A deep dive into the convergence of generative AI and edge computing.
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A.I.
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AI is transforming healthcare by enhancing diagnostic accuracy, optimizing patient care, and improving treatment outcomes, while also necessitating careful consideration of ethics, privacy, and data security.
GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
Wevolver was at the center of action during the Hexagon LIVE in Eindhoven, reporting live on cutting-edge tech and smarter manufacturing solutions, interviewing experts and exploring innovation in digital workflows, sustainability, and precision engineering. Watch the livestream on demand here!
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.
4 minutes read
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.
5 minutes read
In the final chapter of the Edge AI Technology Report: Generative AI Edition explores the technical hurdles organizations face as they attempt to leverage edge-based generative AI. It also examines strategic opportunities for innovation in hardware, deployment configurations, and security measures.
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.
4 minutes read
webinar | JAN 15, 2025
Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th
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.
4 minutes read
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.
4 minutes read
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.
