High above our heads, a silent battle is unfolding within Earth's magnetic shield. For decades, scientists have tracked "killer electrons"—ultrafast particles capable of piercing satellite armor and ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Martin Kleppmann, an associate professor at ...

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents. The classic RAG workflow (chunk documents, calculate ...

Abstract: Hyperspectral image classification has been a very active area of research in recent years. It faces challenges related with the high dimensionality of the data and the limited availability ...

Microsoft Research conducts fundamental science and technology research across a spectrum of research areas. With labs around the globe we pursue breakthroughs across the computing and AI stack to ...

Vector embeddings are the backbone of modern enterprise AI, powering everything from retrieval-augmented generation (RAG) to semantic search. But a new study from Google DeepMind reveals a fundamental ...

Google announced a new multi-vector retrieval algorithm called MUVERA that speeds up retrieval and ranking, and improves accuracy. The algorithm can be used for search, recommender systems (like ...

Modern language models can generate incredible responses, but they don't work in isolation. Behind the scenes, the best systems combine large models with fast, context-aware search powered by vector ...

TL;DR: Normally vector indexing is thought of as a common implementation for Generative AI. While this is true at a very rudimentary level, this blog discusses how to use vector indexes in Neo4j to ...

Abstract: The large-scale multi-objective optimization problem is characterized by a large decision space. How to design an efficient optimization algorithm that can search a large decision space and ...