embedding
PulseAugur coverage of embedding — every cluster mentioning embedding across labs, papers, and developer communities, ranked by signal.
- used by Vector Search 80%
- used by large-language models 70%
- used by Vector Databases 70%
- used by cosine similarity 70%
- instance of Vector Databases 60%
- instance of Tokens 60%
- used by artificial neural network 60%
- instance of generative artificial intelligence 60%
- used by fine-tuning 60%
- affiliated with large-language models 50%
- instance of large-language models 50%
- other cosine similarity 50%
6 day(s) with sentiment data
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Hybrid Search Explained: Combining BM25 and Vector Embeddings for AI
This article explains the concept of hybrid search, which combines traditional keyword-based search (like BM25) with modern vector search. Vector search uses embeddings to represent text as numerical vectors, allowing f…
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New method generates graph embeddings without training
Researchers have developed a method to generate informative graph embeddings without relying on complex model design or gradient-based training. By propagating random features through hierarchical structures derived fro…
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RAG explained: How LLMs access and use specific data for answers
Retrieval-Augmented Generation (RAG) is a technique used in LLM applications to provide models with access to specific, up-to-date data beyond their training sets. A RAG pipeline involves a retriever finding relevant te…
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AI embeddings forget like human brains, study finds
New research indicates that the degradation of recall in AI memory systems, specifically those using vector databases and embeddings, mirrors human memory's forgetting patterns. The study found that embeddings, regardle…
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AI agents and RAG explored for humanitarian aid at Ubuntu Voice Hackathon
The Ubuntu Voice Hackathon is exploring the use of AI, specifically AI agents and retrieval-augmented generation (RAG), to support communities in humanitarian emergencies. The project aims to create a low-bandwidth plat…
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AI Concepts Explained: A Guide to Modern AI
This article provides a jargon-free explanation of 15 core concepts that underpin modern artificial intelligence. It covers fundamental areas such as machine learning, deep learning, neural networks, and natural languag…
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Developer's intent detector struggles with negation despite high recall
A developer built an intent detection system using embeddings to identify user messages requiring action, aiming to reduce costs associated with calling large language models for every message. The system compares messa…
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AI uses embeddings to map meaning for semantic search and memory
AI models determine similarity by converting meaning into spatial locations through embeddings. This process allows for semantic search, recommendations, and the functioning of AI memory by identifying the nearest point…
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New research explores spectral clustering for Gaussian mixture block models
Researchers have initiated a study into clustering and embedding graphs sampled from high-dimensional Gaussian mixture block models. This approach aims to model modern networks by associating each vertex with a latent f…
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Evaluating RAG Systems: Metrics and Layered Harnesses
This article discusses practical methods for evaluating Retrieval-Augmented Generation (RAG) systems, moving beyond subjective assessments. It highlights the importance of separating retrieval failures from generation f…
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Lean AI Memory uses Markdown and Git for simpler AI context retention
A developer has proposed a novel approach to AI memory management called Lean AI Memory, which utilizes human-readable Markdown files and Git for version control. This method aims to simplify how AI agents retain projec…
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Demystifying 5 Key AI Terms: Embeddings, Agents, RAG, Fine-Tuning, and Context Engineering
This article aims to demystify five key terms in the field of artificial intelligence: embeddings, AI agents, Retrieval-Augmented Generation (RAG), fine-tuning, and context engineering. By understanding these concepts, …
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Understanding Transformers: From Tokenization to Self-Attention
This article breaks down the core concepts behind Transformer models, focusing on how they process language. It explains tokenization, where text is divided into smaller pieces, and token IDs, which are numerical repres…
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AI App Security: Beyond Prompt Injection to API Key Hygiene
Building secure AI applications requires attention to both model-layer and access-layer security. While prompt injection and data leakage are common concerns, a more frequent vulnerability involves exposing API keys dir…
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Vector Databases: The Engine Behind Modern AI Applications
This article provides an in-depth explanation of vector databases, highlighting their crucial role in powering many AI applications. It delves into concepts such as embeddings, nearest neighbor search, and their functio…
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Building an AI Database Assistant: From Natural Language to Secure SQL
This article details the process of building an AI-powered database assistant that can answer questions in natural language by generating SQL queries. It emphasizes that the core challenge lies not in the AI's ability t…
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RAG Systems Enhanced with Hybrid Search and Reranking Beyond Vector Search
This article delves into enhancing Retrieval-Augmented Generation (RAG) systems by moving beyond simple vector search. It explains that while embeddings are crucial for semantic similarity, they are insufficient on thei…
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AI/ML Interview Prep: Focus on Scenarios Beyond LeetCode
Several articles from Towards AI and other sources offer guidance for AI and ML professionals preparing for interviews. The content focuses on practical, scenario-based questions related to embeddings and MLOps, aiming …
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New frameworks leverage LLMs and embeddings to expand scientific taxonomies · 2 sources tracked
Two new research papers introduce frameworks for enhancing scientific taxonomies using Large Language Models (LLMs) and embeddings. The first, ReLTEx, focuses on reliable LLM-based taxonomy expansion by combining LLM ge…
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AWS launches vector search for Amazon DynamoDB
AWS has officially launched vector search capabilities for Amazon DynamoDB, allowing users to store embeddings alongside their operational data. This feature enables similarity searches with low latency and high recall,…