This study presents a novel application of Graph Neural Networks (GNNs) to optimize dealership network planning for a luxury car manufacturer in the U.S. By conducting a comprehensive literature review on dealership location determinants, the study identifies 65 county-level explanatory variables, augmented by two additional measures of regional interconnectedness derived from social and mobility data. An ablation study involving 34 variable combinations and ten state-of-the-art GNN operators reveals key insights into the predictive power of various variables, particularly highlighting the significance of competition, demographic factors, and mobility patterns in influencing dealership location decisions. The analysis pinpoints seven specific counties as promising targets for network expansion. This research not only illustrates the effectiveness of GNNs in solving complex geospatial decision-making problems but also provides actionable recommendations and valuable methodological insights for industry practitioners.

Tech expert Kurt Knutsson reveals how Figure's robot shows advanced manufacturing skills at BMW plant. Nearly six months after announcing its partnership with BMW, Figure's gleaming silver humanoid robot is making significant progress in its training for manufacturing tasks. A recently released video shows off the robot's evolving capabilities, highlighting the potential future of AI-powered humanoids in industrial settings. This development marks a crucial step forward in integrating advanced robotics into real-world manufacturing environments. GET SECURITY ALERTS, EXPERT TIPS - SIGN UP FOR KURT'S NEWSLETTER - THE CYBERGUY REPORT HERE The field of AI-powered humanoid robots is currently experiencing a surge in development, with numerous companies working towards creating versatile machines capable of performing a wide array of physical tasks typically done by humans.

The Master of Data Science and Machine Learning is a 12-month program offered by Queen's School of Computing addressing the growing demand for graduates with a Data Science and Machine Learning background from leading technology firms, healthcare companies, automobile manufacturers, research labs, and government agencies. Data Science and Machine Learning play a critical role in understanding customers, making effective decisions, recommending relevant information, detecting cyber-intrusions or financial fraud, and much more. The creation of this professional program will help to distinguish Computing graduates and increase their competitiveness for these highly skilled positions. The Program is offered remotely to up to 100 Egyptian students. There will be three cohorts of students.

CIO article sponsored by Cisco – March 11, 2022. The manufacturing industry has a history of embracing innovations designed to improve efficiencies, quality, and worker safety. Consider the impact Henry Ford made by bringing the assembly line to auto manufacturing, creating previously unheard-of efficiencies. Today, manufacturers are using newer tools like robotics, machine learning, and artificial intelligence (AI) to achieve similar goals. Click here to view original web page at www.cio.com

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The U.K. automotive industry has been a pinnacle of excellence over the last century. However, during the last few decades, sectoral shifts and an evolving competitive landscape have adversely affected the industry, with the pandemic further aggravating these challenges by throwing the demand-supply equilibrium into disarray. The recent and historic fall in car manufacturing in July – which saw production fall to its lowest level since 1956 - is a combination of factors. In an industry as resource intensive as car manufacturing, the success of every manufacturer hinges on how well they navigate both local and global market challenges, such as staffing and material shortages. On one hand, the'pingdemic' has meant that carmakers have had to deal with unexpected staff shortages at a local level. More globally, the rising prominence of semiconductors in today's tech-powered products have meant that if manufacturers can't cope with an ongoing microchip shortage, production often comes to a grinding halt.

How auto-manufacturers can apply ML & AI algorithms to enhance image analytics on their factory floor and to ensure higher product quality? Despite its great potential for quality control, vision inspection is far from reaching its full potential in manufacturing. Manual inspection, as well as traditional computer vision methods, are prone to error and are often unable to uncover the root cause of problems. In search of a solution to optimize its welding process, a leading powertrain manufacturer turned to OptimalPlus. Using advanced image algorithms, the OptimalPlus platform extracts key features from images, analyzes them, and informs MES decisions in near real-time.

As you may already know, IoT connects a vast array of portable devices, home appliances, wearables, and other electronics/machines over a network. Connected devices can signal their environment and be remotely monitored, controlled, and maintained. While all this works well on paper, there is a catch (and a rather obvious one). Round the clock monitoring naturally leads to a never-ending influx of complex data. For instance, a car manufacturing company may want to monitor everything from tire pressure to fuel performance in order to push the boundaries of future models.

Car manufacturers are using AI in every facet of the car-making process. AI-based systems are enabling robots to pick parts from the conveyor belt with a high rate of success. Using deep learning, the robot automatically determines which parts to pick, how to pick, and in what sequence. This can significantly help reduce the number of workforces, and, in turn, boost the accuracy level of the process.

The Cincinnati Auto Expo is known for its display of luxury vehicles, but you won't need to test drive anything luxurious to get a taste of the latest in semi-autonomous features. Semi-autonomous features such as automatic cruise control, rear cameras and lane changing technology – previously reserved for luxury line automobiles – have become mainstream in a matter of about five years, according to Charlie Howard, executive vice president of Greater Cincinnati Automobile Dealers Association. Howard said the industry is inching towards autonomous vehicles, or self-driving cars, feature by feature. "What you're just seeing more and more and more is that technology, you know, trickling through the various vehicles at all different price points," he said. Some of the features trigger your seat or steering wheel to vibrate when you back up too close to an obstacle, or when your car begins to veer outside of its lane.