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ORGANIZATIONS.

SHAPING THE INDUSTRY.

The Next Byte

Entertainment

The Next Byte Podcast is hosted by two young engineers - Daniel and Farbod - who select the most interesting tech/engineering cont...

165 Posts

EPFL

University

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

56 Posts

High Tech Campus Eindhoven

High Tech

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

49 Posts

ETH Zurich

University for science and technology

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

43 Posts

Edge Impulse

Machine Learning

Edge Impulse offers the latest in machine learning tooling, enabling all en...

24 Posts

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Latest Posts

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.

Challenges and Opportunities in Edge-based Generative AI

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.

AI-Powered Health Diagnostics

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