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Efficient antenna technology is critical for IoT, smart metering, and industrial connectivity. This article explores how omnidirectional antennas are now in demand to simplify designs and enhance performance.

Featured

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.

RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained

ORGANIZATIONS. SHAPING THE INDUSTRY.

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Nordic Semiconductor

Semiconductors

Nordic Semiconductor is a fabless semiconductor company specializing in wireless technology that powers the IoT.

181 Posts

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EPFL

University

Located in Switzerland, EPFL is one of Europe’s most vibrant and cosmopolit...

56 Posts

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High Tech Campus Eindhoven

High Tech

High Tech Campus Eindhoven is Europe's smartest square km and has the ultim...

49 Posts

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ETH Zurich

University for science and technology

Freedom and individual responsibility, entrepreneurial spirit and open-​min...

43 Posts

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University of Michigan

Higher Education

Work together. Create smart machines. Serve society. University of Michiga...

40 Posts

The ETH spin-off Flink Robotics wants to revolutionize the handling of packages. Its founders Moritz Geilinger and Simon Huber have developed software that allows robots to work together and quickly take on new tasks.

Readying robots for new tasks

Latest Posts

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.

RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained

A team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments.

Can robots learn from machine dreams?