Artificial Intelligence

Integrated, learning maintenance system identifies potential faults early, avoiding more than 500 minutes of vehicle assembly disruption every year.

The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) is calling for participants in a new consortium supporting development of innovative methods for evaluating artificial intelligence (AI) systems to improve the rapidly growing technology’s safety and trustworthiness.

To build a robust ecosystem, Aetina introduces the Pro-AI Service, offering its customers a comprehensive solution that seamlessly integrates AI hardware, software, various platforms, and structures.

SymphonyAI, a leader in predictive and generative enterprise AI SaaS, has revealed its inaugural suite of generative AI industrial copilots to help frontline workers speed operations and improve efficiency.

iMerit, a leading artificial intelligence (AI) data solutions company, has introduced a new defect detection solution to accelerate AI development for advanced manufacturing and warehouse robotics applications.

Companies introduce Siemens Industrial Copilot, a generative AI-powered assistant, designed to enhance human-machine collaboration and boost productivity.

To meet the increasing demand in reverse logistics and eliminate the subjectivity of human inspectors, Griffyn Robotech developed the Deepsight Cosmetic Grading Machine (CGM). The patented vision system enables the quick and accurate detection, measurement and analysis of all surface defects with high repeatability. 

This is precisely the goal pursued by the ‘AIQualify’ research project with the help of an emerging software framework. A software-based assistance system supports users in defining and formulating test and evaluation criteria

The deep learning model for grain size determination, on the other hand, can be used to assess arbitrarily large component areas and exhibits high accuracy and ideal reproducibility.

QSort is based on machine-human interaction, where the machine is automating the tedious task for the quality controller while the quality controller is guiding the machine according to their knowledge and experience.