LAI #66: Information Theory for People in a Hurry 0 like March 13, 2025Share this postAuthor(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! This week, Im heading to San Jose, CA, for Nvidia GTC, happening from March 17 to 21. Ill attend many discussions and am excited to meet some of you there. If youre around, feel free to stop by and say hi!Now, for this weeks issue, we have a very interesting article on information theory, exploring self-information, entropy, cross-entropy, and KL divergence these concepts bridge probability theory with real-world applications. We also dive into the challenge of imbalanced data in anomaly detection, introducing a method that leverages LLM embeddings to identify subtle irregularities especially useful when traditional techniques like oversampling or undersampling fall short.Plus, weve got practical tutorials on GraphRAG, knowledge distillation, RAG for verification systems, and more exciting collaborations and community-driven opportunities. Enjoy the read! Louis-Franois Bouchard, Towards AI Co-founder & Head of CommunityThis issue is brought to you thanks to NVIDIA GTC:NVIDIA GTC is back, and its shaping up to be one of the biggest AI events of the year! Running from March 17 to 21 in San Jose, CA, GTC will bring together developers, researchers, and business leaders to explore cutting-edge advancements in AI, accelerated computing, and data science.Theres a packed agenda, including:Keynote by NVIDIA CEO Jensen Huang covering AI agents, robotics, and the future of accelerated computingThe Rise of Humanoid Robots exploring how AI is pushing robotics forwardAI & Computing Frontiers with Yann LeCun and Bill Dally a deep dive into where AI is headedIndustrial AI & Digitalization how AI is transforming industries in the physical worldHands-on Workshops & Training Labs practical sessions on AI, GPU programming, and moreJoin Us at NVIDIA GTC The AI Event of the Year! March 1721 San Jose, CA & OnlineLearn AI Together Community section!Featured Community post from the DiscordHasshiloh_pendergraff has built an open-source platform, Divora, that allows developers to fully control and train their AI models without being tied to any API or external service. The code is transparent, and you can submit improvements for community review. You can start using it for free here and support a fellow community member. If you have any questions or feedback, share them in the thread!AI poll of the week!Since the majority of you prefer building from scratch, Im curious to know how you have approached the process, if there are any environments that particularly work well, tell me in the thread!Collaboration OpportunitiesThe Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too we share cool opportunities every week!1. Ayanb1827 is building a generative AI second brain assistant and is looking for collaborators. If youre into LLMs or RAG or just want to learn and practice building cool AI projects, connect with him in the thread!2. Ivy_kaye is looking for a few individuals who are beginners in AI to study together. This might be a good place to start if you are also starting out. Reach out to her in the thread!Meme of the week!Meme shared by hitoriarchieTAI Curated sectionArticle of the weekPractical Guide to Distilling Large Models into Small Models: A Novel Approach with Extended Distillation By Shenggang LiThis article explores a practical approach to knowledge distillation, transferring the capabilities of large models to smaller, more efficient ones. It compares traditional distillation, which focuses on mimicking the final output, with step-by-step distillation, incorporating the teacher models reasoning process. The author introduces an enhanced step-by-step method that stabilizes learning through gradual rationale loss ramp-up, cosine similarity for reasoning alignment, and stronger consistency regularization. The improved method addresses the limitations of the original step-by-step approach, leading to better generalization and prediction accuracy. Code experiments using logistic regression demonstrate the effectiveness of these techniques. The author also discusses how these improvements can be applied to large language models, enhancing interpretability and performance. The key innovation is the margin-based cosine similarity loss for rationale distillation.Our must-read articles1. Exploring GraphRAG: Smarter AI Knowledge Retrieval with Neo4j & LLMs By Sridhar SampathThe article details GraphRAG, a technique developed by Microsoft that combines Neo4j Knowledge Graphs with Large Language Models (LLMs) to improve AI accuracy and reasoning. It addresses the limitations of traditional LLMs, such as hallucinations and fragmented context, by using structured graph-based retrieval before generating AI responses. The author illustrates GraphRAGs capabilities by building a Football Knowledge Graph Chatbot, demonstrating how it enhances contextual understanding, accuracy, and transparency. The process involves constructing a Neo4j Knowledge Graph, converting user queries into Cypher queries for retrieval, and using GPT to format the retrieved knowledge into human-readable responses. The author compares GraphRAG to traditional RAG, highlighting its advantages in factual retrieval, structured reasoning, scalability, and domain-agnostic applicability.2. Rethinking Imbalance: LLM Embeddings for Detecting Subtle Irregularities By Elangoraj ThiruppandiarajThis blog addresses the persistent challenge of imbalanced data in anomaly detection. It introduces a method using LLM embeddings to identify subtle irregularities, which is particularly useful when standard techniques like oversampling or undersampling fall short. It explains how converting data into embeddings allows for clustering similar events and preserving nuances often missed by traditional methods. The core idea involves comparing new data points against known anomalies in the embedding space to detect similar characteristics. The author also discusses challenges like computational requirements and model updates, offering practical suggestions for implementation and potential applications beyond anomaly detection, such as fraud detection and healthcare diagnostics.3. Building Robust Verification Pipelines for RAG Systems: Ensuring Accurate and Relevant LLM Responses By Kaitai DongThis blog explores six verification methods to ensure the accuracy and relevance of responses from Retrieval-Augmented Generation (RAG) systems. It details techniques like LLM-as-Judge, Retrieval Verification, and Entity/Claim Verification, which assess factual accuracy and source alignment. The article also covers Question-Answer Alignment to ensure relevance, Confidence Estimation for uncertainty quantification, and Multi-perspective Verification for consistency across multiple responses. Each methods strengths, weaknesses, and best use cases are analyzed, providing practical guidance for building robust verification pipelines to enhance the reliability of LLM applications.4. Information Theory for People in a Hurry By Eyal Kazin PhDThis blog explores key concepts from information theory: self-information, entropy, cross-entropy, and KL divergence. It explains how these metrics quantify surprise, uncertainty, and misalignment between probability distributions. Using a weather forecasting example, it demonstrates how cross-entropy can optimize message length in data compression and efficient communication. It also highlights the practical applications of these concepts in machine learning and data analysis, providing Python code for calculations.If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post