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53 articles
This explainer explores how advanced AI technologies in AR glasses blur the line between real and artificial reality, examining the neural rendering techniques that make this possible and the philosophical implications of perceptual ambiguity.
Learn to build a simplified world model inspired by China's Orca system that predicts abstract world states without requiring labeled action data, demonstrating how unsupervised learning can reduce data requirements in robotics.
Learn to build a basic automated monitoring system similar to Waymo's technology that can detect suspicious activities in vehicles and trigger alerts.
Mistral AI enters robotics with Robostral Navigate, an 8B model that navigates robots using only a single camera, showcasing a significant step toward affordable, real-world robotics.
Learn to build a vision-language-action model for warehouse robotics that can reduce human intervention by half, similar to Nomagic's breakthrough implementation.
Learn to build a privacy protection system for smart glasses that can automatically detect when privacy covers are in place using computer vision and template matching techniques.
Learn how LingBot-Vision, an open-source AI model from Ant Group, uses boundary-focused learning to understand spatial relationships in images, with applications in robotics, self-driving cars, and more.
Learn to build a smart glasses application using Python and OpenCV that can detect faces, display overlays, and recognize gestures - similar to what we see in futuristic media.
Learn how to process ultrasound images using Python and computer vision techniques, similar to those used in advanced medical imaging systems like Midjourney's scanner.
Learn how to simulate augmented reality display technology similar to the RayNeo Air 4 Pro glasses using Python and computer vision libraries.
Computer vision deployments are helping retailers automate physical shelf tracking, addressing costly in-store execution failures and improving productivity. A new study by Coresight Research highlights the financial impact of these inefficiencies and the growing adoption of automation in retail.
A new AI model called 'Count Anything' can count objects in any image using only a text prompt, reducing error rates by half compared to previous systems.
