Patterns that Connect: AI, Management, Metaverse, Quantum, Philosophy, and Physics

  Copyright: Sanjay Basu Preface As computational technology evolves, much of the recent attention has focused on Large Language Models (LLMs). These powerful systems excel at processing and generating human language, enabling remarkable advancements in natural language processing, conversational AI, and content creation. However, as businesses and institutions strive to handle ever-increasing volumes of data, there is a growing need to shift the spotlight from language-centric AI to solutions specifically designed for massive, complex data sets. Large Data Models (LDMs) address this demand by offering highly scalable architectures for processing, analyzing, and extracting actionable insights from disparate and often overwhelming data sources. By directing efforts toward LDMs, the computing world can harness more structured and efficient mechanisms for decision support, real-time analytics, and intelligent automation at a scale that was previously unthinkable. This shift recognizes...

  Copyright: Sanjay Basu Much has been written about DeepSeek R1, primarily focusing on the technology. This article is for C-suite executives exploring how to leverage artificial intelligence (AI) for competitive advantage. DeepSeek R1 represents a powerful new option. It’s an advanced large language model (LLM) that excels at complex reasoning— think solving tough math problems, generating high-quality code, and producing articulate, context-aware responses. Below, we break down the business-critical benefits of DeepSeek’s innovative training approach, why it stands apart from existing methodologies, and the bottom-line considerations around compute resources. What Makes DeepSeek Different? Multi-Stage Training Pipeline Most AI models rely on a straightforward sequence of supervised training (teaching the model correct answers) and sometimes reinforcement learning (letting the model “learn by doing”). DeepSeek, however, refines this process by weaving together supervised fine-...