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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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Brain–machine interfaces (BMIs) have enabled a handful of test participants who are unable to move or speak to communicate simply by thinking.
Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th
Featured
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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Latest Posts
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.
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.
4 minutes read
Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge.
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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.
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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.
4 minutes read
Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.
