia Capital’s Pat Grady and Sonya Huang on Generative AI

Pat Grady and Sonya Huang, partners at Sequoia Capital, to discuss their recent essay, “Generative AI: A Creative New World.” The authors delve into the potential of generative AI to enable new forms of creativity and expression, as well as the challenges and ethical considerations of this technology. They also offer insights into the future of generative AI.

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

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

GANTheftAuto: Harrison Kinsley on AI-Generated Gaming Environments

Humans playing games against machines is nothing new, but now computers can develop games for people to play. Programming enthusiast and social media influencer Harrison Kinsley created GANTheftAuto, an AI-based neural network that generates a playable chunk of the classic video game Grand Theft Auto V.

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As the meteoric rise of ChatGPT demonstrates, generative AI can unlock enormous potential for companies, teams and individuals. 

Whether simplifying time-consuming tasks or accelerating 3D workflows to boost creativity and productivity, generative AI is already making an impact across industries — and there’s much more to come.

How generative AI is paving the way for the future will be a key topic at NVIDIA GTC, a free, global conference for the era of AI and the metaverse, taking place online March 20-23. 

Dozens of sessions will dive into topics around generative AI — from conversational text to the creation of virtual worlds from images. Here’s a sampling: 

Fireside Chat With NVIDIA founder and CEO Jensen Huang and OpenAI’s Ilya Suskever: Join this conversation to learn more about the future of AI.
How Generative AI Is Transforming the Creative Process: In this fireside chat, Scott Belsky, chief product officer at Adobe, and Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, will discuss the powerful impact and future direction of generative AI.
Generative AI Demystified: Discover how generative AI enables businesses to improve products and services. NVIDIA’s Bryan Catanzaro will discuss major developments in generative AI and share popular use cases driving cutting-edge generative applications.
Generating Modern Masterpieces: MoMA Dreams Become a Reality: Hear from multimedia artist Refik Anadol, as well as Museum of Modern Art curators Michelle Kuo and Paola Antonelli, who’ll discuss how AI helped transform the archive of data from New York’s legendary modern art museum into a real-time art piece — the first of its kind in a major art museum.
How Generative AI Will Transform the Fashion Industry: See examples of how the latest generative tools are used in fashion, and hear from experts on their experiences in building a practice based on AI.
Emerging Tech in Animation Pre-Production: Learn how Sony Pictures Animation is using generative AI to improve the creative pre-production and storytelling processes.
3D by AI: How Generative AI Will Make Building Virtual Worlds Easier: See some of NVIDIA’s latest work in generative AI models for creating 3D content and scenes, and explore how these tools and research can help 3D artists in their workflows.

Many more sessions on generative AI are available to explore at GTC, and registration is free. Join to discover the latest AI technology innovations and breakthroughs.

Featured image courtesy of Refik Anadol.

Brian Spears says his children will enjoy a more sustainable planet, thanks in part to AI and high performance computing (HPC) simulations.

“I believe I’ll see fusion energy in my lifetime, and I’m confident my daughters will see a fusion-powered world,” said the 45-year-old principal investigator at Lawrence Livermore National Laboratory who helped demonstrate the physics of the clean and abundant power source, making headlines worldwide.

Results from the experiment hit Spears’ inbox at 5:30 a.m. on Dec. 5 last year.

“I had to rub my eyes to make sure I wasn’t misreading the numbers,” he recalled.

A Nuclear Family  

Once he assured himself, he scurried downstairs to share the news with his wife, a chemical engineer at the lab who’s pioneering ways to 3D print glass, and also once worked on the fusion program.

Brian Spears

“One of my friends described us as a Star Trek household — I work on the warp core and she works on the replicator,” he quipped.

In a tweet storm after the lab formally announced the news, Spears shared his excitement with the world.

“Exhausted by an amazing day … Daughters sending me screenshots with breaking news about Mom and Dad’s work … Being a part of something amazing for humanity.”

In another tweet, he shared the technical details.

“Used two million joules of laser energy to crush a capsule 100x smoother than a mirror. It imploded to half the thickness of a hair. For 100 trillionths of a second, we produced ten petawatts of power. It was the brightest thing in the solar system.”

AI Helps Call the Shots

A week before the experiment, Spears’ team analyzed its precision HPC design, then predicted the result with AI. Two atoms would fuse into one, releasing energy in a process simply called ignition.

It was the most exciting of thousands of AI predictions in what’s become the two-step dance of modern science. Teams design experiments in HPC simulations, then use data from the actual results to train AI models that refine the next simulation.

AI uncovers details about the experiments hard for humans to see. For example, it tracked the impact of minute imperfections in the imploding capsule researchers blasted with 192 lasers to achieve fusion.

A look inside the fusion experiment. Graphic courtesy of Lawrence Livermore National Laboratory.

“You need AI to understand the complete picture,” Spears said.

It’s a big canvas, filled with math describing the complex details of atomic physics.

A single experiment can require hundreds of thousands of relatively small simulations. Each takes a half day on a single node of a supercomputer.

The largest 3D simulations — called the kitchen sinks — consume about half of Sierra, the world’s sixth fastest HPC system, packing 17,280 NVIDIA GPUs.

Edge AI Guides Experiments

AI also helps scientists create self-driving experiments. Neural networks can make split-second decisions about which way to take an experiment based on results they process in real time.

For example, Spears, his colleagues and NVIDIA collaborated on an AI-guided experiment last year that fired lasers up to three times a second. It created the kind of proton beams that could someday treat a cancer patient.

“In the course of a day, you can get the kind of bright beam that may have taken you months or years of human-designed experiments,” Spears said. “This approach of AI at the edge will save orders of magnitude of time for our subject-matter experts.”

Directing lasers fired many times a second will also be a key job inside tomorrow’s nuclear fusion reactors.

Navigating the Data Deluge

AI’s impacts will be felt broadly across both scientific and industrial fields, Spears believes.

“Over the last decade we’ve produced more simulation and experimental data than we’re trained to deal with,” he said.

That deluge, once a burden for scientists, is now fuel for machine learning.

“AI is putting scientists back in the driver seat so we can move much more quickly,” he said.

Spears explained the ignition result in an interview (starting 8:19) with Government Matters.

Spears also directs an AI initiative at the lab that depends on collaborations with companies including NVIDIA.

“NVIDIA helps us look over the horizon, so we can take the next step in using AI for science,” he said

A Brighter Future

It’s hard work with huge impacts, like leaving a more resilient planet for the next generation.

Asked whether his two daughters plan a career in science, Spears beams. They’re both competitive swimmers who play jazz trumpet with interests in everything from bioengineering to art.

“As we say in science, they’re four pi, they cover the whole sky,” 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.

ManvsMachine steps In the NVIDIA Studio this week to share insights behind fractal art — which uses algorithms to artistically represent calculations — derived from geometric objects as digital images and animations.

Ethos Reflected

Founded in London in 2007, ManvsMachine is a multidimensional creative company specializing in design, film and visual arts.

ManvsMachine works closely with the world’s leading brands and agencies, including Volvo, Adidas, Nike and more, to produce award-winning creative content.

The team at ManvsMachine finds inspiration from a host of places: nature and wildlife, conversations, films, documentaries, as well as new and historic artists of all mediums.

Fractal Food

For fans of romanesco broccoli, the edible flower bud resembling cauliflower in texture and broccoli in taste might conjure mild, nutty, sweet notes that lend well to savory pairings. For ManvsMachine, it presented an artistic opportunity.

Romanesco broccoli is the inspiration behind ‘Roving Romanesco.’

The Roving Romanesco animation started out as a series of explorations based on romanesco broccoli, a prime example of a fractal found in nature.

ManvsMachine’s goal was to find an efficient way of recreating it in 3D and generate complex geometry using a simple setup.

The genesis of the animation revolved around creating a phyllotaxis pattern, an arrangement of leaves on a plant stem, using the high-performance expression language VEX in SideFX’s Houdini software.

Points offset at 137.5 degrees, known as the golden angle.

This was achieved by creating numerous points and offsetting each from the previous one by 137.5 degrees, known as the golden or “perfect circular” angle, while moving outward from the center. The built-in RTX-accelerated Karma XPU renderer enabled fast simulation models powered by the team’s GeForce RTX 3090 GPUs.

Individual florets begin to form.

The team added simple height and width to the shapes using ramp controls then copied geometry onto those points inside a loop.

Romanesco broccoli starts to come together.

With the basic structure intact, ManvsMachine sculpted florets individually to create a stunning 3D model in the shape of romanesco broccoli. The RTX-accelerated Karma XPU renderer dramatically sped up animations of the shape, as well.

The project was then imported to Foundry’s Nuke software for compositing and final touch-ups. When pursuing a softer look, ManvsMachine counteracted the complexity of the animation with some “easy-on-the-eyes” materials and color choices with a realistic depth of field.

Many advanced nodes in Nuke are GPU accelerated, which gave the team another speed advantage.

Projects like Roving Romanesco represent the high-quality work ManvsMachine strives to deliver for clients.

“Our ethos is reflected in our name,” said ManvsMachine. “Equal importance is placed on ideas and execution. Rather than sell an idea and then work out how to make it later, the preference is to present clients with the full picture, often leading with technique to inform the creative.”

Designers, directors, visual effects artists and creative producers — team ManvsMachine.

Check out @man.vs.machine on Instagram for more inspirational work.

Artists looking to hone their Houdini skills can access Studio Shortcuts and Sessions on the NVIDIA Studio YouTube channel. Discover exclusive step-by-step tutorials from industry-leading artists, watch inspiring community showcases and more, powered by NVIDIA Studio hardware and software.

Follow NVIDIA Studio on Instagram, Twitter and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter.

Streaming video on PCs through Google Chrome and Microsoft Edge browsers is getting a GeForce RTX-sized upgrade today with the release of RTX Video Super Resolution (VSR).

Nearly 80% of internet bandwidth today is streaming video. And 90% of that content streams at 1080p or lower, including from popular sources like Twitch.tv, YouTube, Netflix, Disney+ and Hulu.

However, when viewers use displays higher than 1080p — as do many PC users — the browser must scale the video to match the resolution of the display. Most browsers use basic upscaling techniques, which result in final images that are soft or blurry.

With RTX VSR, GeForce RTX 40 and 30 Series GPU users can tap AI to upscale lower-resolution content up to 4K, matching their display resolution. The AI removes blocky compression artifacts and improves the video’s sharpness and clarity.

Just like putting on a pair of prescription glasses can instantly snap the world into focus, RTX Video Super Resolution gives viewers on GeForce RTX 40 and 30 Series PCs a clear picture into the world of streaming video.

RTX VSR is available now as part of the latest GeForce Game Ready Driver, which delivers the best experience for new game launches like Atomic Heart and THE FINALS closed beta.

The Evolution of AI Upscaling

AI upscaling is the process of converting lower-resolution media to a higher resolution by putting low-resolution images through a deep learning model to predict the high-resolution versions. To make these predictions with high accuracy, a neural network model must be trained on countless images at different resolutions.

4K displays can muddy visuals by having to stretch lower-resolution images to fit their screen. Using AI to upscale streamed video makes lower-resolution images fit with unrivaled crispness.

The deployed AI model can then take low-resolution video and produce incredible sharpness and enhanced details that no traditional scaler can recreate. Edges look sharper, hair looks scruffier and landscapes pop with striking clarity.

In 2019, an early version of this technology was released with SHIELD TV. It was a breakthrough that improved streamed content targeted for TVs, mostly ranging from 480p to 1080p, and optimized for a 10-foot viewing experience.

PC viewers are typically seated much closer than TV viewers to their displays, requiring a higher level of processing and refinement for upscaling. With GeForce RTX 40 and 30 Series GPUs, users now have extremely powerful AI processors with Tensor Cores, enabling a new generation of AI upscaling through RTX VSR.

How RTX Video Super Resolution Works

RTX VSR is a breakthrough in AI pixel processing that dramatically improves the quality of streamed video content beyond edge detection and feature sharpening.

Blocky compression artifacts are a persistent issue in streamed video. Whether the fault of the server, the client or the content itself, issues often become amplified with traditional upscaling, leaving a less pleasant visual experience for those watching streamed content.

Click the image to see the differences between bicubic upscaling (left) and RTX Video Super Resolution.

RTX VSR reduces or eliminates artifacts caused by compressing video — such as blockiness, ringing artifacts around edges, washout of high-frequency details and banding on flat areas — while reducing lost textures. It also sharpens edges and details.

The technology uses a deep learning network that performs upscaling and compression artifact reduction in a single pass. The network analyzes the lower-resolution video frame and predicts the residual image at the target resolution. This residual image is then superimposed on top of a traditional upscaled image, correcting artifact errors and sharpening edges to match the output resolution.

The deep learning network is trained on a wide range of content with various compression levels. It learns about types of compression artifacts present in low-resolution or low-quality videos that are otherwise absent in uncompressed images as a reference for network training. Extensive visual evaluation is employed to ensure that the generated model is effective on nearly all real-world and gaming content.

Getting Started

RTX VSR requires a GeForce RTX 40 or 30 Series GPU and works with nearly all content streamed in Google Chrome and Microsoft Edge.

The feature also requires updating to the latest GeForce Game Ready Driver, available today, or the next NVIDIA Studio Driver releasing in March. Both Chrome (version 110.0.5481.105 or higher) and Edge (version 110.0.1587.56) have updated recently to support RTX VSR.

To enable it, launch the NVIDIA Control Panel and open “Adjust video image settings.” Check the super resolution box under “RTX video enhancement” and select a quality from one to four — ranging from the lowest impact on GPU performance to the highest level of upscaling improvement.

Learn more, including other setup configurations, in this NVIDIA Knowledge Base article.

From scaling mountains in the annual California Death Ride bike challenge to creating a low-cost, open-source ventilator in the early days of the COVID-19 pandemic, NVIDIA Chief Scientist Bill Dally is no stranger to accomplishing near-impossible feats.

On Friday, he achieved another rare milestone: induction into the Silicon Valley Engineering Council’s Hall of Fame.

The aim of the council — a coalition of engineering societies, including the Institute of Electrical and Electronics Engineers, SAE International and the Association for Computing Machinery — is to promote engineering programs and enhance society through science.

Since 1990, its Hall of Fame has honored engineers who have accomplished significant professional achievements while serving their profession and the wider community.

Previous inductees include industry luminaries such as Intel founders Robert Noyce and Gordon Moore, former president of Stanford University and MIPS founder John Hennessy, and Google distinguished engineer and professor emeritus at UC Berkeley David Patterson.

Recognizing ‘an Industry Leader’

In accepting the distinction, Dally said, “I am honored to be inducted into the Silicon Valley Hall of Fame. The work for which I am being recognized is part of a large team effort. Many faculty and students participated in the stream processing research at Stanford, and a very large team at NVIDIA was involved in translating this research into GPU computing. It is a really exciting time to be a computer engineer.”

“The future is bright with a lot more demanding applications waiting to be accelerated using the principles of stream processing and accelerated computing.”

His induction kicked off with a video featuring colleagues and friends, spanning his career across Caltech, MIT,  Stanford and NVIDIA.

In the video, NVIDIA founder and CEO Jensen Huang describes Dally as “an extraordinary scientist, engineer, leader and amazing person.”

Fei-Fei Li, professor of computer science at Stanford and co-director of the Stanford Institute for Human-Centered AI, commended Dally’s journey “from an academic scholar and a world-class researcher to an industry leader” who is spearheading one of the “biggest digital revolutions of our time in terms of AI — both software and hardware.”

Following the tribute video, Fred Barez, chair of the Hall of Fame committee and professor of mechanical engineering at San Jose State University, took the stage. He said of Dally: “This year’s inductee has made significant contributions, not just to his profession, but to Silicon Valley and beyond.”

Underpinning the GPU Revolution

As the leader of NVIDIA Research for nearly 15 years, Dally has built a team of more than 300 scientists around the globe, with groups covering a wide range of topics, including AI, graphics, simulation, computer vision, self-driving cars and robotics.

Prior to NVIDIA, Dally advanced the state of the art in engineering at some of the world’s top academic institutions. His development of stream processing at Stanford led directly to GPU computing, and his contributions are responsible for much of the technology used today in high-performance computing networks.

Telcos are seeking industry-standard solutions that can run 5G, AI applications and immersive graphics workloads on the same server — including for computer vision and the metaverse.

To meet this need, NVIDIA is developing a new AI-on-5G solution that combines 5G vRAN, edge AI and digital twin workloads on an all-in-one, hyperconverged and GPU-accelerated system.

The lower cost of ownership enabled by such a system would help telcos drive revenue growth in smart cities, as well as the retail, entertainment and manufacturing industries, to support a multitrillion-dollar, 5G-enabled ecosystem.

The AI-on-5G system consists of:

Fujitsu’s virtualized 5G Open RAN product suite, which was developed as part of the 5G Open RAN ecosystem experience (OREX) project promoted by NTT DOCOMO. It also includes Fujitsu’s virtualized central unit (vCU) and distributed unit (vDU), plus other virtualized software functions of vRAN from Fujitsu.
The NVIDIA Aerial software development kit for 5G vRAN; NVIDIA Omniverse for building and operating custom 3D pipelines and large-scale simulations; NVIDIA RTX Virtual Workstation (vWS) software; and NVIDIA CloudXR for streaming extended reality.
Hardware includes the NVIDIA A100X and L40 converged accelerators.

OREC has supported performance verification and evaluation tests for this system.

Collaborating With Fujitsu

“Fujitsu is delivering a fully virtualized 5G vRAN together with multi-access edge computing on the same high-performance, energy-efficient, versatile and scalable computing infrastructure,” said Masaki Taniguchi, senior vice president and head of mobile systems at Fujitsu. “This combination, powered by AI and XR applications, enables telcos to deliver ultra-low latency services, highly optimized TCO and energy-efficient performance.”

The announcement is a step toward accomplishing the O-RAN alliance’s goal of enabling software-defined, AI-driven, cloud-native, fully programmable, energy-efficient and commercially ready telco-grade 5G Open RAN solutions. It’s also consistent with OREC’s goal of implementing a widely adopted, high-performance and multi-vendor 5G vRAN for both public and enterprise 5G deployments.

The all-in-one system uses GPUs to accelerate the software-defined 5G vRAN, as well as the edge AI and graphics applications, without bespoke hardware accelerators nor a specific telecom CPU. This ensures that the GPUs can accelerate the vRAN (based on NVIDIA Aerial), AI video analytics (based on NVIDIA Metropolis), streaming immersive extended reality (XR) experiences (based on NVIDIA CloudXR) and digital twins (based on NVIDIA Omniverse).

“Telcos and their customers are exploring new ways to boost productivity, efficiency and creativity through immersive experiences delivered over 5G networks,” said Ronnie Vasishta, senior vice president of telecom at NVIDIA. “At Mobile World Congress, we are bringing those visions into reality, showcasing how a single GPU-enabled server can support workloads such as NVIDIA Aerial for 5G, CloudXR for streaming virtual reality and Omniverse for digital twins.”

The AI-on-5G system is part of a growing portfolio of 5G solutions from NVIDIA that are driving transformation in the telecommunications industry. Anchored on the NVIDIA Aerial SDK and A100X converged accelerators — combined with BlueField DPUs and a suite of AI frameworks — NVIDIA provides a high-performance, software-defined, cloud-native, AI-enabled 5G for on-premises and telco operators’ RAN.

Telcos working with NVIDIA can gain access to thousands of software vendors and applications in the ecosystem, which can help address enterprise needs in smart cities, retail, manufacturing, industrial and mining.

NVIDIA and Fujitsu will demonstrate the new AI-on-5G system at Mobile World Congress in Barcelona, running Feb. 27-March 2, at hall 4, stand 4E20.

Advancements in whole genome sequencing have ignited a revolution in digital biology.

Genomics programs across the world are gaining momentum as the cost of high-throughput, next-generation sequencing has declined.

Whether used for sequencing critical-care patients with rare diseases or in population-scale genetics research, whole genome sequencing is becoming a fundamental step in clinical workflows and drug discovery.

But genome sequencing is just the first step. Analyzing genome sequencing data requires accelerated compute, data science and AI to read and understand the genome. With the end of Moore’s law, the observation that there’s a doubling every two years in the number of transistors in an integrated circuit, new computing approaches are necessary to lower the cost of data analysis, increase the throughput and accuracy of reads, and ultimately unlock the full potential of the human genome.

An Explosion in Bioinformatics Data

Sequencing an individual’s whole genome generates roughly 100 gigabytes of raw data. That more than doubles after the genome is sequenced using complex algorithms and applications such as deep learning and natural language processing.

As the cost of sequencing a human genome continues to decrease, volumes of sequencing data are exponentially increasing.

An estimated 40 exabytes will be required to store all human genome data by 2025. As a reference, that’s 8x more storage than would be required to store every word spoken in history.

Many genome analysis pipelines are struggling to keep up with the expansive levels of raw data being generated.

Accelerated Genome Sequencing Analysis Workflows

Sequencing analysis is complicated and computationally intensive, with numerous steps required to identify genetic variants in a human genome.

Deep learning is becoming important for base calling right within the genomic instrument using RNN- and convolutional neural network (CNN)-based models. Neural networks interpret image and signal data generated by instruments and infer the 3 billion nucleotide pairs of the human genome. This is improving the accuracy of the reads and ensuring that base calling occurs closer to real time, further hastening the entire genomics workflow, from sample to variant call format to final report.

For secondary genomic analysis, alignment technologies use a reference genome to assist with piecing a genome back together after the sequencing of DNA fragments.

BWA-MEM, a leading algorithm for alignment, is helping researchers rapidly map DNA sequence reads to a reference genome. STAR is another gold-standard alignment algorithm used for RNA-seq data that delivers accurate, ultrafast alignment to better understand gene expressions.

The dynamic programming algorithm Smith-Waterman is also widely used for alignment, a step that’s accelerated 35x on the NVIDIA H100 Tensor Core GPU, which includes a dynamic programming accelerator.

Uncovering Genetic Variants

One of the most critical stages of sequencing projects is variant calling, where researchers identify differences between a patient’s sample and the reference genome. This helps clinicians determine what genetic disease a critically ill patient might have, or helps researchers look across a population to discover new drug targets. These variants can be single-nucleotide changes, small insertions and deletions, or complex rearrangements.

GPU-optimized and -accelerated callers such as the Broad Institute’s GATK — a genome analysis toolkit for germline variant calling — increase speed of analysis. To help researchers remove false positives in GATK results, NVIDIA collaborated with the Broad Institute to introduce NVScoreVariants, a deep learning tool for filtering variants using CNNs.

Deep learning-based variant callers such as Google’s DeepVariant increase accuracy of calls, without the need for a separate filtering step. DeepVariant uses a CNN architecture to call variants. It can be retrained to fine-tune for enhanced accuracy with each genomic platform’s outputs.

Secondary analysis software in the NVIDIA Clara Parabricks suite of tools has accelerated these variant callers up to 80x. For example, germline HaplotypeCaller’s runtime is reduced from 16 hours in a CPU-based environment to less than five minutes with GPU-accelerated Clara Parabricks.

Accelerating the Next Wave of Genomics

NVIDIA is helping to enable the next wave of genomics by powering both short- and long-read sequencing platforms with accelerated AI base calling and variant calling. Industry leaders and startups are working with NVIDIA to push the boundaries of whole genome sequencing.

For example, biotech company PacBio recently announced the Revio system, a new long-read sequencing system featuring NVIDIA Tensor Core GPUs. Enabled by a 20x increase in computing power relative to prior systems, Revio is designed to sequence human genomes with high-accuracy long reads at scale for under $1,000.

Oxford Nanopore Technologies offers the only single technology that can sequence any-length DNA or RNA fragments in real time. These features allow the rapid discovery of more genetic variation. Seattle Children’s Hospital recently used the high-throughput nanopore sequencing instrument PromethION to understand a genetic disorder in the first few hours of a newborn’s life.

Ultima Genomics is offering high-throughput whole genome sequencing at just $100 per sample, and Singular Genomics’ G4 is the most powerful benchtop system.

Learn More

At NVIDIA GTC, a free AI conference taking place online March 20-23, speakers from PacBio, Oxford Nanopore, Genomic England, KAUST, Stanford, Argonne National Labs and other leading institutions will share the latest AI advances in genomic sequencing, analysis and genomic large language models for understanding gene expression.

The conference features a keynote from NVIDIA founder and CEO Jensen Huang on Tuesday, March 21, at 8 a.m. PT.

NVIDIA Clara Parabricks is free for students and researchers. Get started today or try a free hands-on lab to experience the toolkit in action.

Cloudy British weather is the butt of many jokes — but the United Kingdom’s national power grid is making the most of its sunshine.

With the help of Open Climate Fix, a nonprofit product lab, the control room of the National Grid Electricity System Operator (ESO) is testing AI models that provide granular, near-term forecasts of sunny and cloudy conditions over the country’s solar panels.

These insights can help ESO, the U.K.’s electric grid operator, address a key challenge in renewable energy: Sudden cloud cover can cause a significant dip in solar power generation, so grid operators ask fossil fuel plants to overproduce energy as backup.

With better forecasts, ESO could cut down on the extra fossil fuel energy held as reserve — improving efficiency while decreasing carbon footprint.

“Traditional weather models aren’t very good at predicting clouds, but using AI and satellite imagery, we can bring a lot more accuracy to solar forecasting,” said Dan Travers, co-founder of Open Climate Fix, a U.K.-based startup. “Solar energy is really effective at displacing coal, but grid operators need accurate forecasts to make it possible to integrate large amounts of solar generation — so we see a lot of opportunity in bringing this solution to coal-heavy electric grids worldwide.”

Open Climate Fix is a member of NVIDIA Inception, a global program that offers cutting-edge startups expertise, technology and go-to-market support. The team publishes its datasets, dozens of models and open-source code to HuggingFace and GitHub.

Each colored dot on the map represents a solar photovoltaic system. Blue dots represent low solar-energy output, yellow dots signify high output and black dots are systems with no data.

AI to Catch a Cloud and Pin It Down

Before the advent of renewable energy, the experts managing the electric grid day-to-day only had to worry about the variability of demand across the network — making sure there was enough power generated to keep up with air conditioners during a heat wave, or electric stoves and appliances on weekday evenings.

By adding renewables such as wind and solar energy to the mix, the energy grid must also account for weather-related variation in the level of supply. Satellite images provide the most up-to-date view to determine when clouds are coming between photovoltaic panels and the sun.

Open Climate Fix’s AI models are trained on terabytes of satellite data captured at five-minute intervals over Europe, the Middle East and North Africa. Additional data sources include years’ worth of hourly weather predictions at ten-kilometer resolution, topographic maps, information about the time of day and the sun’s position in the sky, and live readings from around solar panels across the U.K.

The team is using some of the most recent deep learning models for weather modeling including MetNet, GraphCast and Deep Generative Model of Radar. They’ve shown that their transformer-based AI models are 3x better at predicting solar energy generation than the forecasts generated by ESO’s traditional methods. The increased precision can help ESO reach its goal of being able to operate a zero-carbon electric grid by 2025.

“The physics-based forecasting models are powerful for predicting weather on the scale of days and weeks, but take hours to produce — making them ill-suited for predictions at the hour or minute level,” said Travers. “But with satellite images captured at intervals of a few minutes, we can get closer to a live view of cloud cover.”

AI’s Working on Sunshine

Cloud cover is of particular concern in the U.K., where cities including London, Birmingham and Glasgow receive an average of 1,400 or fewer hours of sunshine each year — less than half that of Los Angeles. But even in desert climates where cloudy days are rare, Open Climate Fix’s AI models could be repurposed to detect when solar panels would be covered by dust from a sandstorm.

In addition to forecasting for the entire U.K., the nonprofit is also developing models that can forecast how much energy individual solar panels will capture. This data could help large solar farm operators understand and maximize their energy output. Smart home companies, too, could use the information to optimize energy use from solar panels on customers’ roofs — giving homeowners insights about when to run power-hungry devices or schedule electric vehicle charging.

Open Climate Fix uses a cluster of NVIDIA RTX A6000 GPUs granted through an NVIDIA Hardware Grant to power its work. When training multiple models at the same time, the team shifts its overflow workload to NVIDIA A100 Tensor Core GPUs available through cloud service providers.

“The hardware grants have helped us develop and iterate on our models more easily,” said Jacob Bieker, a machine learning researcher at Open Climate Fix. “When our team is first debugging and training a model, it’s two or three times faster to do so locally.”

To learn more about AI accelerating decarbonization, boosting grid resiliency and driving energy efficiency, register free for NVIDIA GTC, which takes place online, March 20-23.

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Main image of National Grid ESO Electricity National Control Center, courtesy of ESO Media Center

A million developers across the globe are now using the NVIDIA Jetson platform for edge AI and robotics to build innovative technologies. Plus, more than 6,000 companies — a third of which are startups — have integrated the platform with their products.

These milestones and more will be celebrated during the NVIDIA Jetson Edge AI Developer Days at GTC, a global conference for the era of AI and the metaverse, taking place online March 20-23.

Register free to learn more about the Jetson platform and begin developing the next generation of edge AI and robotics.

One in a Million

Atlanta-based Kris Kersey, the mind behind the popular YouTube channel Kersey Fabrications, is one developer using the NVIDIA Jetson platform for his one-in-a-million technological innovations.

He created a fully functional Iron Man helmet that could be straight out of the Marvel Comics films. It uses the NVIDIA Jetson Xavier NX 8GB developer kit as the core of the “Arc Reactor” powering its heads-up display — a transparent display that presents information wherever the user’s looking.

In just over two years, Kersey built from scratch the wearable helmet, complete with object detection and other on-screen sensors that would make Tony Stark proud.

“The software design was more than half the work on the project, and for me, this is the most exciting, interesting part,” Kersey said. “The software takes all of the discrete hardware components and makes them into a remarkable system.”

To get started, Kersey turned to GitHub where he found “Hello AI World,” a guide for deploying deep-learning inference networks and deep vision primitives with the NVIDIA TensorRT software development kit and NVIDIA Jetson. He then wrote a wrapper code to connect his own project.

Watch Kersey document his Iron Man project from start to finish:

This 3D-printed helmet is just the beginning for Kersey, who’s aiming to build a full Iron Man suit later this year. He plans to make the entire project’s code open source, so anyone who dreams of becoming a superhero can try it for themselves.

Jetson Edge AI Developer Days at GTC

Developers like Kersey can register for the free Jetson Edge AI Developer Days at GTC, which feature NVIDIA experts who’ll cover the latest Jetson hardware, software and partners. Sessions include:

Level Up Edge AI and Robotics With NVIDIA Jetson Orin Platform
Accelerate Edge AI With NVIDIA Jetson Software
Getting the Most Out of Your Jetson Orin Using NVIDIA Nsight Developer Tools
Bring Your Products to Market Faster With the NVIDIA Jetson Ecosystem
Design a Complex Architecture on NVIDIA Isaac ROS

Plus, there’ll be a Connect with Experts session focusing on the Jetson platform that provides a deep-dive Q&A with embedded platform engineers from NVIDIA on Tuesday, March 21, at 12 p.m. PT. This interactive session offers a unique opportunity to meet, in a group or individually, with the minds behind NVIDIA products and get your questions answered. Space is limited and on a first-come, first-served basis.

Additional Sessions by Category

GTC sessions will also cover robotics, intelligent video analytics and smart spaces. Below are some of the top sessions in these categories.

Robotics:

Deep Reinforcement Learning With Real-World Data
Building a Robot Digital Twin in Isaac Sim: A Step-by-Step Example
Creating Digital Twins and Simulations of Industrial Robotic Workcells for Smart Factories
Real-World Implementations of Simulation for Next-Generation Robotics

Computer Vision and AI Video Analytics:

End-to-End, Cloud-Native Vision AI: From Synthetic Data to Solution Deployment
AI Models Made Simple Using TAO
An Intro Into NVIDIA DeepStream and AI-Streaming Software Tools
Advancing AI Applications With Custom GPU-Powered Plugins for NVIDIA DeepStream
Leveraging NVIDIA Pretrained Models to Build Loss-Prevention Applications Faster

Smart Cities and Spaces:

Automating Airports From Curbside to Tarmac
Achieving Seamless Traffic Management With AI Vision and Digital Twins
Leveraging Omniverse and Metropolis Microservices Platforms to Optimize Warehouse Operations
3D Synthetic Data: Simplifying and Accelerating the Training of Vision AI Models for Industrial Workflows

Check out the latest Jetson community projects for ideas to replicate or be inspired by.

Grab the latest Jetson modules and developer kits from the NVIDIA Jetson store.

And sign up for the NVIDIA Developer Program to connect with Jetson developers from around the world and get access to the latest software and software development kits, including NVIDIA JetPack.