OEM : Semiconductor
NVIDIA Omniverse (tm) is a platform for simulating and connecting to virtual worlds. In the world of Omniverse, digital content designers can meet virtually to develop complex 3D content in real time. Omniverse obeys the laws of physics. It can simulate particles, fluids, materials, springs, and cables—making it perfect for training robots, designing products, or creating digital twins of buildings, factories, and even cities.
NVIDIA relies on Ansys Simulation
New NVIDIA Neural Graphics SDKs Make Metaverse Content Creation Available to All
These SDKs — including NeuralVDB, a ground-breaking update to industry standard OpenVDB,and Kaolin Wisp, a Pytorch library establishing a framework for neural fields research — ease the creative process for designers while making it easy for millions of users who aren’t design professionals to create 3D content.
Neural graphics is a new field intertwining AI and graphics to create an accelerated graphics pipeline that learns from data. Integrating AI enhances results, helps automate design choices and provides new, yet to be imagined opportunities for artists and creators. Neural graphics will redefine how virtual worlds are created, simulated and experienced by users.
The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach
Silicon Valley magic met Wednesday with 175 years of industrial technology leadership as Siemens CEO Roland Busch and NVIDIA Founder and CEO Jensen Huang shared their vision for an “industrial metaverse” at the launch of the Siemens Xcelerator business platform in Munich. Pairing physics-based digital models from Siemens with real-time AI from NVIDIA, the companies announced they will connect the Siemens Xcelerator and NVIDIA Omniverse platforms.
The partnership also promises to make factories more efficient and sustainable. Users will more easily be able to turn data streaming from the factory floor PLCs and sensors into AI models. These models can be used to continuously optimize performance, predict problems, reduce energy consumption, and streamline the flow of parts and materials across the factory floor.
Nvidia, Ready Robotics Partner to Accelerate Industrial Automation
Nvidia is set to incorporate Ready Robotics’ Forge/OS universal operating system into its Omniverse Isaac Simulator, as part of a wider collaboration between the companies.
Nvidia’s investment, contributed alongside Micron Technology and SIP Global Partners, will allow Ready Robotics to continue developing its Forge/OS platform. The system creates software drivers for digital twins of robots, helping developers such as Epson, Yasawa and Universal Robots trial and monitor units.
Visual Components Connector for NVIDIA Omniverse: The future of Manufacturing Planning
How to Maximize Your Production: Line Analysis
Toyota Indiana is the first TMNA manufacturing site to implement Invisible AI technology at scale with an initial deployment of 500 edge AI devices in 2022. The partnership supports Toyota’s core philosophy of continuous improvement for safety, quality, and operational efficiencies. Invisible AI technology helps Toyota better understand manual assembly operations, which accounts for a majority of the work performed in manufacturing.
Invisible AI’s technology uses edge AI devices with a built in NVIDIA Jetson module, 1TB of storage and a high-resolution 3D camera to track all floor activity – without using the cloud or any bandwidth. This self-contained AI device processes body motion data to identify potential for high-stress injuries and prevent simple defects in real-time, which generates millions in savings for customers. The software is entirely anonymized and privacy-centric by design and can be deployed in 60 seconds without any coding or engineering expertise, allowing customers to scale to thousands of cameras with ease. As an NVIDIA Inception and Metropolis partner, Invisible AI continues to push the boundaries of computer vision.
Amazon Robotics Builds Digital Twins of Warehouses with NVIDIA Omniverse and Isaac Sim
NVIDIA Omniverse Ecosystem Expands 10x, Amid New Features and Services for Developers, Enterprises and Creators
There are also new connections to industrial automation and digital twin software developers. Bentley Systems, the infrastructure engineering software company, announced the availability of LumenRT for NVIDIA Omniverse, powered by Bentley iTwin. It brings engineering-grade, industrial-scale real-time physically accurate visualization to nearly 39,000 Bentley System customers worldwide. Ipolog, a developer of factory, logistics and planning software, released three new connections to the platform. This, coupled with the growing Isaac Sim robotics ecosystem, allows customers such as BMW Group to better develop holistic digital twins.
At GTC, NVIDIA announced NVIDIA OVX, a computing system architecture designed to power large-scale digital twins. NVIDIA OVX is built to operate complex simulations that will run within Omniverse, enabling designers, engineers and planners to create physically accurate digital twins and massive, true-to-reality simulation environments.
AI on 5G: inspiring use cases for innovation-hungry businesses
The Ericsson-NVIDIA concept we presented at MWC delivers AI applications at the edge of a high-performance 5G Cloud RAN, allowing for data to be processed on-premise to provide real-time decisions and alerts. Running AI and 5G on the same Cloud infrastructure lowers total cost of ownership and pre-integration makes it much easier for enterprises to adopt AI on 5G solutions.
NVIDIA’s AI-on-5G Platform opens a new technical playbook by delivering AI applications at the edge over a high-performance, software-defined 5G RAN. It’s a homogenous scale-out platform (a rack of 1RU telecom-grade servers running both AI and 5G workloads) that is easily expandable from small to large deployments. Thanks to its modular architecture of AI, 5G, compute and orchestration/management stacks, it can support different customer configurations too.
The New Isaac AMR Platform (Full Version)
Sight Machine, NVIDIA Collaborate to Turbocharge Manufacturing Data Labeling
The collaboration connects Sight Machine’s manufacturing data foundation with NVIDIA’s AI platform to break through the last bottleneck in the digital transformation of manufacturing – preparing raw factory data for analysis. Sight Machine’s manufacturing intelligence will guide NVIDIA machine learning software running on NVIDIA GPU hardware to process two or more orders of magnitude more data at the start of digital transformation projects.
Accelerating data labeling will enable Sight Machine to quickly onboard large enterprises with massive data lakes. It will automate and accelerate work and lead to even faster time to value. While similar automated data mapping technology is being developed for specific data sources or well documented systems, Sight Machine is the first to use data introspection to automatically map tags to models for a wide variety of plant floor systems.
Mariner Speeds Up Manufacturing Workflows With AI-Based Visual Inspection
Traditional machine vision systems installed in factories have difficulty discerning between true defects — like a stain in fabric or a chip in glass — and false positives, like lint or a water droplet that can be easily wiped away.
Spyglass Visual Inspection, or SVI, helps manufacturers detect the defects they couldn’t see before. SVI uses AI software and NVIDIA hardware connected to camera systems that provide real-time inspection of pieces on production lines, identify potential issues and determine whether they are true material defects — in just a millisecond.
BMW uses Nvidia’s Omniverse to build state-of-the-art factories
BMW has standardized on a new technology unveiled by Nvidia, the Omniverse, to simulate every aspect of its manufacturing operations, in an effort to push the envelope on smart manufacturing. BMW has done this down to work order instructions for factory workers from 31 factories in its production network, reducing production planning time by 30%, the company said.
Product customizations dominate BMW’s product sales and production. They’re currently producing 2.5 million vehicles per year, and 99% of them are custom. BMW says that each production line can be quickly configured to produce any one of ten different cars, each with up to 100 options or more across ten models, giving customers up to 2,100 ways to configure a BMW. In addition, Nvidia Omniverse gives BMW the flexibility to reconfigure its factories quickly to accommodate new big model launches.
BMW succeeds with its product customization strategy because each system essential to production is synchronized on the Nvidia Omniverse platform. As a result, every step in customizing a given model reflects customer requirements and also be shared in real-time with each production team. In addition, BMW says real-time production monitoring data is used for benchmarking digital twin performance. With the digital twins of an entire factory, BMW engineers can quickly identify where and how each specific models’ production sequence can be improved. An example is how BMW uses digital humans and simulation to test new workflows for worker ergonomics and efficiency, training digital humans with data from real associates. They’re also doing the same with the robotics they have in place across plant floors today. Combining real-time production and process monitoring data with simulated results helps BMW’s engineers quickly identify areas for improvement, so quality, cost, and production efficiency goals keep getting achieved.
Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse
Expanding Omniverse: BMW Group Builds their Factory of the Future 2.0
Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects
People worldwide produce 2 billion tons of waste a year, with 37 percent going to landfill, according to the World Bank.
“Sorting by hand on conveyor belts is dirty and dangerous, and the whole place smells like rotting food. People in the recycling industry told me that robots were absolutely needed,” said Horowitz, the company’s CEO.
His startup, AMP Robotics, can double sorting output and increase purity for bales of materials. It can also sort municipal waste, electronic waste, and construction and demolition materials.
Tilling AI: Startup Digs into Autonomous Electric Tractors for Organics
Ztractor offers tractors that can be configured to work on 135 different types of crops. They rely on the NVIDIA Jetson edge AI platform for computer vision tasks to help farms improve plant conditions, increase crop yields and achieve higher efficiency.
How the USPS Is Finding Lost Packages More Quickly Using AI Technology from Nvidia
In one of its latest technology innovations, the USPS got AI help from Nvidia to fix a problem that has long confounded existing processes – how to better track packages that get lost within the USPS system so they can be found in hours instead of in several days. In the past, it took eight to 10 people several days to locate and recover lost packages within USPS facilities. Now it is done by one or two people in a couple hours using AI.
NVIDIA Omniverse - Designing, Optimizing and Operating the Factory of the Future
BMW Group and NVIDIA take virtual factory planning to the next level
The BMW Group and NVIDIA are generating a completely new approach to planning highly complex manufacturing systems – with the Omniverse platform. The virtual factory planning tool integrates a range of planning data and applications and allows real-time collaboration with unrestricted compatibility. As industry leaders, the BMW Group and NVIDIA are setting new standards in virtual factory planning.
Harvesting AI: Startup’s Weed Recognition for Herbicides Grows Yield for Farmers
In 2016, the former dorm-mates at École Nationale Supérieure d’Arts et Métiers, in Paris, founded Bilberry. The company today develops weed recognition powered by the NVIDIA Jetson edge AI platform for precision application of herbicides at corn and wheat farms, offering as much as a 92 percent reduction in herbicide usage.
Driven by advances in AI and pressures on farmers to reduce their use of herbicides, weed recognition is starting to see its day in the sun.
The misplaced optimism is twofold: first there is the fact that eight years later Intel has again appointed a new CEO (Pat Gelsinger), not to replace the one I was writing about (Brian Krzanich), but rather his successor (Bob Swan). Clearly the opportunity was not seized. What is more concerning is that the question is no longer about seizing an opportunity but about survival, and it is the United States that has the most to lose.