#53 How Neural Networks Learn More Features Than Dimensions

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This issue is resource-heavy but quite fun, with real-world AI concepts, tutorials, and some LLM essentials. We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. We also cover an important part of the RAG pipeline: the embedding model and other topics like Dynamic Weight Logistic Regression (DWLR), Dynamic Interaction Neural Network (DINN), and a lot more!

What’s AI Weekly

This week in What’s AI, I dive into an important part of the Retrieval-Augmented Generation (RAG) pipeline: the embedding model. All your data will be transformed into embeddings, which we’ll then use to retrieve information. So, it’s quite important to understand embedding models. Let’s dive into this crucial part of the pipeline, how to fine-tune them, and why that’s important. Read the complete article here, or if you prefer watching, check out the full video on YouTube.

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

Featured Community post from the Discord

Eschnou has done some experiments with open source RAG, using OpenGPA and R2R, using complex queries over movie scripts. They have written a blog post discussing the results and limitations of current RAG approaches. The blog also introduces the idea of a RAG benchmark based on movie scripts and explores ideas to solve this context issue in RAG. Check out the blog here and support a fellow community member. Share your thoughts and questions in the Discord thread!

Collaboration Opportunities

The 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. Qubit81 is making a small peer group where we can participate in Kaggle competitions, work on projects, and grow together. If that sounds like fun, reach out to him in the thread!

2. Jjj8405 is seeking an NLP/LLM expert to join the team for a project. If this is relevant for you, connect in the thread!

Meme of the week!

Meme shared ghost_in_the_machine

TAI Curated section

Article of the week

Dynamic Weight Models: Bridging GLM and Neural Networks By Shenggang Li

This article explores the development of two novel predictive models: Dynamic Weight Logistic Regression (DWLR) and Dynamic Interaction Neural Network (DINN). DWLR addresses the limitations of traditional logistic regression by incorporating dynamically adjusted weights based on input features and activation functions. Benchmarked against logistic regression, XGBoost, LightGBM, Random Forest, and GAM, DWLR demonstrated superior performance in several metrics, particularly accuracy and AUC. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. While DINN’s performance was competitive, it showed potential for further improvement through regularization and optimization techniques. The author provides code and data for reproducibility.

Our must-read articles

1. How to Build a GraphRAG-Powered AI Assistant For The BFSI Sector By Ashish Abraham

This article explores building a GraphRAG-powered AI assistant for the BFSI sector using FalkorDB. It addresses the limitations of traditional RAG systems in handling complex, multi-hop queries by integrating knowledge graphs. It explains the advantages of graph databases over vector databases for this application, highlighting FalkorDB’s speed and efficiency. The process includes creating a knowledge graph from a PDF using LangChain and an LLM, generating Cypher queries for data retrieval, and employing a dual-LLM approach for analysis and response generation. A Gradio interface integrates these components into a functional chatbot, demonstrating how this architecture can improve customer support by efficiently managing complex financial data and answering intricate customer inquiries.

2. Mechanistic Interpretability: What’s Superposition? By Building Blocks

This article explores mechanistic interpretability, focusing on superposition in neural networks. It explains how networks can learn more features than their hidden dimensions allow, a phenomenon particularly relevant for LLMs and diffusion models. A simplified autoencoder model is used to demonstrate this, comparing linear and non-linear models with varying feature sparsity. Results show that non-linear models with sparse features exhibit superposition, leveraging bias terms and ReLU activation to mitigate feature interference and improve representation.

3. Mastering Tracing and Monitoring of AutoGen Agents with Microsoft PromptFlow By Chinmay Bhalerao

This article explains how Microsoft PromptFlow enhances the tracing and monitoring of AutoGen agents, aiding debugging and optimization of LLM-based applications. PromptFlow, a comprehensive LLM application development toolkit, streamlines the entire application lifecycle, from development to monitoring. It demonstrates a workflow with multiple AutoGen agents, leveraging PromptFlow’s tracing capabilities to track agent interactions. While it highlights the benefits, it also notes limitations, such as tracing doesn’t work as live streaming and some initial setup challenges requiring code modification within the PromptFlow library.

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Published via Towards AI