AI in the Sky: NVIDIA GPUs Help Researchers Remove Clouds From Satellite Images

AI in the Sky: NVIDIA GPUs Help Researchers Remove Clouds From Satellite Images

Satellite images can be a fantastic civil engineering tool — at least when clouds don’t get in the way.

Now researchers at Osaka University have shown how to use GPU-accelerated deep learning to remove these clouds.

The scientists from the university’s Division of Sustainable Energy and Environmental Engineering used a “generative adversarial network” or GAN.

“By training the generative network to ‘fool’ the discriminative network into thinking an image is real, we obtain reconstructed images that are more self-consistent,” first author Kazunosuke Ikeno said in a statement.

Created in 2014 by Ian Goodfellow, then a Ph.D. student at the University of Montreal, GANs rely on a pair of competing networks to create realistic images. These competing networks allow developers to train AIs with less data.

Images of clouds can be removed by hand, but that’s time-consuming. Machine learning techniques, by contrast, require large numbers of training images to work, and that’s not always practical.

So the researchers at the University of Osaka turned to GANs, which rely on two algorithms.

The first, known as a “generative network,” reconstructs images without clouds.

The second, a “discriminative network,” uses a convolutional neural network to pick which images are created by the first network and actual photos.

The two competing networks make each other better without the need for as much data. The result: highly realistic images with no clouds.

Using the resulting data as textures for 3D models allows more accurate datasets of building image masks to be automatically generated.

Using 400 by 400-pixel images, the researchers trained the models on a PC running the Ubuntu open-source operating system and a GeForce GTX 1060 GPU.

“This method makes it possible to detect buildings in areas without labeled training data,” senior author Tomohiro Fukuda said in a statement.

In the future, researchers could use the technique to detect other objects, such as roads and rivers in aerial photographs.

Sounds like a sunny forecast.

Read the full paper: https://www.sciencedirect.com/science/article/pii/S1474034621001336?via%3Dihub

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Doing the Math: Michigan Team Cracks the Code for Subatomic Insights

Doing the Math: Michigan Team Cracks the Code for Subatomic Insights

In record time, Vikram Gavini’s lab crossed a big milestone in viewing tiny things.

The three-person team at the University of Michigan crafted a program that uses complex math to peer deep into the world of the atom. It could advance many fields of science, as well as the design for everything from lighter cars to more effective drugs.

The code, available in the group’s open source repository, got a 20x speedup in just 18 months thanks to GPUs.

A Journey to the Summit

In mid-2018 the team was getting ready to release a version of the code running on CPUs when it got an invite to a GPU hackathon at Oak Ridge National Lab, the home of Summit, one of the world’s fastest supercomputers.

“We thought, let’s go see what we can achieve,” said Gavini, a professor of mechanical engineering and materials science.

“We quickly realized our code could exploit the massive parallelism in GPUs,” said Sambit Das, a post-doc from the lab who attended the five-day event.

Before it was over, Das and another lab member, Phani Motamarri, got 5x speedups moving the code to CUDA and its libraries. They also heard the promise of much more to come.

From 5x to 20x Speedups in Six Months

Over the next few months, the lab continued to tune its program for analyzing 100,000 electrons in 10,000 magnesium atoms. By early 2019, it was ready to run on Summit.

Taking an iterative approach, the lab ran increasing portions of its code on more and more of Summit’s nodes. By April, it was using most of the system’s 27,000 GPUs, getting nearly 46 petaflops of performance, 20x prior work.

It was an unheard-of result for a program based on density functional theory (DFT), the complex math that accounts for quantum interactions among subatomic particles.

Distributed Computing for Difficult Calculations

DFT calculations are so complex and fundamental that they currently consume a quarter of the time on all public research computers. They are the subject of 12 of the 100 most-cited scientific papers, used to analyze everything from astrophysics to DNA strands.

Initially, the lab reported its program used nearly 30 percent of Summit’s peak theoretical capability, an unusually high efficiency rate. By comparison, most other DFT codes don’t even report efficiency because they have difficulty scaling beyond use of a few processors.

“It was really exciting to get to that point because it was unprecedented,” said Gavini.

Recognition for a Math Milestone

In late 2019, the group was named a finalist for a Gordon Bell award. It was the lab’s first submission for the award that’s the equivalent of a Nobel in high performance computing.

“That provided a lot of visibility for our lab and our university, and I think this effort is just the beginning,” Gavini said.

Indeed, since the competition, the lab pushed the code’s performance to 64 petaflops and 38 percent efficiency on Summit. And it’s already exploring its use on other systems and applications.

Seeking More Apps, Performance

The initial work analyzed magnesium, a metal much lighter than the steel and aluminum used in cars and planes today, promising significant fuel savings. Last year, the lab teamed up with another group exploring how electrons move in DNA, work that could help other researchers develop more effective drugs.

The next big step is running the code on Perlmutter, a supercomputer using the latest NVIDIA A100 Tensor Core GPUs. Das reports he’s already getting 4x speedups compared to the Summit GPUs thanks to the A100 GPUs’ support for TensorFloat-32, a mixed-precision format that delivers both fast results and high accuracy.

The lab’s program already offers 100x speedups compared to other DFT codes, but Gavini’s not stopping there. He’s already thinking about testing it on Fugaku, an Arm-based system that’s currently the world’s fastest supercomputer.

“It’s always exciting to see how far you can get, and there’s always a next milestone. We see this as the beginning of a journey,” he said.

The post Doing the Math: Michigan Team Cracks the Code for Subatomic Insights appeared first on The Official NVIDIA Blog.

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Teamwork Makes the Dream Work: GFN Thursday Celebrates Team17 Titles Streaming From the Cloud

Teamwork Makes the Dream Work: GFN Thursday Celebrates Team17 Titles Streaming From the Cloud

GFN Thursday is all about bringing games powered by GeForce NOW’s GPUs in the cloud to gamers. Today, that spotlight shines on Team17, the prolific publisher behind many games in the GeForce NOW library.

The party gets started with their newest release, a day-and-date launch of Sheltered 2, streaming on the cloud alongside the 12 additions to the GeForce NOW library this week. Team17 joins the free game fun with The Escapists, free to claim on the Epic Games Store from Sept. 23-30.

We also look at a number of their greatest hits that members can stream today.

A Bit of Everything, All of the Time

Gear up for an awesome gaming session playing some of the greatest hits from publishers like Team17 on GeForce NOW.

With 30 years of experience making video games, Team17 has seen it all in PC gaming. From releasing hits like the Worms franchise and the Overcooked series to helping new indie developers bring their unique creations to players around the world, the publisher has shown that fearless experimentation can lead to awesome outcomes and a whole lot of great gaming.

Bringing Team17’s diverse collection to GeForce NOW makes for an easy win for gamers everywhere.

“GeForce NOW lets us share our passion for games as well as our independent spirit with more players than ever,” said Harley Homewood, head of sales at Team17. “Players can experience our diverse collection of games, streaming on GeForce NOW, even across their low-powered devices with the power of a full gaming rig.”

With today’s launch of Sheltered 2 (Steam) — which joins recent releases like Honey, I Joined a Cult (Steam) and Hell Let Loose (Steam) — streaming alongside other Team17 games, there’s something for everyone, ready to play across all GeForce NOW compatible devices.

Here are some great suggestions from the Team17 catalog to help kick off the weekend fun.

Gimme Shelter, and So Much More

Take a trip to a post-apocalyptic world in the survival sim, Sheltered 2 (Steam) which launched on GeForce NOW this week. Become a leader and build up your own faction of survivors. Tackle dangers like starvation, dehydration and radiation poisoning by managing your resources as well as threats from your fellow humans when exploring and withstanding the wasteland.

Want to lead, but in a more light-hearted way? Check out Honey, I Joined a Cult (Steam) and try your hand at building your own following. For family fun, battle it out in Worms Rumble to see who’s the last worm standing. And the kitchen gets heated with a little chaotic competition in Overcooked (Steam) and the hit sequel Overcooked! 2 (Steam).

Join battles of 100 players with infantry, tanks and artillery in all out war playing Hell Let Loose.

Take combat to a whole new scale by fighting in some of the most iconic World War II battles in the first-person shooter Hell Let Loose (Steam). Go in guns blazing in the frantically fast Neon Abyss (Steam). Or hack ’n’ slash your way through a nightmare world in the brutal Blasphemous (Steam).

If you can dream it, you can build it in the crafty building game Main Assembly (Steam). Make a great escape playing The Escapists 2 (Steam) and The Escapists — available on Steam and free to claim on the Epic Games Store from Sept. 23-30. Fight physics and take on the exciting career of a fast-paced furniture mover in Moving Out (Steam). Or grab a group of friends for some fun in Golf With Your Friends (Steam).

Find these fantastic games and more by searching for “Team17” in the GeForce NOW app.

Game On

Uncover the secrets of a forgotten community hidden in a forest full of wandering spirits in Kena: Bridge of Spirits.

This GFN Thursday packs a punch with a bunch of new games ready to stream, with five day-and-date release titles, including Kena: Bridge of Spirits, Sable, World War Z: Aftermath and the aforementioned Sheltered 2. Here’s the full list of this week’s additions: 

  • Beyond Contact (day-and-date release on Steam, September 21)
  • Kena: Bridge of Spirits (day-and-date release on Epic Games Store, September 21)
  • Sheltered 2 (day-and-date release on Steam, September 21)
  • World War Z: Aftermath (day-and-date release on Steam, September 21)
  • Sable (day-and-date release on Steam and Epic Games Store, September 23)
  • The Escapists (free on Epic Games Store, September 23)
  • Darwin Project (Steam)
  • EVE Online (Epic Games Store)
  • Gas Station Simulator (Steam)
  • Miscreated (Steam)
  • Professional Fishing (Steam)
  • Zanki Zero: Last Beginning (Steam)

Finally, let’s get the weekend started right with a special GFN Thursday question:

what in-game item would be most helpful in surviving an apocalypse? 🤔

🌩 NVIDIA GeForce NOW (@NVIDIAGFN) September 22, 2021

Let us know on Twitter or in the comments below.

The post Teamwork Makes the Dream Work: GFN Thursday Celebrates Team17 Titles Streaming From the Cloud appeared first on The Official NVIDIA Blog.

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NVIDIA Extends AI Inference Performance Leadership, with Debut Results on Arm-Based Servers

NVIDIA Extends AI Inference Performance Leadership, with Debut Results on Arm-Based Servers

NVIDIA delivers the best results in AI inference using either x86 or Arm-based CPUs, according to benchmarks released today.

It’s the third consecutive time NVIDIA has set records in performance and energy efficiency on inference tests from MLCommons, an industry benchmarking group formed in May 2018.

And it’s the first time the data-center category tests have run on an Arm-based system, giving users more choice in how they deploy AI, the most transformative technology of our time.

Tale of the Tape

NVIDIA AI platform-powered computers topped all seven performance tests of inference in the latest round with systems from NVIDIA and nine of our ecosystem partners including Alibaba, Dell Technologies, Fujitsu, GIGABYTE, Hewlett Packard Enterprise, Inspur, Lenovo, Nettrix and Supermicro.

And NVIDIA is the only company to report results on all MLPerf tests in this and every round to date.

MLPerf AI inference results, Sept. 2021

Inference is what happens when a computer runs AI software to recognize an object or make a prediction. It’s a process that uses a deep learning model to filter data, finding results no human could capture.

MLPerf’s inference benchmarks are based on today’s most popular AI workloads and scenarios, covering computer vision, medical imaging, natural language processing, recommendation systems, reinforcement learning and more.

So, whatever AI applications they deploy, users can set their own records with NVIDIA.

Why Performance Matters

AI models and datasets continue to grow as AI use cases expand from the data center to the edge and beyond. That’s why users need performance that’s both dependable and flexible to deploy.

MLPerf gives users the confidence to make informed buying decisions. It’s backed by dozens of industry leaders, including Alibaba, Arm, Baidu, Google, Intel and NVIDIA, so the tests are transparent and objective.

Flexing Arm for Enterprise AI

The Arm architecture is making headway into data centers around the world, in part thanks to its energy efficiency, performance increases and expanding software ecosystem.

The latest benchmarks show that as a GPU-accelerated platform, Arm-based servers using Ampere Altra CPUs deliver near-equal performance to similarly configured x86-based servers for AI inference jobs. In fact, in one of the tests, the Arm-based server out-performed a similar x86 system.

NVIDIA has a long tradition of supporting every CPU architecture, so we’re proud to see Arm prove its AI prowess in a peer-reviewed industry benchmark.

“Arm, as a founding member of MLCommons, is committed to the process of creating standards and benchmarks to better address challenges and inspire innovation in the accelerated computing industry,” said David Lecomber, a senior director of HPC and tools at Arm.

“The latest inference results demonstrate the readiness of Arm-based systems powered by Arm-based CPUs and NVIDIA GPUs for tackling a broad array of AI workloads in the data center,” he added.MLPerf AI inference results for Arm

Partners Show Their AI Powers

NVIDIA’s AI technology is backed by a large and growing ecosystem.

Seven OEMs submitted a total of 22 GPU-accelerated platforms in the latest benchmarks.

Most of these server models are NVIDIA-Certified, validated for running a diverse range of accelerated workloads. And many of them support NVIDIA AI Enterprise, software officially released last month.

Our partners participating in this round included Dell Technologies, Fujitsu, Hewlett Packard Enterprise, Inspur, Lenovo, Nettrix and Supermicro as well as cloud-service provider Alibaba.

The Power of Software

A key ingredient of NVIDIA’s AI success across all use cases is our full software stack.

For inference, that includes pre-trained AI models for a wide variety of use cases. The NVIDIA TAO Toolkit customizes those models for specific applications using transfer learning.

Our NVIDIA TensorRT software optimizes AI models so they make best use of memory and run faster. We routinely use it for MLPerf tests, and it’s available for both x86 and Arm-based systems.

We also employed our NVIDIA Triton Inference Server software and Multi-Instance GPU (MIG) capability in these benchmarks. They deliver for all developers the kind of performance that usually requires expert coders.

Thanks to continuous improvements in this software stack, NVIDIA achieved gains up to 20 percent in performance and 15 percent in energy efficiency from previous MLPerf inference benchmarks just four months ago.

All the software we used in the latest tests is available from the MLPerf repository, so anyone can reproduce our benchmark results. We continually add this code into our deep learning frameworks and containers available on NGC, our software hub for GPU applications.

It’s part of a full-stack AI offering, supporting every major processor architecture, proven in the latest industry benchmarks and available to tackle real AI jobs today.

To learn more about the NVIDIA inference platform, check out our NVIDIA Inference Technology Overview.

The post NVIDIA Extends AI Inference Performance Leadership, with Debut Results on Arm-Based Servers appeared first on The Official NVIDIA Blog.

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NVIDIA Invites Healthcare Startup Submissions to Access UK’s Most Powerful Supercomputer

NVIDIA Invites Healthcare Startup Submissions to Access UK’s Most Powerful Supercomputer

It takes major computing power to tackle major projects in digital biology — and that’s why we’re connecting pioneering healthcare startups with the U.K.’s most powerful supercomputer, Cambridge-1.

U.K. startups can now apply to harness the system, which is dedicated to advancing healthcare with AI and digital biology.

Since inaugurating Cambridge-1 in July, five founding partners have been tapping the supercomputer for projects in drug discovery, medical imaging and genomics. Peptone, a U.K. company collaborating with NVIDIA on AI-driven protein engineering, will also use Cambridge-1 for its work.

“The astounding parallelism embedded in NVIDIA processors allows us to run very dense, very complex simulations,” said Peptone founder and CEO Kamil Tamiola. “We envision that computers will transform engineering and design in the protein space.”

The first NVIDIA supercomputer designed and built for external research access, Cambridge-1 is an NVIDIA DGX SuperPOD powered by 80 NVIDIA DGX A100 systems, BlueField-2 DPUs and NVIDIA HDR InfiniBand networking. Running on 100 percent renewable energy, it’s among the top 50 supercomputers in the world.

Giving startups access to the powerful system will help the companies bring their healthcare innovations to market faster, accelerating the evolution of drug discovery, genome sequencing and disease research.

Peptone Pushes the Frontier of Protein Engineering

Peptone, a member of the NVIDIA Inception program for AI and data science startups, aims to change the way protein drugs are engineered using unsupervised learning and reinforcement learning techniques. It plans to use Cambridge-1 to design antibodies that could help treat several inflammatory disorders.

Developed in collaboration with NVIDIA, Peptone’s protein drug discovery engine enables scientists to search for protein variants that would be cost effective to manufacture and offer promising therapeutic properties. It’s already in use for pre-clinical research.

The startup combines generative AI models, computational molecular physics and lab experiments to model unstructured proteins. Also called disordered proteins, these molecules lack a stable shape, making it extremely difficult to engineer drug compounds to bind with them.

Up to half the proteins in the human body are of this shape-shifting variety, including proteins involved in cancer, inflammatory diseases and neurodegenerative diseases like Parkinson’s and Alzheimer’s.

These proteins are challenging to study experimentally, so Peptone uses AI and complex simulations to study the link between molecular mutations and protein behavior. The company’s researchers require powerful computational resources to help them iterate faster, mutate more of the simulated proteins and build a bigger library of promising drug molecules to test.

“We don’t run one massive simulation, we run millions of short simulations. With many of these calculations running concurrently, we need to think about calculating events asynchronously to get the best performance from our system,” said Tamiola. “For us, the A100 GPU was a breakthrough — because with the NVIDIA Ampere GPU architecture, it was so much easier to get this right.”

Tamiola said the latest NVIDIA CUDA libraries helped Peptone reduce latency and “unlock that computational power” by compiling the team’s standard C++ code to CUDA, allowing them to quickly port their algorithms to run on NVIDIA GPUs.

UK Startups: Apply for Cambridge-1 Access

Applications from U.K. healthcare startups for access to Cambridge-1 will be accepted here until December 30.

Cambridge-1 has 5 founding members collaborating with NVIDIA

The selected companies, to be announced in early 2022, will also be invited to meet with the founding partners of the supercomputer — AstraZeneca, GSK, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore — and will gain access to NVIDIA Inception benefits including critical go-to-market support, training and technology.

Learn more about the capabilities of Cambridge-1, and about the NVIDIA Inception program, which includes more than 500 of the U.K.’s most dynamic AI startups.

Subscribe to NVIDIA healthcare news and follow NVIDIA Healthcare on Twitter.

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Wild Things: 3D Reconstructions of Endangered Species with NVIDIA’s Sifei Liu

Wild Things: 3D Reconstructions of Endangered Species with NVIDIA’s Sifei Liu

Endangered species can be difficult to study — they’re elusive, and the very act of observing them can disrupt their lives. Now, scientists can take a closer look at endangered species by studying AI-generated 3D representations of them.

Sifei Liu, a senior research scientist at NVIDIA, has worked with her team to create an algorithm that can reconstruct 3D meshes — graphics models used to display the edges, vertices and overall shape of an object — from 2D inputs like images and videos.

Liu spoke with NVIDIA AI Podcast host Noah Kravitz about her team’s project, called Online Adaptation for Consistent Mesh Reconstruction in the Wild. Liu and her team have presented the project at various prominent conferences, including NeurIPS 2020.

Key Points From This Episode:

  • The deep learning algorithm made by Liu and her team creates 3D meshes with just 2D annotations. This means the AI mimics natural human perception, which can predict and visualize the 3D shape of an object based on a 2D image of it.
  • Initially focused on birds and zebras, Liu hopes to expand the project for use by scientists who study any endangered species, since it can be difficult to make 3D models of animals that are hard to come across. The meshes could one day be 3D-printable.

Tweetables:

“There are a lot of opportunities to leverage deep learning and graphics, and 3D reconstruction is one of them.” — Sifei Liu [19:29]

With deep learning, “you can try to make our world more beautiful.” — Sifei Liu [20:56]

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The post Wild Things: 3D Reconstructions of Endangered Species with NVIDIA’s Sifei Liu appeared first on The Official NVIDIA Blog.

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Next Generation: ‘Teens in AI’ Takes on the Ada Lovelace Hackathon

Next Generation: ‘Teens in AI’ Takes on the Ada Lovelace Hackathon

Jobs in data science and AI are among the fastest growing in the entire workforce, according to LinkedIn’s 2021 Jobs Report.

Teens in AI, a London-based initiative, is working to inspire the next generation of AI researchers, entrepreneurs and leaders through a combination of hackathons, accelerators, networking events and bootcamps.

In October, the organization, with support from NVIDIA, will host the annual Ada Lovelace Hackathon, created for young women ages 11-18 to get a glimpse of all that can be done in the world of AI.

Inspired By AI

The need to embolden young women to join the tech industry is great.

Only 30 percent of the world’s science researchers are women. And fewer than one in five authors at leading AI conferences are women, about the same ratio of those teaching AI-related subjects, according to the AI Now Institute.

Founded by social entrepreneur Elena Sinel, Teens in AI is trying to change that. It aims to give young people — especially young women — early exposure to AI that’s being developed and deployed to promote social good.

The organization, which was launched at the 2018 AI for Good Global Summit at the United Nations, has an expansive network of mentors from some of the world’s leading companies. These volunteers work with students and inspire them to use AI to address social, humanitarian and environmental challenges.

“A shortage of STEM skills costs businesses billions of dollars every year, impacting UK businesses alone by about £1.5 billion a year,” Sinel said. “Yet with so few girls — especially those from disadvantaged backgrounds — studying STEM, we are depriving ourselves of potential talent.”

Sinel said that Teens in AI makes STEM education approachable and increases exposure to female role models, showing young women that a bright  STEM  career isn’t reserved for only males.

“We can’t do this on our own, so we’re constantly on the lookout for like-minded corporate partners like NVIDIA who will work with us to grow this community of young people who want to make the world more inclusive and sustainable,” she said.

Ada Lovelace Hackathon

With the company’s support, the Ada Lovelace Hackathon — named for the 19th century mathematician who is often regarded as the first computer programmer — showcases speakers and mentors to encourage young women to pursue a career in AI. This year’s event is expected to reach more than 1,000 girls from 20+ countries.

Participants will have the opportunity to receive prizes and get access to NVIDIA Deep Learning Institute credits for more advanced hands-on training and experience.

NVIDIA employees around the world will serve as mentors and judges.

Kate Kallot, head of emerging areas at NVIDIA, judged last year’s Ada Lovelace Hackathon, as well as August’s Global AI Accelerator Program for Teens in AI.

“I hope to inform and inspire young people in how they can help fuel applications and the AI revolution,” Kallot said. “While there’s a heavy demand for people with technical skills, what’s also needed is a future AI workforce that is truly reflective of our diverse world.”

Kallot talked more about the importance of fighting racial biases in the AI industry on a recent Teens in AI podcast episode.

Championing Diversity

NVIDIA’s support of Teens in AI is part of our broader commitment to bringing more diversity to tech, expanding access to AI education and championing opportunities for traditionally underrepresented groups.

This year, we announced a partnership with the Boys & Girls Clubs of Western Pennsylvania to develop an open-source AI and robotics curriculum for high school students. The collaboration has given hundreds of Jetson Nano developer kits to educators in schools and nonprofits, through the NVIDIA Jetson Nano 2GB Developer Kit Grant Program.

NVIDIA also works with minority-serving institutions and diversity-focused professional organizations to offer training opportunities — including free seats for hands-on certification courses through the NVIDIA Deep Learning Institute.

Driving the Future of AI

AI is growing at an incredible rate. The AI market is predicted to be worth $360 billion by 2028, up from just $35 billion in 2020, and is expected to add $880 billion to the U.K. economy by 2035.

Over 90 percent of leading businesses have an ongoing investment in AI, 23 percent of customer service organizations are using AI-powered chatbots and 46 percent of people are using AI every single day.

In such a landscape, encouraging young people across the globe to embark on their AI journeys is all the more important.

Learn more about Teens in AI and the NVIDIA Jetson Grant Program.

The post Next Generation: ‘Teens in AI’ Takes on the Ada Lovelace Hackathon appeared first on The Official NVIDIA Blog.

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Trash Talk: Startup’s AI-Driven Detection System Primed to Take a Bite Out of Global Waste

Trash Talk: Startup’s AI-Driven Detection System Primed to Take a Bite Out of Global Waste

Of the 8.3 billion tons of virgin plastic waste created each year, despite decades of efforts to reduce the amount that ends up in landfills, only about 9 percent gets recycled.

London-based computer vision startup Recycleye looks to give those recycling numbers a big boost with its AI-driven system for identifying waste materials.

By automating and speeding the movement of materials through sorting systems, and identifying them with more precision, Recycleye says it can significantly increase capacity for recycling companies while upping the overall recovery rate.

Pretty lofty promises from a two-year-old company that started in the most unglamorous of ways.

“We went out and collected trash from bins, took it back to my garage, and started building our first proof-of-concept,” said Peter Hedley, chief technology officer at Recycleye, which is a member of the NVIDIA Inception accelerator program for AI startups.

One Man’s Trash Is Another Man’s Treasure

Hedley and Dewulf, co-founders of Recycleye.

Hedley and company co-founder and CEO Victor Dewulf started discussing the possibilities of their technology during their masters’ work at the Imperial College of London, where they worked on applying computer vision to waste streams.

Dewulf ultimately wrote a paper on the topic, got interest from academia and industry, and then left his job as an analyst at Goldman Sachs to start working on a Ph.D. so he could refine his idea. The next thing Hedley knew, Dewulf had him dumpster diving and developing the idea into a commercial product.

Buoyed by acceptance into Microsoft’s AI accelerator program, they found themselves on the receiving end of an £800,000 (about $1.1 million) seed investment, followed by another £400,000 in grants in 2020.

By the end of 2020, the startup had already proven itself with the top waste-management firms in the U.K. Last April, they partnered with energy leader TotalEnergies and recycling pioneer Valorplast on the OMNI project, one of seven winning projects selected by nonprofit French sustainability company Citeo.

An AI on Closing the Recycling Loop 

To date, effectively recognizing and separating items that have contained food from other items has not been possible. However, a crucial step in improving recycling rates is to optimize the quality of recycled materials that are passed on to plastic manufacturers.

Recycleye’s partnership with Valorplast and TotalEnergies focuses on the application of AI to identify food-grade and nonfood-grade plastic packaging with the goal of increasing the circular recycling of these products. It could even help in the development of new applications, such as improved food packaging.

Recycleye has also partnered with universities to create WasteNet, an open-source database that is now the world’s largest waste dataset, with more than 2.5 million training images.

Recycleye is an NVIDIA Metropolis partner, providing offerings that integrate the full Metropolis stack for video analytics inference. The Recycleye team uses the NVIDIA Jetson platform and dug into accelerated deep learning tools such as pretrained models, the NVIDIA TAO Toolkit and the NVIDIA DeepStream SDK.

With the NVIDIA tools and huge training dataset behind it, Recycleye has slashed the time it takes to deploy its model from an unworkable two months down to just two hours, while achieving accuracies exceeding human vision in identifying items.

The company’s devices run on the network’s edge, doing all computations onsite, provided there’s sufficient internet connectivity. Models are trained in the cloud, on a GPU-enabled instance of Microsoft Azure, and then deployed to Recycleye’s devices at client sites.

Algorithms are automatically updated on client devices during software updates. The cloud system also processes data logs and provides the client with dashboard summaries of that data.

A Recycleye intelligent picking system.

Coming: Global Expansion and More Robots

Recycleye also develops robots powered by Recycleye Vision, jointly with FANUC, one of the world’s largest robotics manufacturers. Having already installed Recycleye Vision and Recycleye Robotics in the U.K. and France earlier this year, Hedley expects more waste companies will follow suit to automate their manual sorting with robotics.

But Recycleye has much bigger aspirations now that it has a scalable system that can be deployed globally, and Hedley said that the more deployments the company does, the better its technology will get.

“When we have a device at both the end and the front of a line, we can see what happens if the quantity of material increases, but the quality decreases,” said Hedley. “We can start optimizing machinery and configuration, and start having machines making decisions.”

The post Trash Talk: Startup’s AI-Driven Detection System Primed to Take a Bite Out of Global Waste appeared first on The Official NVIDIA Blog.

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Architecture Firm Brings New Structure to Design Workflows With Real-Time Rendering and Virtual Collaboration

Architecture Firm Brings New Structure to Design Workflows With Real-Time Rendering and Virtual Collaboration

When working on future skyscrapers, masterplans or other projects, Kohn Pedersen Fox looks beyond traditional processes.

The global architecture firm aims to find the most creative and optimal design using advanced technologies like generative design, deep learning and immersive visualization. And during design reviews, KPF relies on collaborative sessions so their teams, clients and stakeholders can interact with the proposals and provide real-time feedback.

With over 700 employees spread across cities like New York, London, Singapore, and San Francisco working on different aspects of a project, KPF also needed a way to centralize all the design models in the cloud so users could better coordinate and visualize data more efficiently.

NVIDIA Omniverse, an open platform for 3D collaboration and simulation, is helping KPF enhance these design workflows. By implementing Omniverse, and accelerating it with NVIDIA RTX technology, KPF is looking to unify their teams in one shared virtual environment, enabling them to render high-quality designs and simultaneously collaborate on the same projects.

Bringing Designers Virtually Together in One Place

The designers at KPF work on models in different software applications such as Rhinoceros 3D with Grasshopper, and Autodesk 3ds Max and Revit. The team typically runs visualizations on these apps using local hardware, but this makes it difficult to review the entire project because they can only show what is on a single application at a time rather than a collective, immersive scene.

For KPF, preparing and optimizing a model for advanced real-time performance can be a tedious, time-consuming process, one that can take days to complete. Additionally, its teams must export individual files from other applications into 3ds Max for scene building when rendering large datasets. Now, with Omniverse, the design teams at KPF can better address these challenges.

KPF deployed Omniverse Nucleus from their data center in the U.K. The Enterprise Nucleus Server synchronizes all the data from the different design applications and converts the files to Universal Scene Description format. It acts as a universal asset exchange, letting the teams send only the deltas of project file changes.

The published model datasets, now in USD, are composed in Omniverse Create and visualized in real time using the Omniverse RTX Renderer, allowing the designers to modify models as they work. Using workstations and laptops equipped with NVIDIA Quadro RTX 4000 GPUs and NVIDIA RTX A3000 GPUs, and advanced workstations powered by NVIDIA Quadro RTX 6000 and NVIDIA RTX A6000, KPF can render high-quality images with the Omniverse RTX Renderer.

Schematic of KPF’s deployment of Omniverse, with multiple users working on RTX-enabled laptops and workstations, and Omniverse Nucleus deployed from their central data center.

NVIDIA Omniverse renders ray-traced and path-traced images in high fidelity and in real time. This allows KPF designers and their clients to view the most photorealistic and physically accurate models possible.

“Omniverse serves as the ‘one source of truth’ since the latest content can be viewed from one application, rather than viewing separate data from different teams,” said Cobus Bothma, director of Applied Research at KPF. “Having the content in one location substantially saves time on the overall project by avoiding exporting large files from every individual application — we simply use Omniverse to push real-time sets of geometries without overloading the file sizes.”

Streaming From the Cloud

Omniverse is also helping KPF explore running design reviews in virtual and augmented reality.

With Omniverse and cloud-based streaming, KPF can experiment with delivering higher fidelity images to any device, whether it’s a scaled-down version or a 1:1 digital model of the property. And by streaming from the cloud, KPF can make alterations in real time through changing variables like sunlight or noise.

“We believe AR has a strong role to play in design experience and decision-making in the future,” said Bothma. “With Omniverse, we’re able to stream Omniverse Create to remote devices, allowing non-RTX devices to have similar functionality to our high-end, RTX-enabled workstations.”

KPF will continue to push all compute, geometry and visualization to the cloud, so their employees can run complex compute functions on devices like laptops and tablets. The move into the cloud will make sharing compute resources more efficient and scalable, and provide KPF with greater flexibility for upgrading and servicing the infrastructure.

Learn more about NVIDIA Omniverse for AEC.

The post Architecture Firm Brings New Structure to Design Workflows With Real-Time Rendering and Virtual Collaboration appeared first on The Official NVIDIA Blog.

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Find the Love We Shared in September: NVIDIA Canvas Update Paints With New Styles

Find the Love We Shared in September: NVIDIA Canvas Update Paints With New Styles

NVIDIA Canvas, the AI-powered painting app that enables artists to paint by material, using AI to turn doodles into beautiful artwork, released an update today introducing custom styles. Now users can apply the look and feel or “style” of their own images to their final Canvas painting.

Supporting the new Canvas update is the September Studio Driver, ready for download today. The latest Studio driver brings support for the upcoming Windows 11 release, powering creative workflows on the new operating system in addition to a host of GPU-accelerated Windows 11 features.

The Studio driver also adds support for Jianying Pro video editing software and an update to the Maxine AR SDK that improves quality and stability of AI-driven body tracking in apps like Notch.

Popular in China, Jianying Pro added NVIDIA Video Codec support for both encoding and decoding, accelerating video production up to 4.3x now that NVENC and NVDEC are utilized on NVIDIA GPUs.

In addition to today’s Canvas update, NVIDIA Omniverse recently expanded the metaverse by millions, while an NVIDIA Broadcast app update earlier this month improved the background noise removal network at the most critical moment in livestreaming.

NVIDIA GPU users can download the latest Studio driver (release 472.12) with support for all the latest creative app updates through GeForce Experience, NVIDIA RTX Experience or from the driver download page.

And if you need a device to power these amazing apps, new laptops from ASUS are also joining the NVIDIA Studio lineup: the mighty ProArt StudioBook 16, the elegant Zenbook Pro and the versatile VivoBook Pro.

Choose Your Tools Wisely

With a wide range of needs and apps that require differing levels of performance, finding and selecting the right hardware needed to match a project’s needs can be intimidating. What specs are needed? How will hardware perform against challenging creative workflows?

A laptop that can tackle today’s productions might struggle with tomorrow’s projects, and understanding the technobabble can be a frustrating experience. Keeping the goals of the project in mind, let’s look at a few scenarios and how hardware choices can benefit creators.

Power to Tell Your Story

Meet Juan, an aspiring YouTube vlogger who wants to build his portfolio of documentary-style videos that showcase modern life as a college student. Filming his projects on a 4K-supported DSLR camera such as a Canon EOS 5D Mark IV, Juan needs to quickly ingest, edit, color-correct and finalize footage for each 20-minute upload on his YouTube channel. His university provides Adobe Creative Cloud, so he plans to run post-production work in Adobe Premiere Pro, including color correction through Adobe’s Lumetri tools.

RTX GPUs, like the GeForce RTX 3070 in the ASUS ProArt Studiobook 16 OLED, are capable of powering almost anything a video editor can throw at it.

For creators like Juan, an NVIDIA Studio laptop powered by a GeForce RTX 3060 will help speed his workflow dramatically. When running multiple apps simultaneously or layering assets and filters, a step up to the GeForce RTX 3070 will be beneficial, especially with 8GB of video memory. He can play back footage at full resolution and minimize dependence on creating proxy files, even leveraging the NVIDIA Encoder (NVENC) to export final projects in no time. And he will need a factory calibrated, wide gamut color display to view his content, just like his audience will see it.

Juan’s needs align with the GeForce RTX 3060-powered Dell XPS 17 and the recently launched, GeForce RTX 3070-powered ASUS ProArt Studiobook 16 OLED.

Create and Collaborate

Anita wants to help her friends develop a new indie game and is building out environmental scenes in Unreal Engine. While using the in-engine tools to generate scenery in UE4, she also needs to revise textures using Adobe Substance Painter, and manipulate rigs from open-source modeling tool Blender.

Workflows this demanding, with multiple tools open at once, mean Anita will be best served by an NVIDIA Studio laptop powered by an RTX 3080 GPU. Available with 8GB or 16GB of VRAM, she’ll be able to render and manipulate game assets in-engine.

A Studio laptop like the HP ZBook Studio G8 with a beautiful 4K HDR AMOLED display provides both beauty and brawn.

Powered by up to a GeForce RTX 3080 or an RTX A5000 GPU, the HP Zbook Studio G8 displays sharp and realistic visuals covering 100 percent of sRGB color gamut for precise color accuracy and highly detailed photorealistic visuals.

And if her team is also powered by NVIDIA Studio, they can collaborate in real time with NVIDIA Omniverse.

Speed to Save You Time

For professional photographers, the hardware requirements can be simpler even while the workflow remains demanding. Julia wants to process and edit photos for her friend’s band using Adobe Lightroom. She could use any computer to manually process each photo she’s keeping from the shoot, but that takes time and time is money.

With an NVIDIA Studio laptop powered by an RTX 3050 or RTX 3050 Ti, Julia can use the Enhance features tool in Lightroom that uses machine learning to sharpen and improve image quality, and runs up to 5x faster on NVIDIA GPUs. Or she can boost lower-resolution images with Super Resolution, available in Adobe Camera Raw and Lightroom, intelligently enlarging photos while maintaining clean edges and preserving important details.

These AI-assisted automation tools run smoothly on the HP Envy 15 or the newly announced ASUS VivoBook Pro, powered by the GeForce RTX 3050 Ti, all without putting her over budget.

The ASUS Vivobook Pro features a stunning 14-inch NanoEdge display, powerful GeForce RTX 3050 GPU and extremely lightweight at just over 3 pounds for exceptional portability.

Beyond these examples, there’s a sweet spot for every creator. Learn more about NVIDIA Studio systems and check out the compare GPU page for a deeper dive including options for professionals.

Find the “just right” laptop for a wide-range of creative workflows.

Take It to the MAX

NVIDIA Studio is heading to the virtual Adobe MAX next month to enlighten artists and creators on how to supercharge their workflows. It all kicks off on Tuesday, Oct. 26, with an opportunity to share the love. For every click on the Studio sponsor page during the show, a $1 contribution (up to $25k) will be made to P.S. ARTS — a nonprofit organization dedicated to advancing equity and opportunity for children, by providing arts education in public schools and communities. Stay tuned for more information.

Subscribe to the Studio YouTube channel for tutorials, tips and tricks by industry-leading artists, and stay up to date on all things Studio by signing up for the NVIDIA Studio newsletter.

The post Find the Love We Shared in September: NVIDIA Canvas Update Paints With New Styles appeared first on The Official NVIDIA Blog.

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