AI to Hit Mars, Blunt Coronavirus, Play at the London Symphony Orchestra

AI is the rocket fuel that will get us to Mars. It’s the vaccine that will save us on Earth. And it’s the people who aspire to make a dent in the universe. Our latest “I Am AI” video, unveiled during NVIDIA CEO Jensen Huang’s keynote address at the GPU Technology Conference, pays tribute to Read article >

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AI to Hit Mars, Blunt Coronavirus, Play at the London Symphony Orchestra

AI is the rocket fuel that will get us to Mars. It’s the vaccine that will save us on Earth. And it’s the people who aspire to make a dent in the universe.

Our latest “I Am AI” video, unveiled during NVIDIA CEO Jensen Huang’s keynote address at the GPU Technology Conference, pays tribute to the scientists, researchers, artists and many others making historic advances with AI.

To grasp AI’s global impact, consider: the technology is expected to generate $2.9 trillion worth of business value by 2021, according to Gartner.

It’s on course to classify 2 trillion galaxies to understand the universe’s origin, and to zero in on the molecular structure of the drugs needed to treat coronavirus and cancer.

As depicted in the latest video, AI has an artistic side, too. It can paint as well as Bob Ross. And its ability to assist in the creation of original compositions is worthy of the London Symphony Orchestra, which plays the accompanying theme music, a piece that started out written by a recurrent neural network.

AI is also capable of creating text-to-speech synthesis for narrating a short documentary. And that’s just what it did.

These fireworks and more are the story of I Am AI. Sixteen companies and research organizations are featured in the video. The action moves fast, so grab a bowl of popcorn, kick back and enjoy this tour of some of the highlights of AI in 2020.

Reaching Into Outer Space

Understanding the formation of the structure and the amount of matter in the universe requires observing and classifying celestial objects such as galaxies. With an estimated 2 trillion galaxies to examine in the observable universe, it’s what cosmologists call a “computational grand challenge.”

The recent Dark Energy Survey collected data from over 300 million galaxies. To study them with unprecedented precision, the Center for Artificial Intelligence Innovation at the National Center for Supercomputing Applications at the University of Illinois at Urbana Champaign teamed up with the Argonne Leadership Computing Facility at the U.S. Department of Energy’s Argonne National Laboratory.

NCSA tapped the Galaxy Zoo project, a crowdsourced astronomy effort that labeled millions of galaxies observed by the Sloan Digital Sky Survey. Using that data, an AI model with 99.6 percent accuracy can now chew through unlabeled galaxies to ID them and accelerate scientific research.

With Mars targeted for human travel, scientists are seeking the safest path. In that effort, the NASA Solar Dynamics Observatory takes images of the sun every 1.3 seconds. And researchers have developed an algorithm that removes errors from the images, which are placed into a growing archive for analysis.

Using such data, NASA is tapping into NVIDIA GPUs to analyze solar surface flows so that it can build better models for predicting the weather in space. NASA also aims to identify origins of energetic particles in Earth’s orbit that could damage interplanetary spacecraft, jeopardizing trips to Mars.

Restoring Voice and Limb

Voiceitt — a Tel Aviv-based startup that’s developed signal processing, speech recognition technologies and deep neural nets — offers a synthesized voice for those whose speech has been distorted. The company’s app converts unintelligible speech into easily understood speech.

The University of North Carolina at Chapel Hill’s Neuromuscular Rehabilitation Engineering Laboratory and North Carolina State University’s Active Robotic Sensing (ARoS) Laboratory develop experimental robotic limbs used in the labs.

The two research units have been working on walking environment recognition, aiming to develop environmental adaptive controls for prostheses. They’ve been using CNNs for prediction running on NVIDIA GPUs. And they aren’t alone.

Helping in Pandemic

Whiteboard Coordinator remotely monitors the temperature of people entering buildings to minimize exposure to COVID-19. The Chicago-based startup provides temperature-screening rates of more than 2,000 people per hour at checkpoints. Whiteboard Coordinator and NVIDIA bring AI to the edge of healthcare with NVIDIA Clara Guardian, an application framework that simplifies the development and deployment of smart sensors.

Viz.ai uses AI to inform neurologists about strokes much faster than traditional methods. With the onset of the pandemic, Viz.ai moved to help combat the new virus with an app that alerts care teams to positive COVID-19 results.

Axial3D is a Belfast, Northern Ireland, startup that enlists AI to accelerate the production time of 3D-printed models for medical images used in planning surgeries. Having redirected its resources at COVID-19, the company is now supplying face shields and is among those building ventilators for the U.K.’s National Health Service. It has also begun 3D printing of swab kits for testing as well as valves for respirators. (Check out their on-demand webinar.)

Autonomizing Contactless Help

KiwiBot, a cheery-eyed food delivery bot from Berkeley, Calif., has included in its path a way to provide COVID-19 services. It’s autonomously delivering masks, sanitizers and other supplies with its robot-to-human service.

Masterpieces of Art, Compositions and Narration

Researchers from London-based startup Oxia Palus demonstrated in a paper, “Raiders of the Lost Art,” that AI could be used to recreate lost works of art that had been painted over. Beneath Picasso’s 1902 The Crouching Beggar lies a mountainous landscape that art curators believe is of Parc del Laberint d’Horta, near Barcelona.

They also know that Santiago Rusiñol painted Parc del Laberint d’Horta. Using a modified X-ray fluorescence image of The Crouching Beggar and Santiago Rusiñol’s Terraced Garden in Mallorca, the researchers applied neural style transfer, running on NVIDIA GPUs, to reconstruct the lost artwork, creating Rusiñol’s Parc del Laberint d’Horta.

 

For GTC a few years ago, Luxembourg-based AIVA AI composed the start — melodies and accompaniments — of what would become an original classical music piece meriting an orchestra. Since then we’ve found it one.

Late last year, the London Symphony Orchestra agreed to play the moving piece, which was arranged for the occasion by musician John Paesano and was recorded at Abbey Road Studios.

 

NVIDIA alum Helen was our voice-over professional for videos and events for years. When she left the company, we thought about how we might continue the tradition. We turned to what we know: AI. But there weren’t publicly available models up to the task.

A team from NVIDIA’s Applied Deep Learning Research group published the answer to the problem: Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis. Licensing Helen’s voice, we trained the network on dozens of hours of it.

First, Helen produced multiple takes, guided by our creative director. Then our creative director was able to generate multiple takes from Flowtron and adjust parameters of the model to get the desired outcome. And what you hear is “Helen” speaking in the I Am AI video narration.

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Revved Up Retail: Mercedes-Benz Consulting Optimizes Dealership Layout Using Modcam Store Analytics

Retailers are bringing the power of AI to their stores to better understand customer buying behavior and preferences and provide them a better experience. AI startup Modcam, based in Sweden, uses smart sensors to provide detailed data on retail, showroom and office space traffic. These sensors, powered by NVIDIA Jetson Nano modules, perform real-time compute Read article >

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Revved Up Retail: Mercedes-Benz Consulting Optimizes Dealership Layout Using Modcam Store Analytics

Retailers are bringing the power of AI to their stores to better understand customer buying behavior and preferences and provide them a better experience.

AI startup Modcam, based in Sweden, uses smart sensors to provide detailed data on retail, showroom and office space traffic. These sensors, powered by NVIDIA Jetson Nano modules, perform real-time compute of AI algorithms using this data at the edge.

This allows retailers of all sorts to securely extract valuable insights regarding customer buying preferences.

Mercedes-Benz Consulting is working with the startup to hit the accelerator on the next generation of the automotive retail experience. Just outside its headquarters in Stuttgart, Germany, the company has constructed an experimental showroom equipped with Modcam sensors to test different layouts and new in-store technologies.

Since cars are an occasional, big-ticket purchase for most people, much of an automaker’s retail success relies on the showroom experience. As car companies invest in autonomous and intelligent driving technologies, they’re also looking to their storefronts to deliver an easy-to-navigate, optimized layout that enhances the shopping experience.

With the help of Modcam’s AI algorithms for edge computing, Mercedes-Benz Consulting has gained valuable insight into consumer behavior in both the show floor and service areas.

Smart Shopping

Modcam’s intelligent AI analyzes how people move in spaces. This helps retailers determine patterns, like whether a certain layout or signage is effective, and identifies customer interest in products that they may linger over.

It does so without collecting or storing private information. The deep neural networks running at the edge detect customers as people with non-identifying characteristics, and the smart sensors don’t store any of the images they analyze.

Modcam relies on the high-performance, energy-efficient Jetson Nano and is optimized using the NVIDIA Metropolis application framework to perform this real-time compute. This small, yet powerful computer lets Modcam run multiple deep neural networks in parallel for applications such as object detection, segmentation and tracking — all in an easy-to-use platform that runs in as little as 5 watts.

“Our previous generation of sensors and processors wasn’t powerful enough, so we upgraded to the NVIDIA Jetson Nano to deliver a 60x increase in neural network performance demanded by our next-generation systems,” said Andreas Nordgren, chief operating officer at Modcam.

And with this high-performance, intelligent edge solution, Modcam is helping retailers around the world deliver more optimized merchandising and a better shopping experience.

Modcam is a member of NVIDIA Inception, a virtual accelerator program that enables early-stage companies with fundamental tools, expertise and go-to-market support.

AI-Powered Luxury

With the concept store near its headquarters, Mercedes-Benz Consulting can test different floor layouts as well as touchscreen promotions and signage to display product information.

By outfitting the store with Modcam’s edge AI system, the automaker is able to measure the success of these layouts and campaigns, determining how much traffic flows to which models and how customers interact with different store configurations.

The luxury automaker can then extend these learnings to its dealerships around the world to optimize the customer buying and service experience.

And with the help of real-time edge computing, they can iterate quickly to consistently provide the best possible in-store experience.

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Robotics Reaps Rewards at ICRA: NVIDIA’s Dieter Fox Named RAS Pioneer

Thousands of researchers from around the globe will be gathering — virtually — next week for the IEEE International Conference on Robotics and Automation. As a flagship conference on all things robotics, ICRA has become a renowned forum since its inception in 1984. This year, NVIDIA’s Dieter Fox will receive the RAS Pioneer Award, given Read article >

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Robotics Reaps Rewards at ICRA: NVIDIA’s Dieter Fox Named RAS Pioneer

Thousands of researchers from around the globe will be gathering — virtually — next week for the IEEE International Conference on Robotics and Automation.

As a flagship conference on all things robotics, ICRA has become a renowned forum since its inception in 1984. This year, NVIDIA’s Dieter Fox will receive the RAS Pioneer Award, given by the IEEE Robotics and Automation Society.

Fox is the company’s senior director of robotics research and head of the NVIDIA Robotics Research Lab, in Seattle, as well as a professor at the University of Washington Paul G. Allen School of Computer Science & Engineering and head of the UW Robotics and State Estimation Lab. At the NVIDIA lab, Fox leads over 20 researchers and interns, fostering collaboration with the neighboring UW.

He’s receiving the RAS Pioneer Award “for pioneering contributions to probabilistic state estimation, RGB-D perception, machine learning in robotics, and bridging academic and industrial robotics research.”

“Being recognized with this award by my research colleagues and the IEEE society is an incredible honor,” Fox said. “I’m very grateful for the amazing collaborators and students I had the chance to work with during my career. I also appreciate that IEEE sees the importance of connecting academic and industrial research — I believe that bridging these areas allows us to make faster progress on the problems we really care about.”

Fox will also give a talk at the conference, where a total of 19 papers that investigate a variety of topics in robotics will be presented by researchers from NVIDIA Research.

Here’s a preview of some of the show-stopping NVIDIA research papers that were accepted at ICRA:

Robotics Work a Finalist for Best Paper Awards

6-DOF Grasping for Target-Driven Object Manipulation in Clutter” is a finalist for both the Best Paper Award in Robot Manipulation and the Best Student Paper.

The paper delves into the challenging robotics problem of grasping in cluttered environments, which is a necessity in most real-world scenes, said Adithya Murali, one of the lead researchers and a graduate student at the Robotics Institute at Carnegie Mellon University. Much current research considers only planar grasping, in which a robot grasps from the top down rather than moving in more dimensions.

Arsalan Mousavian, another lead researcher on the paper and a senior research scientist at the NVIDIA Robotics Research Lab, explained that they performed this research in simulation. “We weren’t bound by any physical robot, which is time-consuming and very expensive,” he said.

Mousavian and his colleagues trained their algorithms on NVIDIA V100 Tensor Core GPUs, and then tested on NVIDIA TITAN GPUs. For this particular paper, the training data consisted of simulating 750,000 robot object interactions in less than half a day, and the models were trained in a week. Once trained, the robot was able to robustly manipulate objects in the real world.

Replanning for Success

NVIDIA Research also considered how robots could plan to accomplish a wide variety of tasks in challenging environments, such as grasping an object that isn’t visible, in a paper called “Online Replanning in Belief Space for Partially Observable Task and Motion Problems.”

The approach makes a variety of tasks possible. Caelan Garrett, graduate student at MIT and a lead researcher on the paper, explained, “Our work is quite general in that we deal with tasks that involve not only picking and placing things in the environment, but also pouring things, cooking, trying to open doors and drawers.”

Garrett and his colleagues created an open-source algorithm, SS-Replan, that allows the robot to incorporate observations when making decisions, which it can adjust based on new observations it makes while trying to accomplish its goal.

They tested their work in NVIDIA Isaac Sim, a simulation environment used to develop, test and evaluate virtual robots, and on a real robot.

DexPilot: A Teleoperated Robotic Hand-Arm System

In another paper, NVIDIA researchers confronted the problem that current robotics algorithms don’t yet allow for a robot to complete precise tasks such as pulling a tea bag out of a drawer, removing a dollar bill from a wallet or unscrewing the lid off a jar autonomously.

In “DexPilot: Depth-Based Teleoperation of Dexterous Robotic Hand-Arm System,” NVIDIA researchers present a system in which a human can remotely operate a robotic system. DexPilot observes the human hand using cameras, and then uses neural networks to relay the motion to the robotic hand.

Whereas other systems require expensive equipment such as motion-capture systems, gloves and headsets, DexPilot archives teleoperation through a combination of deep learning and optimization.

It took 15 hours to train on a single GPU once we collected the data, according to NVIDIA researchers Ankur Handa and Karl Van Wyk, two of the authors of the paper. They and their colleagues used the NVIDIA TITAN GPU for their research.

Learn all about these papers and more at ICRA 2020.

The NVIDIA research team has more than 200 scientists around the globe, focused on areas such as AI, computer vision, self-driving cars, robotics and graphics. Learn more at www.nvidia.com/research.

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