Hosted by KnowledgeDB · 🇺🇸 US · EN · 36 episodes
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KnowledgeDB.ai is your go-to podcast for diving deep into the infrastructure that powers Generative AI. Each episode explores groundbreaking papers, insightful publications, and emerging technologies shaping the future of AI systems. From distributed computing and graph databases to hardware accelerators and model optimization, we decode the research behind the tech.Whether you're a developer, researcher, or just curious about the mechanics behind GenAI, KnowledgeDB.ai provides a blend of technical depth and practical insights to keep you informed and inspired. Tune in and stay ahead of the
KnowledgeDB hosts KnowledgeDB.ai, a technology show with 36 episodes published.
These sources provide an extensive overview of Large Language Model (LLM)-based Text-to-SQL (NL2SQL) systems, focusing on techniques like prompt engineering, supervised fine-tuning (SFT), and Retrieval-Augmented Generati
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Mem0, Graphiti, Cognee, and LangMem are open-source libraries that provide persistent memory for AI agents. Mem0 uses a hybrid database to optimize personalization and reduce token costs. Graphiti creates temporal knowle
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Text-to-SQL, translating natural language to SQL, has seen significant advancements due to Large Language Models (LLMs). However, challenges remain in handling complex database schemas, diverse SQL operations beyond simp
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This briefing document synthesizes information from several recent academic papers and a commercial announcement, highlighting cutting-edge developments in enhancing Large Language Models (LLMs) with robust memory and re
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https://arxiv.org/abs/2506.15841 The research introduces MEM1, a novel reinforcement learning framework designed to enhance language agents' efficiency and performance in complex, multi-turn interactions. Unlike traditio
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https://arxiv.org/abs/2501.13956 The research introduces Zep, a novel memory service for AI agents, designed to overcome the limitations of current retrieval-augmented generation (RAG) frameworks, which struggle with dyn
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https://machinelearning.apple.com/research/illusion-of-thinking The document investigates the capabilities and limitations of Large Reasoning Models (LRMs) , a new generation of language models designed for complex probl
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Ref: https://arxiv.org/html/2503.06474v2 The document introduces ROGRAG, a novel GraphRAG framework designed to improve large language models' (LLMs) performance on specialized and emerging topics. It addresses the limit
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The provided sources offer a comprehensive overview of the rapid and transformative evolution of Artificial Intelligence . They highlight that AI user adoption, usage, and capital expenditures are experiencing unpreceden
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https://arxiv.org/abs/2501.06642 This manuscript argues that achieving true artificial intelligence (AI) autonomy requires integrating **common sense**, a fundamental ability observed in all animals, which current system
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https://arxiv.org/abs/2504.20734 These sources introduce and describe **UniversalRAG**, a novel framework designed to enhance Retrieval-Augmented Generation (RAG) by incorporating knowledge from **multiple corpora with d
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Model Context Protocol (MCP) is presented as a crucial emerging specification for managing how AI models access enterprise data across multiple applications. It addresses the security and permission challenges arising fr
Show notesRef: https://aiwithmike.substack.com/p/text2sql-the-art-of-teaching-machines Mike Erlihson's Substack post explores the complexities of Text2SQL, the process of enabling machines to translate natural language questions i
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https://securityboulevard.com/2025/04/wizs-security-graphdb-vs-deeptempos-loglm/ This Security Boulevard article from April 2025 contrasts Wiz's Security GraphDB, a system that identifies known cloud security risks by ma
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https://arxiv.org/abs/2503.10385 The provided document introduces a language-agnostic algebraic foundation for constructing knowledge graphs from diverse data sources. This formal system aims to address the current lack
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https://arxiv.org/abs/2402.07630 The paper "G-Retriever" introduces a new method for question answering on textual graphs. It addresses the challenge of enabling users to interact with graphs through a conversational int
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Ref: https://arxiv.org/abs/2502.21321 This document provides a comprehensive survey of post-training methodologies for Large Language Models (LLMs), focusing on refining reasoning capabilities and aligning models with us
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https://graphwise.ai/resources/white-paper/knowledge-organization-llm-rag/ This Unisphere Research report, sponsored by Semantic Web Company, examines the current state of Large Language Model (LLM) and Retrieval-Augment
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https://arxiv.org/abs/2411.05844 This research paper introduces LEGO-GraphRAG, a modular framework for improving Retrieval-Augmented Generation (RAG) systems that use knowledge graphs. The framework systematically catego
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https://www.sciencedirect.com/science/article/pii/S1570826824000441 This pre-print research paper investigates the use of knowledge graphs to improve the accuracy and trustworthiness of Large Language Model (LLM)-powered
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KnowledgeDB.ai is hosted by KnowledgeDB. The show is categorised under technology and has published 36 episodes.
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