Categories: AI/ML News

Self-supervised machine learning adapts to new tasks without retraining

The field of machine learning is traditionally divided into two main categories: “supervised” and “unsupervised” learning. In supervised learning, algorithms are trained on labeled data, where each input is paired with its corresponding output, providing the algorithm with clear guidance. In contrast, unsupervised learning relies solely on input data, requiring the algorithm to uncover patterns or structures without any labeled outputs.

Five machine learning types to know

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision, large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications. The validation and training datasets…

December 21, 2023

In "FAANG"

Unsupervised and semi-supervised anomaly detection with data-centric ML

February 9, 2023

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Self-Supervised Learning with Gaussian Processes

Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear classification, etc. To ensure smoothness of the representation space,…

January 31, 2026

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AI Generated Robotic Content