Yes. But then comes the third level of defense, which is trusted human moderators doing occasional bot-or-not flagging to train the algorithms. I think in practice you could get fairly good at this if the system was built for it, and if most humans in the network cooperated.Yishan (@yishan):@balajis I think this will run into the “motivated bears are smarter than the laziest humans” problem and any system that detects all bots will have a high false positive rate.This is probably ok in practice because huma ...

Graph RAG Overview - Graph RAG aims to enhance LLMs by incorporating knowledge graphs, addressing limitations like lack of domain knowledge, unverifiable answers, hallucinations, and biases [1][3][4][5][9][10] - Graph RAG leverages knowledge graphs (collections of nodes, relationships, and properties) to provide more relevant, contextual, and explainable results compared to basic RAG systems using vector databases [8][9][10][12][13][14] - Microsoft research indicates Graph RAG can achieve better results with lower token costs, supported by studies showing improvements in capabilities and analyst trends [15][16] Knowledge Graph Construction - Knowledge graph construction involves structuring unstructured information, extracting entities and relationships, and enriching the graph with algorithms [19][20][21][22] - Lexical graphs represent documents and elements (chunks, sections, paragraphs) with relationships based on document structure, temporal sequence, and similarity [25][26] - Entity extraction utilizes LLMs with graph schemas to identify entities and relationships from text, potentially integrating with existing knowledge graphs or structured data like CRM systems [27][28][29][30] - Graph algorithms (clustering, link prediction, page rank) enrich the knowledge graph, enabling cross-document topic identification and summarization [20][30][34] Graph RAG Retrieval and Applications - Graph RAG retrieval involves initial index search (vector, full text, hybrid) followed by traversing relationships to fetch additional context, considering user context for tailored results [32][33] - Modern LLMs are increasingly trained on graph processing, allowing them to effectively utilize node-relationship-node patterns provided as context [34] - Tools and libraries are available for knowledge graph construction from various sources (PDFs, YouTube transcripts, web articles), with open-source options for implementation [35][36][39][43][45] - Agentic approaches in Graph RAG break down user questions into tasks, using domain-specific retrievers and tools in sequence or loops to generate comprehensive answers and visualizations [42][44] - Industry leaders are adopting Graph RAG for production applications, such as LinkedIn's customer support, which saw a 286% reduction in median per-issue resolution time [17][18]

[Music] Thank you so much for the intro. I'm so excited to be here uh talking about like figure out like how do we like AI and human like work in the world of white bowling and I built excro and if you've don't know about it like you'll see like many thing about it and one of the expectation you probably have uh about speaker at the AI engineer conference is that I talk about AI on every single sentence for the entire talk. So I'm just going to give you a warning.I'm only going to do it for the second half ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):4 stages of training LLMs from scratch, clearly explained (with visuals): ...

LLM Training Stages - The document outlines 4 stages of training LLMs from scratch [1] Visual Aids - The explanation includes visuals for clarity [1]

current LLMs are the answer to the ageold question: "How can we endlessly scale midwit consultant types?" ...

Product Overview - MindsDB is presented as a federated query engine with a built-in MCP server [1] - The platform supports querying data from over 200 sources, including Slack, Gmail, and social platforms [1] - MindsDB offers query capabilities in both SQL and natural language [1] - The platform is 100% open-source and has over 33 thousand stars [1]

General Information - The content is a wrap-up and call to action to reshare the information [1] - The author shares tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval Augmented Generation (RAGs) daily [1] Technical Focus - The author provides a clear explanation (with visuals) on how to sync GPUs in multi-GPU training [1]

LLM Advancements - The field of LLMs has experienced significant advancements in the past 12 months [1] - The report reviews the latest models, free from vendor or employer influence [1] Speaker Information - Simon Willison is the creator of Datasette, an open source tool for exploring and publishing data [1] - Simon Willison was an engineering director at Eventbrite [1] - Simon Willison is a co-creator of the Django Web Framework [1] Event Information - The recording took place at the AI Engineer World's Fair in San Francisco [1] - Readers can stay updated on upcoming events and content by joining the newsletter [1]

Model Behavior Analysis - Recent LLMs, in the studied scenario, do not exhibit fake alignment [1] - The industry is investigating if this behavior persists in more realistic settings, where models are not explicitly informed of a training scenario [1]