It's no secret that artificial intelligence (AI) is one of the hottest topics in the tech world today. Every day, it seems like there's a new story about how AI is being used to improve some aspect of our lives, from personal assistants to driverless cars. Given all the hype, it's no wonder that educators are eager to introduce AI concepts to their students. Now, thanks to resources inside Intel's 5-module teaching kit for AI inference teaching the Intel Distribution of OpenVINO toolkit, it is easier than ever to introduce the concepts of deep learning AI to students. Get your students hands-on coding experience with this teacher kit, which comes with a lesson plan, 5-modules of workbooks, videos, quizzes, and Jupyter* Notebook coding lab tutorials.

IRVINE, Calif., Dec. 16, 2021 (GLOBE NEWSWIRE) -- Seoul Robotics, the 3D perception solution company using deep learning AI to power the future of mobility, today announced that in 2021 the company more than doubled the number of partnerships and global employees. In July alone, Seoul Robotics closed more business than in the entirety of 2020. Since launching in North America in January 2021, Seoul Robotics has introduced three plug-and-play 3D perception systems, Discovery, Voyage, and Endeavor, as well as the most advanced version of its 3D perception platform, SENSR 2.2. SENSR 2.2 is the only 3D perception software on the market leveraging deep learning AI and is compatible with over 75 different makes and models of 3D sensors, including LiDAR. Over the past year, Seoul Robotics has secured and scaled solutions with several top-tier companies and government entities, including multiple Tier-1 original equipment manufacturers (OEMs) and Departments of Transportation.

The Golden Jubilee National Hospital in Clydebank, Scotland, has updated its cardiac CT scanning equipment. The Aquilion One/Prism Edition CT scanner introduces next-generation reconstruction technology to assist with high cardiac imaging case loads. "We are delighted with the new CT scanner," said CT superintendent radiographer Julie Morrison. "It is easy to operate and our radiologists have been delighted with the fantastic quality of cardiac images at low dose to support our heart and lung patient services. The wide area detector will enable us to obtain an entire heart in a fraction of a second and the AI technology further enhances the ability to gain low dose, high quality images at speed. We feel ready for the future."

The reputation and bottom line of a company can be adversely affected if defective products are released. If a defect is not detected, and the flawed product is not removed early in the production process, the damage can be costly – and the higher the unit value, the higher those costs will be. And worst of all, dissatisfied customers can demand returns. To mitigate these costs, many manufacturers install cameras to monitor their products as they move along their production lines. However, the data obtained may not always be useful – or more appropriately said, the data is useful, but existing machine vision systems may not be able to accurately assess it at full production speeds.

Countless use cases exist today proving that artificial intelligence (AI) systems are capable of transforming businesses, society and the economy. Examples of AI behaving appropriately in a growing number of scenarios across industries -- from autonomous driving for the elderly and disabled and detecting tumors in medical scans to AI-driven education in the classroom -- have crystallized the ever-growing value of putting AI to work at scale. Despite all of AI's glorious promises, organizations have yet to reach economies of scale in AI. This goal of scaling AI has been the biggest and most elusive challenge for businesses in their digital transformation efforts and it remains a work in progress. A recent PwC survey underscored the severity of this problem by revealing that although 90% of executives believe AI offers more opportunities than risks, only 4% plan to deploy AI enterprise-wide this year.

ST. LOUIS, MO – July 17, 2020 – World Wide Technology (WWT) today announced that it has been selected by the NVIDIA Partner Network (NPN) as the 2019 Deep Learning AI Partner of the Year for the Americas. This is the third year that WWT has been honored in this category. The NPN selected WWT for its ongoing AI research and development program. To help customers develop AI leadership, WWT published six white papers about leveraging the compute power of NVIDIA DGX systems to develop Machine Learning and Deep Learning models for real-time edge video analytics, network optimization, and performance comparisons of multiple reference architectures for ML model development. The WWT research into ML and Deep Learning is tied to real-world business outcomes, and improvements in mining safety, utilities grid optimization, and resource management for manufacturing.

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.

Deep learning has well-known tools: TensorFlow, PyTorch, Pandas and others. These are all essential, day to day tools that help data scientists be more productive. Enterprise deep learning has more comprehensive platforms like Saturn Cloud. The focus on building increasingly complex models to solve a range of business problems relies on experimentation and rapid iteration. As deep learning moves off the whiteboard and into production, a complete platform is needed to make that jump.

Imagine you're a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you've uncovered is from a previously discovered dinosaur. That's how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics. With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed.