Posts in category: nVidia AI

Eliu Huerta is harnessing AI and high performance computing (HPC) to observe the cosmos more clearly.

For several years, the astrophysics researcher has been chipping away at a grand challenge, using data to detect signals produced by collisions of black holes and neutron stars. If his next big design for a neural network is successful, astrophysicists will use it to find more black holes and study them in more detail than ever.

Such insights could help answer fundamental questions about the universe. They may even add a few new pages to the physics textbook.

Huerta studies gravitational waves, the echoes from dense stellar remnants that collided long ago and far away. Since Albert Einstein first predicted them in his theory of relativity, academics debated whether these ripples in the space-time fabric really exist.

Researchers ended the debate in 2015 when they observed gravitational waves for the first time. They used pattern-matching techniques on data from the Laser Interferometer Gravitational-Wave Observatory (LIGO), home to some of the most sensitive instruments in science.

Detecting Black Holes Faster with AI

Confirming the presence of just one collision took a supercomputer to process data the instruments could gather in a single day. In 2017, Huerta’s team showed how a deep neural network running on an NVIDIA GPU could find gravitational waves with the same accuracy in a fraction of the time.

“We were orders of magnitude faster and we could even see signals the traditional techniques missed and we did not train our neural net for,” said Huerta, who leads AI and gravity groups at the National Center for Supercomputing Applications at the University of Illinois, Urbana-Champaign.

The AI model Huerta used was based on data from tens of thousands of waveforms. He trained it on a single NVIDIA GPU in less than three hours.

Seeing in Detail How Black Holes Spin

This year, Huerta and two of his students created a more sophisticated neural network that can detect how two colliding black holes spin. Their AI model even accurately measured the faint signals of a small black hole when it was merging with a larger one.

It required data on 1.5 million waveforms. An IBM POWER9-based system with 64 NVIDIA V100 Tensor Core GPUs took 12 hours to train the resulting neural network.

To accelerate their work, Huerta’s team got access to 1,536 V100 GPUs on 256 nodes of the IBM AC922 Summit supercomputer at Oak Ridge National Laboratory.

Taking advantage of NVIDIA NVLink, a connection between Summit’s GPUs and its IBM POWER9 CPUs, they trained the AI model in just 1.2 hours.

The results, described in a paper in Physics Letters B, “show how the combination of AI and HPC can solve grand challenges in astrophysics,” he said.

Interestingly, the team’s work is based on WaveNet, a popular AI model for converting text-to-speech. It’s one of many examples of how AI technology that’s rapidly evolving in consumer and enterprise use cases is crossing over to serve the needs of cutting-edge science.

The Next Big Leap into Black Holes

So far, Huerta has used data from supercomputer simulations to detect and describe the primary characteristics of gravitational waves. Over the next year, he aims to use actual LIGO data to capture the more nuanced secondary characteristics of gravitational waves.

“It’s time to go beyond low-hanging fruit and show the combination of HPC and AI can address production-scale problems in astrophysics that neither approach can accomplish separately,” he said.

The new details could help scientists determine more accurately where black holes collided. Such information could help them more accurately calculate the Hubble constant, a measure of how fast the universe is expanding.

The work may require tracking as many as 200 million waveforms, generating training datasets 100x larger than Huerta’s team used so far. The good news is, as part of their July paper, they’ve already determined their algorithms can scale to at least 1,024 nodes on Summit.

Tallying Up the Promise of HPC+AI

Huerta believes he’s just scratching the surface of the promise of HPC+AI. “The datasets will continue to grow, so to run production algorithms you need to go big, there’s no way around that,” he said.

Meanwhile, use of AI is expanding to adjacent areas. The team used neural nets to classify the many, many galaxies found in electromagnetic surveys of the sky, work NVIDIA CEO Jensen Huang highlighted in his GTC keynote in May.

Separately, one of Huerta’s grad students used AI to describe the turbulence when neutron stars merge more efficiently than previous techniques. “It’s another place where we can go into the traditional software stack scientists use and replace an existing model with an accelerated neural network,” Huerta said.

To accelerate the adoption of its work, the team has released as open source code its AI models for cosmology and gravitational wave astrophysics.

“When people read these papers they may think it’s too good to be true, so we let them convince themselves that we are getting the results we reported,” he said.

The Road to Space Started at Home

As is often the case with landmark achievements, there’s a parent to thank.

“My dad was an avid reader. We spent lots of time together doing math and reading books on a wide range of topics,” Huerta recalled.

“When I was 13, he brought home The Meaning of Relativity by Einstein. It was way over my head, but a really interesting read.

“A year or so later he bought A Brief History of Time by Stephen Hawking. I read it and thought it would be great to go to Cambridge and learn about gravity. Years later that actually happened,” he said.

The rest is a history that Huerta is still writing.

For more on Huerta’s work, check on an article from Oak Ridge National Laboratory.

At top: An artist’s impression of gravitational waves generated by binary neutron stars. Credit: R. Hurt, Caltech/NASA Jet Propulsion Lab

Paul Edwards is helping carry the age-old business of giving loans into the modern era of AI.

Edwards started his career modeling animal behavior as a Ph.D. in numerical ecology. He left his lab coat behind to lead a group of data scientists at Scotiabank, based in Toronto, exploring how machine learning can improve predictions of credit risk.

The team believes machine learning can both make the bank more profitable and help more people who deserve loans get them. They aim to share later this year some of their techniques in hopes of nudging the broader industry forward.

Scorecards Evolve from Pencils to AI

The new tools are being applied to scorecards that date back to the 1950s when calculations were made with paper and pencil. Loan officers would rank applicants’ answers to standard questions, and if the result crossed a set threshold on the scorecard, the bank could grant the loan.

With the rise of computers, banks replaced physical scorecards with digital ones. Decades ago, they settled on a form of statistical modeling called a “weight of evidence logistic regression” that’s widely used today.

One of the great benefits of scorecards is they’re clear. Banks can easily explain their lending criteria to customers and regulators. That’s why in the field of credit risk, the scorecard is the gold standard for explainable models.

“We could make machine-learning models that are bigger, more complex and more accurate than a scorecard, but somewhere they would cross a line and be too big for me to explain to my boss or a regulator,” said Edwards.

Machine Learning Models Save Millions

So, the team looked for fresh ways to build scorecards with machine learning and found a technique called boosting.

They started with a single question on a tiny scorecard, then added one question at a time. They stopped when adding another question would make the scorecard too complex to explain or wouldn’t improve its performance.

The results were no harder to explain than traditional weight-of-evidence models, but often were more accurate.

“We’ve used boosting to build a couple decision models and found a few percent improvement over weight of evidence. A few percent at the scale of all the bank’s applicants means millions of dollars,” he said.

XGBoost Upgraded to Accelerate Scorecards

Edwards’ team understood the potential to accelerate boosting models because they had been using a popular library called XGBoost on an NVIDIA DGX system. The GPU-accelerated code was very fast, but lacked a feature required to generate scorecards, a key tool they needed to keep their models simple.

Griffin Lacey, a senior data scientist at NVIDIA, worked with his colleagues to identify and add the feature. It’s now part of XGBoost in RAPIDS, a suite of open-source software libraries for running data science on GPUs.

As a result, the bank can now generate scorecards 6x faster using a single GPU compared to what used to require 24 CPUs, setting a new benchmark for the bank. “It ended up being a fairly easy fix, but we could have never done it ourselves,” said Edwards.

GPUs speed up calculating digital scorecards and help the bank lift their accuracy while maintaining the models’ explainability. “When our models are more accurate people who are deserving of credit get the credit they need,” said Edwards.

Riding RAPIDS to the AI Age

Looking ahead, Edwards wants to leverage advances from the last few decades of machine learning to refresh the world of scorecards. For example, his team is working with NVIDIA to build a suite of Python tools for scorecards with features that will be familiar to today’s data scientists.

“The NVIDIA team is helping us pull RAPIDS tools into our workflow for developing scorecards, adding modern amenities like Python support, hyperparameter tuning and GPU acceleration,” Edwards said. “We think in six months we could have example code and recipes to share,” he added.

With such tools, banks could modernize and accelerate the workflow for building scorecards, eliminating the current practice of manually tweaking and testing their parameters. For example, with GPU-accelerated hyperparameter tuning, a developer can let a computer test 100,000 model parameters while she is having her lunch.

With a much bigger pool to choose from, banks could select scorecards for their accuracy, simplicity, stability or a balance of all these factors. This helps banks ensure their lending decisions are clear and reliable and that good customers get the loans they need.

Digging into Deep Learning

Data scientists at Scotiabank use their DGX system to handle multiple experiments simultaneously. They tune scorecards, run XGBoost and refine deep-learning models. “That’s really improved our workflow,” said Edwards.

“In a way, the best thing we got from buying that system was all the support we got afterwards,” he added, noting new and upcoming RAPIDS features.

Longer term, the team is exploring use of deep learning to more quickly identify customer needs. An experimental model for calculating credit risk already showed a 20 percent performance improvement over the best scorecard, thanks to deep learning.

In addition, an emerging class of generative models can create synthetic datasets that mimic real bank data but contain no information specific to customers. That may open a door to collaborations that speed the pace of innovation.

The work of Edwards’ team reflects the growing interest and adoption of AI in banking.

“Last year, an annual survey of credit risk departments showed every participating bank was at least exploring machine learning and many were using it day-to-day,” Edwards said.

AI is reshaping markets in extraordinary ways. Soon, every company will be in AI, and will need both speed and scale to power increasingly complex machine learning models.

Accelerating innovation for enterprises around the world, Oracle today announced general availability of bare-metal Oracle Cloud Infrastructure instances featuring the NVIDIA A100 Tensor Core GPU.

NVIDIA founder and CEO Jensen Huang, speaking during the Oracle Live digital launch of the new instance, said: “Oracle is where companies store their enterprise data. We’re going to be able to take this data with no friction at all, run it on Oracle Cloud Infrastructure, conduct data analytics and create data frames that are used for machine learning to learn how to create a predictive model. That model will recommend actions to help companies go faster and make smarter decisions at an unparalleled scale.”

Watch Jensen Huang and Oracle Cloud Infrastructure Executive Vice President Clay Magouyrk discuss AI in the enterprise at Oracle Live.

Hundreds of thousands of enterprises across a broad range of industries store their data in Oracle databases. All of that raw data is ripe for AI analysis with A100 instances running on Oracle Cloud Infrastructure to help companies uncover new business opportunities, understand customer sentiment and create products.

The new Oracle Cloud Infrastructure bare-metal BM.GPU4.8 instance offers eight 40GB NVIDIA A100 GPUs linked via high-speed NVIDIA NVLink direct GPU-to-GPU interconnects. With A100, the world’s most powerful GPU, the Oracle Cloud Infrastructure instance delivers performance gains of up to 6x for customers running diverse AI workloads across training, inference and data science. To power the most demanding applications, the new instance can also scale up with NVIDIA Mellanox networking to provide more than 500 A100 GPUs in a single instance.

NVIDIA Software Accelerates AI and HPC for Oracle Enterprises

Accelerated computing starts with a powerful processor, but software, libraries and algorithms are all essential to an AI ecosystem. Whether it’s computer graphics, simulations like fluid dynamics, genomics processing, or deep learning and data analytics, every field requires its own domain-specific software stack. Oracle is providing NVIDIA’s extensive domain-specific software through the NVIDIA NGC hub of cloud-native, GPU-optimized containers, models and industry-specific software development kits.

“The costs of machine learning are not just on the hardware side,” said Clay Magouyrk, executive vice president of Oracle Cloud Infrastructure. “It’s also about how quickly someone can get spun up with the right tools, how quickly they can get access to the right software. Everything is pre-tuned on these instances so that anybody can show up, rent these GPUs by the hour and get quickly started running machine learning on Oracle Cloud.”

Oracle will also be adding A100 to the Oracle Cloud Infrastructure Data Science platform and providing NVIDIA Deep Neural Network libraries through Oracle Cloud Marketplace to help data scientists run common machine learning and deep learning frameworks, Jupyter Notebooks and Python/R integrated development environments in minutes.

On-Demand Access to the World’s Leading AI Performance

The new Oracle instances make it possible for every enterprise to have access to the world’s most powerful computing in the cloud. A100 delivers up to 20x more peak AI performance than its predecessors with TF32 operations and sparsity technology running on third-generation Tensor Cores. The world’s largest 7nm processor, A100 is incredibly elastic and cost-effective.

The flexible performance of A100 and Mellanox RDMA over Converged Ethernet networking makes the new Oracle Cloud Infrastructure instance ideal for critical drug discovery research, improving customer service through conversational AI, and enabling designers to model and build safer products, to highlight a few examples.

AI Acceleration for Workloads of All Sizes, Companies in All Stages

New businesses can access the power of A100 performance through the NVIDIA Inception and Oracle for Startups accelerator programs, which provide free Oracle Cloud credits for NVIDIA A100 and V100 GPU instances, special pricing, invaluable networking and expertise, marketing opportunities and more.

Oracle will soon introduce virtual machine instances providing one, two or four A100 GPUs per VM, and provide heterogeneous cluster networks of up to 512 A100 GPUs featuring bare-metal A100 GPU instances blended with Intel CPUs. Enterprises interested in accelerating their workloads with Oracle’s new A100 instance can get started with Oracle Cloud Infrastructure on Sept. 30.

To learn more about accelerating AI on Oracle Cloud Infrastructure, join Oracle at GTC, Oct. 5-9.

Humans are wielding AI to create art, and a virtual exhibit that’s part of NVIDIA’s GPU Technology Conference showcases the stunning results.

The AI Art Gallery at NVIDIA GTC features pieces by a broad collection of artists, developers and researchers from around the world who are using AI to push the limits of artistic expression.

When AI is introduced into the artistic process, the artist feeds the machine data and code, explains Heather Schoell, senior art director at NVIDIA, who curated the online exhibit.

Once the output reveals itself, it’s up to the artist to determine if it stands up to their artistic style and desired message, or if the input needs to be adjusted, according to Schoell.

“The output reflects both the artist’s hand and the medium, in this case data, used for creation,” Schoell says.

The exhibit complements what has become the world’s premier AI conference.

GTC, running Oct. 5-9, will bring together researchers from industry and academia, startups and Fortune 500 companies.

So it’s only natural that artists would be among those putting modern AI to work.

“Through this collection we aim to share how the artist can partner with AI as both an artistic medium and creative collaborator,” Schoell explains.

The artists featured in the AI Art Gallery include:

• Daniel Ambrosi – Dreamscapes fuses computational photography and AI to create a deeply textural environment.
• Refik Anadol – Machine Hallucinations, by the Turkish-born, Los Angeles-based conceptual artist known for his immersive architectural digital installations, such as a project at New York’s Chelsea Market that used projectors to splash AI-generated images of New York cityscapes to create what Anadol called a “machine hallucination.”
• Sofia Crespo and Dark Fractures – Work from the Argentina-born artist and Berlin-based studio led by Feileacan McCormick uses GANs and NLP models to generate 3D insects in a virtual, digital space.
• Scott Eaton – An artist, educator and creative technologist residing in London, who combines a deep understanding of human anatomy, traditional art techniques and modern digital tools in his uncanny, figurative artworks.
• Oxia Palus – The NVIDIA Inception startup will uncover a new masterpiece by Leonardo da Vinci that resurrects a hidden sketch and reconstructs the painting style from one of the most famous artists of all time.
• Anna Ridler – Three displays showing images of tulips that change based on Bitcoin’s price, created by the U.K. artist and researcher known for her work exploring the intersection of machine learning, nature and history.
• Helena Sarin – Using her own drawings, sketches and photographs as datasets, Sarin trains her models to generate new visuals that serve as the basis of her compositions — in this case with type of neural network known as a generative adversarial network, or GAN. The Moscow-born artist has embedded 12 of these creations in a book of puns on the acronym GAN.
• Pindar Van Arman – Driven by a collection of algorithms programmed to work with — and against — one another, the U.S.-based artist and roboticist’s creation uses a paintbrush, paint and canvas to create portraits that fuse the look and feel of a photo and a handmade sketch.

For a closer look, registered GTC attendees can go on a live, personal tour of two of our featured artists’ studios.

On Thursday, Oct. 8, you can virtually tour Van Arman’s Fort Worth, Texas, studio between 11 a.m.-12 p.m. Pacific time. And at 2 p.m. Pacific, you can tour Refik Anadol’s Los Angeles studio.

In addition, a pair of panel discussions, Thursday, Oct. 8, with AI Gallery artists will explore what led them to connect AI and fine art.

And starting Oct. 5, you can tune in to an on-demand GTC session featuring Oxia Palus co-founder George Cann, a Ph.D. candidate in space and climate physics at University College London.

Join us at the AI Art Gallery.

Register for GTC. 

One of the leading EV startups in China is charging up its compute capabilities.

Li Auto announced today it would develop its next generation of electric vehicles using the high-performance, energy-efficient NVIDIA DRIVE AGX Orin. These new vehicles will be developed in collaboration with tier 1 supplier Desay SV and feature advanced autonomous driving features, as well as extended battery range for truly intelligent mobility.

The startup has become a standout brand in China over the past year. Its electric model lineup has led domestic sales of medium and large SUVs for eight consecutive months. With this latest announcement, the automaker can extend its lead to the autonomous driving industry.

NVIDIA Orin, the SoC at the heart of the future fleet, achieves 200 TOPS — nearly 7x the performance and 3x the energy efficiency of our previous generation SoC — and is designed to handle the large number of applications and deep neural networks that run simultaneously for automated and autonomous driving. Orin is designed to achieve the systematic safety standards such as ISO 26262 ASIL-D.

This centralized, high-performance system will enable software-defined, intelligent features in Li Auto’s upcoming electric vehicles, making them a smart choice for eco-friendly, safe and convenient driving.

“By cooperating with NVIDIA, Li Auto can benefit from stronger performance and the energy-efficient compute power needed to deliver both advanced driving and fully autonomous driving solutions to market,” said Kai Wang, CTO of Li Auto.

A Software-Defined Architecture

Today, a vehicle’s software functions are powered by dozens of electronic control units, known as ECUs, that are distributed throughout the car. Each is specialized — one unit controls windows and one the door locks, for example, and others control power steering and braking.

This fixed-function architecture is not compatible with intelligent and autonomous features. These AI-powered capabilities are software-defined, meaning they are constantly improving, and require a hardware architecture that supports frequent upgrades.

Vehicles equipped with NVIDIA Orin have the powerful, centralized compute necessary for this software-defined architecture. The SoC was born out of the data center, built with approximately 17 billion transistors to handle the large number of applications and deep neural networks for autonomous systems and AI-powered cockpits.

This high-performance platform will enable Li Auto to become one of the first automakers in China to deploy an independent, advanced autonomous driving system with its next-generation fleet.

The Road Ahead

This announcement is just the first step of a long-term collaboration between NVIDIA and Li Auto.

“The next-generation NVIDIA Orin SoC offers a significant leap in compute performance and energy efficiency,” said Rishi Dhall, vice president of autonomous vehicles at NVIDIA. “NVIDIA works closely with companies like Li Auto to help bring new AI-based autonomous driving capabilities to cutting-edge EVs in China and around the globe.”

By combining NVIDIA’s leadership in AI software and computing with Li Auto’s momentum in the electric vehicle space, together, these companies will develop vehicles that are better for the environment and safer for everyone.

STEM is dope. That’s the simple message that Justin “Mr. Fascinate” Shaifer evangelizes to young people around the world.

Through social media and other platforms, Shaifer fascinates children with STEM projects — including those that can be created using AI with NVIDIA Jetson products — in hopes that more students from underrepresented groups will be inspired to dive into the field. NVIDIA Jetson embedded systems allow anyone to create their own AI-based projects.

Growing up on Chicago’s South Side, Shaifer didn’t know anyone with a career in STEM he could look up to — at least no one he could relate to. Now, he’s become that role model for thousands of kids, working to prove that STEM is cool and attainable for anyone who has a passion for it.

About the Maker

Shaifer is a STEM advocate, animator and TV host who educates students about the importance of STEM and diversity within it. He has a YouTube channel, gives keynote speeches and hosts the Escape Lab live science show on Twitch.

He’s also the founder of Fascinate Inc., a nonprofit with the mission of exciting underrepresented students about careers in STEM and providing schools and after-school programs with fun science curricula.

The organization also launched the Magic Cool Bus project, filling a real-life bus with cutting-edge tech gadgets and bringing it to schools so students can hop on board and explore.

Growing up in a single-parent home, Shaifer was fascinated by science, earning scholarships from NASA and NOAA that covered his expenses to study marine and environmental science at Hampton University. He’s currently working toward a Ph.D. in science education at Columbia University.

His Inspiration

Shaifer was inspired to transition from being a scientist in a lab to a science educator for others in 2017, while volunteering at a museum in Washington.

“I was freestyle rapping about a carbon cycle exhibit, and this nine-year-old Black kid came up to me and said, ‘What do you do, man?’” said Shaifer.

When Shaifer told him he was a scientist, the child said, “That’s so cool. When I grow up, I want to be a scientist just like you!”

“That made me reflect on the fact that at nine years old, I’d never seen an example of a scientist that looked like me,” said Shaifer. “I realized that students need to be exposed to a role model in STEM that they can identify with, at scale.”

Later that year, Shaifer founded Fascinate Inc.

His Favorite Jetson Projects

Shaifer is passionate about exposing students to the world of AI, and he says using NVIDIA Jetson platform is a great way to do so.

Watch him highlight Jetson products:

NVIDIA Jetson Xavier NX Unboxing and Impression

NVIDIA SparkFun JetBot AI Kit Unboxing and Impression

One of Shaifer’s favorite real-world applications that uses the NVIDIA Jetson Nano developer kit is Qrio. The bot, created by Agustinus Nalwan, recognizes a toddler’s toy and plays a relevant YouTube video.

“Especially since I work with young kids, I think that’s a really cool application that allows a child to be engaged, interactive and always learning as they play with their toys,” said Shaifer.

Where to Learn More 

Get fascinated by STEM on Shaifer’s website and YouTube channel.

Discover tools, inspiration and three easy steps to help kickstart your project with AI on our “Get AI, Learn AI, Build AI” page.

Healthcare is under the microscope this year like never before. Hospitals are being asked to do more with less, and researchers are working around the clock to answer pressing questions.

NVIDIA’s GPU Technology Conference brings everything you need to know about the future of AI and HPC in healthcare together in one place.

Innovators across healthcare will come together at the event to share how they are using AI and GPUs to supercharge their medical devices and biomedical research.

Scores of on-demand talks and hands-on training sessions will focus on AI in medical imaging, genomics, drug discovery, medical instruments and smart hospitals.

And advancements powered by GPU acceleration in fields such as imaging, genomics and drug discovery, which are playing a vital role in COVID-19 research, will take center stage at the conference.

There are over 120 healthcare sessions taking place at GTC, which will feature amazing demos, hands-on training, breakthrough research and more from October 5-9.

Turning Months into Minutes for Drug Discovery

AI and HPC are improving speed, accuracy and scalability for drug discovery. Companies and researchers are turning to AI to enhance current methods in the field. Molecular simulation like docking, free energy pertubation (FEP) and molecular dynamics requires a huge amount of computing power. At every phase of drug discovery, researchers are incorporating AI methods to accelerate the process.

Here are some drug discovery sessions you won’t want to miss:

• Powering Computational Drug Design with NVIDIA GPUs
• The Summit Supercomputer as a Virtual Drug Discovery Lab for COVID-19
• Revealing Hidden Determinants of Molecular Recognition in Hit-to-lead Optimization with Graph Convolutional Networks

Architecting the Next Generation of Hospitals

AI can greatly improve hospital efficiency and prevent costs from ballooning. Autonomous robots can help with surgeries, deliver blankets to patients’ rooms and perform automatic check-ins. AI systems can search patient records, monitor blood pressure and oxygen saturation levels, flag thoracic radiology images that show pneumonia, take patient temperatures and notify staff immediately of changes.

Here are some sessions on smart hospitals you won’t want to miss:

• Improve Patient Care with Everyday Sensors and Multimodal AI 
• Architecting AI-Powered Medical Instruments
• Combining Design and GPUs to Improve Surgical Performance

Training AI for Medical Imaging

AI models are being developed at a rapid pace to optimize medical imaging analysis for both radiology and pathology. Get exposure to cutting-edge use cases for AI in medical imaging and how developers can use the NVIDIA Clara Imaging application framework to deploy their own AI applications.

Building robust AI requires massive amounts of data. In the past, hospitals and medical institutions have struggled to share and combine their local knowledge without compromising patient privacy, but federated learning is making this possible. The learning paradigm enables different clients to securely collaborate, train and contribute to a global model. Register for this session to learn more about federated learning and its use on AI COVID-19 model development from a panel of experts.

Must-see medical imaging sessions include:

• Bringing AI to Hospitals: How to Design a Clinical Imaging Infrastructure for AI at the Edge
• The National Swedish Platform for AI Research in Medical Imaging
• XNAT and NVIDIA Clara In Practice: Building a Medical Imaging Research Ecosystem

Accelerating Genomic Analysis

Genomic data is foundational in making precision medicine a reality. As next-generation sequencing becomes more routine, large genomic datasets are becoming more prevalent. Transforming the sequencing data into genetic information is just the first step in a complicated, data-intensive workflow. With high performance computing, genomic analysis is being streamlined and accelerated to enable novel discoveries about the human genome.

Genomic sessions you won’t want to miss include:

• Accelerating Whole Genome Analysis to Make the Most of Your Sequencing Data
• Real-Time Sequencing Meets High Performance Compute

The Best of MICCAI at GTC

This year’s GTC is also bringing to attendees the best of MICCAI, a conference focused on cutting-edge deep learning medical imaging research. Developers will have the opportunity to dive into the papers presented, connect with the researchers at a variety of networking opportunities, and watch on-demand trainings from the first ever MONAI Bootcamp hosted at MICCAI.

Game-Changing Healthcare Startups

Over 70 healthcare AI startups from the NVIDIA Inception program will showcase their latest breakthroughs at GTC. Get inspired by the AI- and HPC-powered technologies these startups are developing for personalized medicine and next-generation clinics.

Here are some Inception member-led talks not to miss:

• Distributed Learning on Sensitive Data
• Cutting-Edge AI Tools Aid in Safe Patient Care in a Smart Hospital
• A Universal, Fast, Auto-Focusing System for Whole Slide Imaging Using Deep Learning

Make New Connections, Share Ideas

GTC will have new ways to connect with fellow attendees who are blazing the trail for healthcare and biomedical innovation. Join a Dinner with Strangers conversation to network with peers on topics spanning drug discovery, medical imaging, genomics and intelligent instrument development. Or, book a Braindate to have a knowledge-sharing conversation on a topic of your choice with a small group or one-on-one.

Learn more about networking opportunities at GTC.

Brilliant Minds Never Turn Off

GTC will showcase the hard work and groundbreaking discoveries of developers, researchers, engineers, business leaders and technologists from around the world. Nowhere else can you access five days of continuous programming with regionally tailored content. This international event will unveil the future of healthcare technology, all in one place.

Check out the full healthcare session lineup at GTC, including talks from over 80 startups using AI to transform healthcare, and register for the event today.

Reviewers have just finished testing NVIDIA’s new flagship GPU — the NVIDIA RTX 3080 — and the raves are rolling in.

NVIDIA CEO Jensen Huang promised “a giant step into the future,” when he revealed NVIDIA’s GeForce RTX 30 Series GPUs on Sept. 1.

The NVIDIA Ampere GPU architecture, introduced in May, has already stormed through supercomputing and hyperscale data centers.

But no one knew for sure what the new architecture would be capable of when unleashed on gaming.

Now they do:

• “Nvidia brought it this generation,” PCWorld’s Brad Chacos wrote. “Ampere is indeed Nvidia’s greatest generational leap in recent memory.”
• “The RTX 3080 is just stupidly, ridiculously, insanely fast. Period.” – declared Eber Anthony, at Hardware Canucks.
• “The bottom line is that the RTX 3080 is the new high-end gaming champion, delivering truly next-gen performance without a massive increase in price.” – wrote Jarred Walton, for Tom’s Hardware.
• We can now confirm that this really is the biggest generational leap in PC graphics we’ve seen in years – perhaps ever,” – noted Bill Thomas, at Tech Radar.

The GeForce RTX 30 Series, NVIDIA’s second-generation RTX GPUs, deliver up to 2x the performance and up to 1.9x the power efficiency over previous-generation GPUs.

This leap in performance will deliver incredible performance in upcoming games such as Cyberpunk 2077, Call of Duty: Black Ops Cold War and Watch Dogs: Legion, currently bundled with select GeForce RTX 3080 graphics cards at participating retailers.

In addition to the trio of new GPUs — the flagship GeForce RTX 3080, the GeForce RTX 3070 and the “ferocious” GeForce RTX 3090 — gamers get a slate of new tools.

They include NVIDIA Reflex — which makes competitive gamers quicker; NVIDIA Omniverse Machinima — for those using real-time computer graphics engines to create movies; and NVIDIA Broadcast — which harnesses AI to build virtual broadcast studios for streamers.

And new 2nd Gen Ray Tracing Cores and 3rd Gen Tensor Cores make ray-traced and DLSS-accelerated experiences even faster.

GeForce RTX 3080 will be out from NVIDIA and our partners Sept. 17.

It could be just a fender bender or an unforeseen rain shower, but a few seconds of disruption can translate to extra minutes or even hours of mind-numbing highway traffic.

But how much of this congestion could be avoided with AI at the wheel?

That’s what the Contra Costa Transportation Authority is working to determine in one of three federally funded automated driving system pilots in the next few years. Using vehicles retrofitted with the NVIDIA DRIVE AGX Pegasus platform, the agency will estimate just how much intelligent transportation can improve the efficiency of everyday commutes.

“As the population grows, there are more demands on roadways and continuing to widen them is just not sustainable,” said Randy Iwasaki, executive director of the CCTA. “We need to find better ways to move people, and autonomous vehicle technology is one way to do that.”

The CCTA was one of eight awardees – and the only local agency – of the Automated Driving System Demonstration Grants Program from the U.S. Department of Transportation, which aims to test the safe integration of self-driving cars into U.S. roads.

The Bay Area agency is using the funds for the highway pilot, as well as two other projects to develop robotaxis equipped with self-docking wheelchair technology and test autonomous shuttles for a local retirement community.

A More Intelligent Interstate

From the 101 to the 405, California is known for its constantly congested highways. In Contra Costa, Interstate 680 is one of those high-traffic corridors, funneling many of the area’s 120,000 daily commuters. This pilot will explore how the Highway Capacity Manual – which sets assumptions for modeling freeway capacity – can be updated to incorporate future automated vehicle technology.

Iwasaki estimates that half of California’s congestion is recurrent, meaning demand for roadways is higher than supply.  The other half is non-recurrent and can be attributed to things like weather events, special events — such as concerts or parades — and accidents. By eliminating human driver error, which has been estimated by the National Highway Traffic Safety Administration to be the cause of 94 percent of traffic accidents, the system becomes more efficient and reliable.

Autonomous vehicles don’t get distracted or drowsy, which are two of the biggest causes of human error while driving. They also use redundant and diverse sensors as well as high-definition maps to detect and plan the road ahead much farther than a human driver can.

These attributes make it easier to maintain constant speeds as well as space for vehicles to merge in and out of traffic for a smoother daily commute.

Driving Confidence

The CCTA will be using a fleet of autonomous test vehicles retrofitted with sensors and NVIDIA DRIVE AGX to gauge how much this technology can improve highway capacity.

The NVIDIA DRIVE AGX Pegasus AI compute platform uses the power of two Xavier systems-on-a-chip and two NVIDIA Turing architecture GPUs to achieve an unprecedented 320 trillion operations per second of supercomputing performance. The platform is designed and built for Level 4 and Level 5 autonomous systems, including robotaxis.

Iwasaki said the agency tapped NVIDIA for this pilot because the company’s vision matches its own: to solve real problems that haven’t been solved before, using proactive safety measures every step of the way.

With half of adult drivers reporting they’re fearful of self-driving technology, this approach to autonomous vehicles is critical to gaining public acceptance, he said.

“We need to get the word out that this technology is safer and let them know who’s behind making sure it’s safer,” Iwasaki said.

Christian Sanz isn’t above trying disguises to sneak into places. He once put on a hard hat, vest and steel-toed boots to get onto the construction site of the San Francisco 49ers football stadium to explore applications for his drone startup.

That bold move scored his first deal.

For the entrepreneur who popularized drones in hackathons in 2012 as founder of the Drone Games matches, starting Skycatch in 2013 was a logical next step.

“We decided to look for more industrial uses, so I went and bought construction gear and was able to blend in, and in many cases people didn’t know I wasn’t working for them as I was collecting data,” Sanz said.

Skycatch has since grown up: In recent years the San Francisco-based company has been providing some of the world’s largest mining and construction companies its AI-enabled automated drone surveying and analytics platform. The startup, which has landed $47 million in funding, promises customers automated visibility over operations.

At the heart of the platform is the NVIDIA Jetson TX2-driven Edge1 edge computer and base station. It can create 2D maps and 3D point clouds in real-time, as well as pinpoint features  to within five-centimeter accuracy. Also, it runs AI models to do split-second inference in the field to detect objects.

Today, Skycatch announced its new Discover1 device. The Discover1 connects to industrial machines, enabling customers to plug in a multitude of sensors that can expand the data gathering of Skycatch.

The Discover1 sports a Jetson Nano inside to facilitate the collection of data from sensors and enable computer vision and machine learning on the edge. The device has LTE and WiFi connectivity to stream data to the cloud.

Changing-Tracking AI

Skycatch can capture 3D images of job sites for merging against blueprints to monitor changes.

Such monitoring for one large construction site showed that electrical conduit pipes were installed in the wrong spot. Concrete would be poured next, cementing them in place. Catching the mistake early helped avoid a much costlier revision later.

Skycatch says that customers using its services can expect to compress the timelines on their projects as well as reduce costs by catching errors before they become bigger problems.

Surveying with Speed

Japan’s Komatsu, one of the world’s leading makers of bulldozers, excavators and other industrial machines, is an early customer of Skycatch.

With Japan facing a labor shortage, the equipment maker was looking for ways to help automate its products. One bottleneck was surveying a location, which could take days, before unleashing the machines.

Skycatch automated the process with its drone platform. The result for Komatsu is that less-skilled workers can generate a 3D map of a job site within 30 minutes, enabling operators to get started sooner with the land-moving beasts.

Jetson for AI

As Skycatch was generating massive sums of data, the company’s founder realized they needed more computing capability to handle it. Also, given the environment in which they were operating, the computing had to be done on the edge while consuming minimal power.

They turned to the Jetson TX2, which provides server-level AI performance using the CUDA-enabled NVIDIA Pascal GPU in a small form factor and taps as little as 7.5 watts of power. It’s high memory bandwidth and wide range of hardware interfaces in a rugged form factor are ideal for the industrial environments Skycatch operates in.

Sanz says that “indexing the physical world” is demanding because of all the unstructured data of photos and videos, which require feature extraction to “make sense of it all.”

“When the Jetson TX2 came out, we were super excited. Since 2017, we’ve rewritten our photogrammetry engine to use the CUDA language framework so that we can achieve much faster speed and processing,” Sanz said.

Remote Bulldozers

The Discover1 can collect data right from the shovel of a bulldozer. Inertial measurement unit, or IMU, sensors can be attached to the Discover1 on construction machines to track movements from the bulldozer’s point of view.

One of the largest mining companies in the world uses the Discover1 in pilot tests to help remotely steer its massive mining machines in situations too dangerous for operators.

“Now you can actually enable 3D viewing of the machine to someone who is driving it remotely, which is much more affordable,” Sanz said.

Skycatch is a member of NVIDIA Inception, a virtual accelerator program that helps startups in AI and data science get to market faster.