My first job after academia was in quantitative finance, a field that relies heavily on the use of mathematical models and statistical methods to analyze financial markets. One of the most widely used tools in this field is the Fama-French factors, a set of variables developed by Nobel laureate Eugene Fama and Kenneth French toContinue reading “Lessons from the ‘Noisy Factors’ Study”
Category Archives: Uncategorized
The Future of Vector Search
Subscribe • Previous Issues Choosing the Right Vector Search System By Ben Lorica and Prashanth Rao. Since we released a vector database index nearly two years ago, the landscape of vector search and databases has evolved dramatically. The rise of Retrieval-Augmented Generation (RAG) has been a pivotal factor, with embeddings emerging as the lingua franca of Generative AI.Continue reading “The Future of Vector Search”
AI at WWDC 2024
Apple has unveiled a suite of AI-powered features and enhancements, branded under the umbrella of Apple Intelligence, that integrate seamlessly into iPhone, iPad, and Mac. These innovations include advanced language tools for rewriting, proofreading, and summarizing text, as well as significant updates to Siri, which now offers richer language understanding and contextual awareness. Users canContinue reading “AI at WWDC 2024”
SB 1047 Unpacked
SB 1047, also known as the California Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, is a proposed state bill that aims to regulate the development and deployment of advanced AI models in California. The bill targets AI systems above a certain computing power threshold, specifically those capable of performing over 10^26 operations,Continue reading “SB 1047 Unpacked”
The Promise and Perils of AI Avatars in the Workplace
Zoom CEO Eric Yuan recently unveiled a bold vision for the future of work, one where AI-powered “digital twins” could liberate us from the shackles of mundane tasks. He described a world where these sophisticated avatars seamlessly navigate meetings, engage in thoughtful discussions, and even make decisions on our behalf, freeing us to focus onContinue reading “The Promise and Perils of AI Avatars in the Workplace”
Distributed Systems: Not Just Scale
In his recent post, Mark Brooker explores the critical benefits of distributed systems, including improved availability, durability, resource utilization, reduced latency, and optimized specialized components. The post is a reminder of how distributed systems, despite their perceived complexity, can simplify overall system design and enable organizational scaling. As businesses expand, distributed systems become essential forContinue reading “Distributed Systems: Not Just Scale”
Mamba-2
Mamba is a new approach to deep learning for sequences, built upon a flexible framework called Structured State Space Models (SSMs). You can think of SSMs as a general way to build sequence models, encompassing familiar architectures like RNNs and CNNs. What makes Mamba stand out is its efficiency with long sequences: its training timeContinue reading “Mamba-2”
Reducing AI Hallucinations: Lessons from Legal AI
A recent Stanford paper sheds light on a critical issue in AI-driven legal research tools: hallucinations. Hallucinations occur when AI models generate false or misleading information, which can have severe consequences in the legal domain. Legal professionals rely on accurate and authoritative information to make informed decisions, draft documents, and advise clients. Inaccurate information canContinue reading “Reducing AI Hallucinations: Lessons from Legal AI”
Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines
By Ben Lorica and Prashanth Rao. GraphRAG (Graph-based Retrieval Augmented Generation) enhances the traditional Retrieval Augmented Generation (RAG) method by integrating knowledge graphs (KGs) or graph databases with large language models (LLMs). It leverages the structured nature of graph databases to organize data as nodes and relationships, enabling more efficient and accurate retrieval of relevantContinue reading “Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines”
GraphRAG: Design Patterns, Challenges, Recommendations
Subscribe • Previous Issues Enhancing RAG with Knowledge Graphs: Blueprints, Hurdles, and Guidelines By Ben Lorica and Prashanth Rao. GraphRAG (Graph-based Retrieval Augmented Generation) enhances the traditional Retrieval Augmented Generation (RAG) method by integrating knowledge graphs (KGs) or graph databases with large language models (LLMs). It leverages the structured nature of graph databases to organize data asContinue reading “GraphRAG: Design Patterns, Challenges, Recommendations”