The Motion Planning Research team is specialized in fusion of Machine Learning and Classical Methods, researches new deep learning approaches, builds deep neural networks, algorithms, and software to impact across navigation, behavior, route planning, as well as trajectory optimization, numerical optimization, and model predictive control. The team creates proof of concepts and once an idea is effective, works to put then into production. Motional's Machine Learning team has produced ground breaking advancements in the autonomous vehicle industry including nuPlan (https://arxiv.org/abs/2106.11810), Motional is a driverless technology company making self-driving vehicles a safe, reliable, and accessible reality. The Motional team is made up of engineers, researchers, innovators, dreamers and doers, who together are creating a first-of-its-kind technology with the potential to transform the way we move.

Pittsburgh, PA--March 29, 2022: Gather AI, the first truly automated inventory management system that brings near-real-time visibility to warehousing operations, has positively impacted many customers. Small-size physical stores all the way to multinational corporations like Walmart and Amazon depend on reliable and accurate inventory management software systems, as these are needed everywhere, and with even more challenging tasks at large-scale retailers, such as the warehouses from the biggest retailers in the Fortune 500. But, even with inventory management software, large organizations still rely on people on forklifts with barcode readers to perform cycle counts, from a significant amount of employees to costly machinery to properly manage large-scale inventories, such as those found in retail, third-party logistics, food distribution, and warehouses in air cargo industries. Most importantly, the visibility of what's sitting on the DC floor is delayed by 3-4 months. To solve this significant problem, Gather AI is building the world's first truly autonomous inventory management platform, freeing logistic-driven organizations from inefficient and manual tasks through intelligent and robust automation.

Arnav Bawa, a student in the artificial intelligence program at Chandler Gilbert Community College, has developed an AI application to interpret EEG brain wave scans. The application can help predict brain seizures, so a patient can take medication or prevent injury from falling. William Glover, a student in the artificial intelligence program at Chandler Gilbert Community College, has developed an AI application for drones. The application can be used in indoor search and rescue situations. It uses AI to interpret a live video feed to look for and recognize people who may be trapped in a burning building.

StradVision, a company developing AI-powered computer vision technology for autonomous vehicles and advanced driver assist systems (ADAS), announced today that German automotive technology supplier ZF Friedrichshafen AG ("ZF") has acquired a 6% stake in the company. As part of the investment, the two companies will expand their portfolio of perception and safety technology for autonomous vehicles. ZF is one of the world's largest automotive component suppliers. The company's products are found in many vehicles from the world's top automakers, including BMW and Toyota Motor Corp. ZF offers a full portfolio of components and systems for active and passive safety technology, which more recently focuses on the four fields of autonomous driving, electromobility, integrated safety and vehicle motion control, including steering systems. ZF's autonomous driving technology also includes the ZF ProAI high-performance processor and vehicle software supporting ADAS and self-driving functions.

Professor and CADScor System inventor will present on the AI-based CADScor System, a rapid assessment of coronary artery disease. The session will be live broadcast on this link: https://us02web.zoom.us/j/86991641341?pwd WGx2V0c1aXkvbVhBQTdhdWhESGZiUT09. The recording and the presentations will also be available after the event. The information was provided, through the agency of the above contact person, for publication at the time specified by the company's news distributor, GlobeNewswire. About Acarix: Acarix is a Swedish medical device company that innovates solutions for rapid AI-based rule out of Coronary Artery Disease (CAD).

Aidoc, the leading provider of healthcare AI solutions, today announced that it received FDA 510(k) clearance for its triage and notification of pneumothorax on X-ray exams. A one-stop partner for the enterprise's clinical AI needs, Aidoc's other seven FDA-cleared solutions are already implemented across U.S. health systems, flagging and communicating suspected pathologies in CT exams – and now have expanded to the high volume X-ray modality. Aidoc's newly FDA-cleared solution runs on all X-ray machines including portable ones, and is designed to analyze X-ray images. It automatically flags positive cases of pneumothorax, facilitating physicians to read X-rays in a timely manner. The ability to quickly identify pneumothorax is imperative as it can worsen rapidly and result in respiratory or cardiac failure.

Artificial Intelligence (AI) holds great potential for robotics, enabling a range of benefits in sectors as diverse as manufacturing and healthcare. Though AI is already making its mark on robotics, it is at a much slower pace and in a far narrower field of application than is commonly assumed. This paper summarizes the most common applications of AI in robotics currently in commercial use and provides an overview of market potential over the next 5 to 10 years. This paper looks at the most common uses of AI in commercial robotic applications and discusses market trends. The impact of AI in robotics on work and jobs is also a topic.

Advances in the field of artificial intelligence resulted in incorporation of these technologies into the process of scientific research and in the field of photonics. Such methods as machine learning and deep learning have become popular design tools for development of photonic devices. Design in this case implies prediction of a physical response of a given structure (forward design) as well as the reverse process of finding parameters of a structure required to provide a desired response (inverse design). While design procedures arguably remain the most widespread implementation of machine learning in photonics, novel applications begin to emerge leading to evolvement of a new research area of intelligent photonics.

JPMorgan Chase and Acumen America co-led FutureFit AI's round to provide workers with an AI-powered tool to help them navigate their careers FutureFit AI, the AI-powered career navigation platform, today announced that it closed on a funding round of $4.5M in late 2021, which will fuel product innovation and growth as the company meets the demand for career navigation and reskilling that has skyrocketed in the aftermath of the COVID-19 pandemic. The investment was co-led by JPMorgan Chase and Acumen America and is the company's first financing round. FutureFit AI is the market leader as the'navigation layer' of workforce development seamlessly connecting multiple stakeholders and solutions in one comprehensive user journey. "To support a more equitable economic recovery, we need to find ways to connect overlooked talent with career opportunity at a time when the needs of employers and the labor market are changing faster than ever," said Hamoon Ekhtiari, CEO of FutureFit AI. Ekhtiari immigrated to North America two decades ago when he didn't know any English and directly experienced the challenges of navigating the labor market.

The project, funded by Fonds de recherche du Québec – Nature et technologies (FRQNT), in partnership with the Ministère de l'Énergie et Ressources naturelles (MERN), under its second sustainable mining partnership research program, will analyze all airborne image data to predict the nature of the subsoil. The algorithms generated can be used as guides to target mineral exploration sites. This new approach to artificial intelligence aims to train parallel neural networks, each processing one type of variable, such as aeromagnetic or electromagnetic data. When processing is complete, a system will combine each network's predictions to generate a final prediction. Professor Gloaguen's team is working with experts in artificial intelligence applied to mining for this network development project, the first phase of which is already underway.