OEM : Semiconductor
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Geek+ announces $100 million series E1 financing round
Geek+, a global provider of autonomous mobile robot technologies, announced today it has closed a new, $100 million series E1 funding series. Investors in the round include Intel Capital, Vertex Growth, and Qingyue Capital Investment. The company will use this funding to accelerate its global market expansion and invest in its AMR technology research and development for key product innovation.
Intel to spend €17bn on chip mega-factory in Germany
“Today 80 percent of chips are produced in Asia. Our landmark pan-European investment addresses the global need for a more balanced and resilient supply chain,” Intel CEO Pat Gelsinger said during a webcast to announce the European investment. Gelsinger said the factories will make chips using Intel’s most advanced transistor technology, though no further details or specifics were provided. The company plans to break ground on the site in the first half of 2023, and produce products by 2027.
Landing AI Secures Funding to Unlock Power of Small Datasets, Unleashing Next Era of AI
Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens™, a fast, easy to use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value.
“You don’t always need big data to win with AI. You need good data that teaches the AI what you want it to learn,” said Ng, Founder & CEO of Landing AI. “AI built for 50 million data points doesn’t work when you only have 50 data points. By bringing machine learning to everyone regardless of the size of their data set, the next era of AI will have a real-world impact on all industries.”
The data-centric approach of Landing AI is also key to making LandingLens fast and easy-to-use. The process of engineering the data, instead of the AI software, gives an efficient way for manufacturers to teach an AI model what to do. Domain experts, not just AI experts, can now build an AI system, and take it to production. For example, rather than needing to write pages of code to train a neural network, a domain expert can do it with a few mouse clicks. This no code/low code data-centric platform enables new users to build advanced AI models in less than a day. Vision inspection projects that used to take over a year can be executed in just weeks using LandingLens.
Inside Intel’s Bold $26 Billion U.S. Plan To Regain Chip Dominance
What’s Harder to Find Than Microchips? The Equipment That Makes Them
We typically associate microchips with the latest and greatest technology, but it turns out that most of the chips that go into the products we use are made with older manufacturing techniques. No one knows precisely what proportion of the world’s microchips is made on used equipment, but Mr. Howe, owner of SDI Fabsurplus, estimates it might be as much as a third.
TSMC is expanding its capacity to make older chips by building a new plant for that purpose in Japan. Intel has no plans to build new capacity for manufacturing older kinds of chips, and continues to concentrate on making bleeding-edge chips, says Lisa Spelman, a vice president in Intel’s data-center group.
Secure device onboarding for manufacturing supply chain
FDO 1.0 can offer many benefits for manufacturers that have industrial and enterprise devices. It’s also useful with multi-ecosystem applications and services and helps streamline distributor sales. Other benefits for manufacturers include:
- Zero-touch onboarding: It can integrate with existing zero-touch solutions.
- Speed and security: It is designed to onboard with IoT devices in less than a minute, which is up to 20 times faster than it would have been for a manual installer.
- Hardware flexibility: It is designed to be hardware-agnostic and work with any microcontroller or computer processor.
- Cloud flexibility: As with hardware, it is flexible and can work with the internet and on-premise.
- Late binding: This reduces costs and complexity in the supply chain by providing a single SKU for all customers.
Late binding, in particular, is a key aspect of the process, Kerslake said. “Late binding reduces costs and complexity in supply chain, providing a single device SKU for all customers instead of making unique SKUs and creating a mess of things.”
Tools Move up the Value Chain to Take the Mystery Out of Vision AI
Intel DevCloud for the Edge and Edge Impulse offer cloud-based platforms that take most of the pain points away with easy access to the latest tools and software. While Xilinx and others have started offering complete systems-on-module with production-ready applications that can be deployed with tools at a higher level of abstraction, removing the need for some of the more specialist skills.
John Deere and Audi Apply Intel’s AI Technology
Identifying defects in welds is a common quality control process in manufacturing. To make these inspections more accurate, John Deere is applying computer vision, coupled with Intel’s AI technology, to automatically spot common defects in the automated welding process used in its manufacturing facilities.
At Audi, automated welding applications range from spot welding to riveting. The widespread automation in Audi factories is part of the company’s goal of creating Industrie 4.0-level smart factories. A key aspect of this goal involves Audi’s recognition that creating customized hardware and software to handle individual use cases is not preferrable. Instead, the company focuses on developing scalable and flexible platforms that allow them to more broadly apply advanced digital capabilities such as data analytics, machine learning, and edge computing.
Intel Accelerates AI for Industrial Applications
The human eye can correct for different lighting conditions easily. However, images collected by camera can naturally vary in intensity and contrast if background lighting varies as well. We’ve seen scale challenges observed by factories trying to deploy AI for defect detection based on the exact same hardware, software and algorithm deployed on different machines on the factory floor. Sometimes it took months for factory managers and data scientists to find out why they were getting great results on one machine with high accuracy, low false positive and false negative rates, while on the next machine over the AI application would crash.
Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds
John Deere performs gas metal arc welding at 52 factories where its machines are built around the world, and it has proven difficult to find defects in automated welds using manual inspections, according to the company.
That’s where the successful pilot program between Intel and John Deere has been making a difference, using AI and computer vision from Intel to “see” welding issues and get things back on track to keep John Deere’s pilot assembly line humming along.
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.