Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making
In large-scale warehousing and distribution operations, conveyor belts are an essential infrastructure that must operate with near-zero downtime to ensure the timely delivery of products. The presence of loose or foreign items on a conveyor belt can pose a serious risk to these operations.
To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.
Smart eyewear promises to transform how we see and interact with the world. Among its many potential advantages, the technology offers hands-free access to information, vision enhancement, and accessibility tools.
Matroid builds no-code computer-vision detectors that can spot everything from microscopic material defects to real-time safety hazards on a factory floor.
In large-scale warehousing and distribution operations, conveyor belts are an essential infrastructure that must operate with near-zero downtime to ensure the timely delivery of products. The presence of loose or foreign items on a conveyor belt can pose a serious risk to these operations.
In this post, we'll walk through how to evaluate that progress using the same metrics our platform provides automatically, so you can build detectors that get smarter, sharper, and more reliable over time.
The no-code platform from Matroid trains ordinary cameras to act like expert inspectors, turning simple footage into a pixel-level defect checklist. Even a handheld GoPro can spot issues human eyes miss—using remarkably small datasets.
In this article, we will look at the challenges that engineers encounter when deploying a predictive maintenance system and how edge AI platforms like Edge Impulse can help factories optimize and deploy predictive maintenance models on industrial edge devices.
The greater the amount of high-quality data you have, the better your machine learning model will be. This post looks at the challenges (and solutions) involved in creating and organizing a dataset when you’re starting from scratch.