Researchers at Stanford Engineering have developed an AI-trained model to accurately recreate the hand movements of elite-level pianists and the physical stresses they endure while playing.
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Computer Vision
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According to , the global AI robotics market will reach $17 billion in 2024 and will keep growing by 24% annually. Today, robots deliver packages, conduct geological surveys, and entertain visitors of parks and exhibitions – however, that’s not the limit of their abilities.
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Struggling with safety compliance and quality control in manufacturing? Discover how Matroid’s no-code computer vision platform is transforming the industry with real-time defect detection, PPE monitoring, and predictive maintenance.
Brain–machine interfaces (BMIs) have enabled a handful of test participants who are unable to move or speak to communicate simply by thinking.
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A team of Caltech researchers has developed an analogous algorithm for autonomous robots—a planning and decision-making control system that helps freely moving robots determine the best movements to make as they navigate the real world.
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AI in the IoT also needs to be performed as close to the sensors as possible, all the way to the edge, on increasingly capable devices in terms of compute power.
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Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
EPFL research investigating the potential impact on education of AI assistants has found that systems like GPT-4 can answer up to 85% of university assessment questions correctly.
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The method could help communities visualize and prepare for approaching storms.
Stanford engineers are testing foods using an approach that combines a three-dimensional mechanical technique and AI to mimic our sensory experience.
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Artificial intelligence (AI) is rapidly transforming healthcare by leveraging machine learning techniques to analyze medical images, patient data, and genetic information with unprecedented speed and accuracy, significantly improving early disease detection and enhancing diagnostic precision.
Optical sensors are the “eyes” of industrial systems, relying on robust peripherals for reliable image processing. As sensors grow more powerful, challenges like data management, mechanical stress, and thermal issues in compact designs demand innovative solutions.
By Xsens MVN Analyze
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Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server — a process that’s expensive, energy-intensive and often slow.
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