Building an AI pipeline is emerging as a critical need across many industries and applications. See and learn how this is being applied when building a smoke detection model using UAS Video Imagery for Prescribed Fire Management. Drones or unmanned aerial systems are likely to be one of the next big changes in fire service and many other use cases. Learn about building a collaborative AI data pipeline to address: • Thermal imaging for hot spots, structural or large commercial fires, natural disaster response; • Applied AI through the lens of seeing how hazard reduction happens through fire authorities, national park staff, and business individual property owners who are using AI to battle the global wildfire crisis; • Algorithms, and how they are deployed to fight future wildfires; and • Smoke detection in UAS Video Imagery for Prescribed Fire Management.
NVIDIA has early identified the promising HPC – AI convergence trend and has been working on enabling it. The growing adoption of NVIDIA Volta GPU by the Top 500 Supercomputers highlights the need of computing acceleration for this HPC & AI convergence. Many projects today demonstrate the benefit of AI for HPC, in terms of accuracy and time to solution, in many domains such as Computational Mechanics (Computational Fluid Mechanics, Solid Mechanics…), Earth Sciences (Climate, Weather and Ocean Modeling), Life Sciences (Genomics, Proteomics…), Computational Chemistry (Quantum Chemistry, Molecular Dynamics…), Computational Physics. NVIDIA today for instance, uses Physics Informed Neural Networks for the heat sink design in our DGX system.
Pizza delivery has become a cutting-edge business: Pizza Hut, for example, recruited Pepper the Robot in 2016 to take customer orders. Little Caesars has patented a pizza-making robot. Domino's, meanwhile, has teamed up with Ford to deploy self-driving delivery vehicles, and it's conducted drone deliveries. To take its technical operations to the next level, Domino's is leveraging Nvidia GPUs to accelerate and improve its AI-powered applications. What is AI? Everything you need to know about Artificial Intelligence Domino's "has grown our data science team exponentially over the last few years, driven by the impact we've had on translating analytics insights into action items for the business team," Zack Fragoso, a data science and AI manager at the pizza company, said in a blog post published by Nvidia.
We are now looking for a Senior Deep Learning Research Scientist: NVIDIA is searching for a world-class researcher in deep learning to join our applied research team. We are passionate about deep learning applied to computer vision, audio, text and other domains, with the goal of improving specific problems encountered in NVIDIA's products. After building prototypes that demonstrate the promise of your research, you will work with product teams to help them integrate your ideas into products. If you're interested in researching and applying the latest advances in the deep learning revolution to solve real-life problems, this team may be an outstanding fit for you! What You'll Be Doing Conceive deep learning approaches to solving particular product problems.
One of the most interesting papers presented at CVPR in 2019 was Nvidia's Semantic Image Synthesis with Spatially-Adaptive Normalization. This features their new algorithm, GauGAN, which can effectively turn doodles into reality. The technology has been in the works for some time, starting from Nvidia's debut of Pix2PixHD in 2017 and Vid2Vid in 2018. Finally, 2019 gave us the impressive addition of GauGAN. It'll be interesting to see what Nvidia has in store for 2020. In this article we'll see how the GauGAN algorithm works on a granular level.
Deep Learning Super Sampling (DLSS) is one of the marquee features for Nvidia's RTX video cards, but it's also one people tend to overlook or outright dismiss. The reason for that is because many people equate the technology to something like a sharpening filter that can sometimes reduce the jagged look of lower-resolution images. But DLSS uses a completely different method with much more potential for improving visual quality, and Nvidia is ready to prove that with DLSS 2.0. Nvidia built the second-generation DLSS to address all of the concerns with the technology. It looks better, gives players much more control, and should support a lot more games.
Last month, Intel announced that it would acquire Israeli AI chip startup Habana Labs for $2B. At the time, I opined that this probably spelled the end for chips from the 2016 Nervana acquisition. Intel planned to bring out both the inference and the training versions of Nervana's second attempt to out-perform NVIDIA by the end of 2019. Apparently, something ugly happened on the way to the data center. Now, as expected, Intel has announced that Nervana will be no more.
To help autonomous vehicles and robots potentially spot objects that lie just outside a system's direct line-of-sight, Stanford, Princeton, Rice, and Southern Methodist universities researchers developed a deep learning-based system that can detect objects, including words and symbols, around corners. "Compared to other approaches, our non-line-of-sight imaging system provides uniquely high resolutions and imaging speeds," said Stanford University's Chris Metzler, on the Rice University post, Cameras see around corners in real time with deep learning. "These attributes enable applications that wouldn't otherwise be possible," he added. To achieve this, the system relies on a laser that can capture detailed images of objects around corners in real time. Specifically, a light from a high-speed laser is beamed onto a wall, the light from the hidden area bounces back to the wall, and that light is reflected to a camera.
A few months ago, I started entertaining the idea of giving my car the ability to detect and recognize objects. I mostly fancied this idea because I've seen what Teslas are capable of, and while I didn't want to buy a Tesla right away (Model 3 is looking juicier with each passing day I gotta say), I thought I'd try meeting my dream halfway. Below, I've documented each step in the project. If you just want to see a video of the detector in action/the GitHub link, skip to the bottom. I started by thinking of what such a system should be capable of.
MOUNTAIN VIEW, Calif., March 11, 2020 – Lenovo and SentinelOne, an autonomous cybersecurity platform company, announced a strategic partnership to integrate SentinelOne's autonomous endpoint protection platform within Lenovo's ThinkShield security portfolio. Lenovo customers now have the ability to purchase devices with SentinelOne, delivering real-time prevention, ActiveEDR, IoT security, and cloud workload protection powered by patented Behavioral AI. Security by design is the foundation with which Lenovo builds its ThinkShield portfolio, protecting customers with the most secure endpoint solutions. With today's announcement, SentinelOne is now a core component of Lenovo's ThinkShield security offerings, empowering workstations, servers, cloud workloads, and IoT devices to autonomously defend themselves in real-time. Its patented AI models live on each device, predicting tomorrow's attacks today and enabling devices to self-heal from any attack instantaneously.