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In his recent book, The Anxious Generation, Jonathan Haidt presents a compelling argument that smartphones and social media have significantly disrupted childhood development, leading to a rise in mental health issues among adolescents. This stance has prompted debate among researchers, with some questioning the way Haidt interprets the supporting evidence. A recent discussion between HaidtContinue reading “Beyond Screens: Factors Influencing Adolescent Mental Health”

Today’s machine learning landscape is defined by large language and foundation models. Unlike traditional ML models of the past, where models were built and trained for specific tasks from scratch, large language models (LLMs) and foundation models now serve as the starting point for most applications. While pre-trained on massive datasets, foundation models often requireContinue reading “Why Your Company Must Invest in Post-Training”

I’ve previously written about GraphRAG, a form of Retrieval-Augmented Generation (RAG) that combines the power of vector embeddings with knowledge graphs (KG). While traditional RAG relies solely on vector-based similarity searches to retrieve relevant information, GraphRAG introduces knowledge graphs to represent and traverse relationships between entities, concepts, and facts. This combination allows GraphRAG to enhanceContinue reading “Enhancing AI Retrieval Systems with Structured Data”

Context loss is a known challenge faced by traditional Retrieval-Augmented Generation (RAG) systems, stemming from the necessary practice of splitting large documents into smaller chunks for efficient processing. When documents are divided, individual chunks often lack sufficient contextual information, making it difficult for the retrieval system to identify and utilize relevant information effectively. For example,Continue reading “Addressing Context Loss in RAG Systems with Contextual Retrieval”

In my previous article, “Why Digital-First Companies Are Building Their Own AI Platforms”, I explored why many tech-forward companies are opting to build their own AI platforms rather than relying on off-the-shelf solutions. The piece sparked considerable interest, with readers eager to explore the specifics of these custom platforms. A common reaction was: now thatContinue reading “How Tech-Forward Organizations Build Custom AI Platforms: A Feature Breakdown”

In a previous article, I explored why many leading companies are building custom AI platforms, even when a range of off-the-shelf options exists. The reasons vary from seeking greater adaptability to establishing market differentiation through tailored AI capabilities. As more businesses push toward these bespoke platforms, scaling becomes a central challenge.  Few technologies match theContinue reading “Ray Summit 2024: Advancing AI Platforms and Applications”

I’ve written a couple of posts examining the significant challenges confronting startups in the AI landscape, particularly those involved in training foundation models like Large Language Models (LLMs). In one post, I highlighted Meta’s release of the world’s largest “open weights” foundation model—a notable development in an environment where OpenAI, another major player, is grapplingContinue reading “The Future of Analysts: Orchestrating AI for Strategic Insights”