Unsupervised learning is a machine learning approach that finds patterns, structure, or useful representations in unlabeled data. It groups similar observations, compresses high-dimensional features, detects outliers, discovers item relationships, and supports modern representation learning.
Understanding industrial vision systems by examining their components, imaging fundamentals, AI integration since 2020, and how to choose the right solution for every application.
Explore the rise of intelligent EV hardware and how real-time processing, ML acceleration, and hardware virtualization are enabling safer, smarter, software-defined electric vehicles.
ETH Zurich researchers have developed a method that makes AI responses increasingly reliable. Their algorithm specifically selects data relevant to the question. In addition, even AI models up to 40 times smaller achieve the same output performance as the best large AI models.
A new method from the MIT-IBM Watson AI Lab helps large language models to steer their own responses toward safer, more ethical, value-aligned outputs.
“InteRecon” enables users to capture items in a mobile app and reconstruct their interactive features in mixed reality. The tool could assist in education, medical environments, museums, and more.
This article covers advanced architectural patterns, performance optimisation strategies, and critical operational considerations for building production-grade AI infrastructure systems!
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.