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

  Copyright: Sanjay Basu    Why Size Isn’t Everything in AI Small models matter — a lot. It’s easy to get dazzled by trillion-parameter giants that promise general intelligence, but I strongly believe the smaller 7-billion-parameter models like MPT-7B, the Llama family of 7B, Falcon 7B, and Mistral 7B are the real unsung heroes, especially when you’re dealing with multi-agent workflows. Why am I advocating for smaller models? Well, let’s talk practicality. On a single NVIDIA A100 40GB GPU — the workhorse we have at our fingertips — you can comfortably run inference for open-source models like Llama 2 7B, Mistral 7B, Phi-2, Falcon 7B, MPT 7B, and even some smaller instruction-tuned variants like FLAN-T5 (up to 3B parameters). Closed-source gems such as Claude 3 Haiku variants and early GPT models at 7B also fit neatly. With a bit of clever optimization (quantization at 4-bit or 8-bit), you can even squeeze in certain 13B models. Now, let’s consider fine-tuning. Full fine-t...

  Copyright: Sanjay Basu From Stammer to Stage There are two kinds of people in this world: those who, when faced with adversity, Forget Everything And Run , and those who Face Everything And Rise . I’d love to say I was always the latter, the valiant hero staring down fear with unwavering resolve, but that would be a lie. No, for most of my early life, I was an Olympic-level sprinter in the Forget Everything And Run category —especially when it came to speaking in public. For as long as I can remember, I have stammered. Not in the charming, Colin Firth-playing-King-George-VI sort of way. No, my stammer was the kind that turned simple classroom recitations into battlefields of humiliation, where every sentence felt like climbing a mountain with a boulder strapped to my tongue. School presentations? A nightmare. Group discussions? Avoided like a plague. The mere thought of being called on by a teacher sent a cold sweat down my spine. In college, things weren’t much better. I had...

  Copyright: Sanjay Basu Beyond Correlation Imagine you’re a detective trying to solve a mystery, piecing together clues and looking for patterns. You notice an interesting phenomenon: every time it rains, the streets are left glistening and wet. At first glance, it seems obvious to conclude that rain is the cause of these wet streets. Indeed, our everyday experiences lead us to believe that when it rains, the water falls from the sky, and the ground becomes wet as a direct result. However, let’s delve deeper into this idea of causality. Consider another scenario where ice cream sales spike during hot, sunny days, which also happen to coincide with a rise in the number of reported sunburns. On the surface, one might hastily conclude that eating ice cream leads to getting sunburned, as both phenomena appear to occur simultaneously. But logically, we know that indulging in a cold treat on a warm day does not actually cause harmful sun exposure. Instead, both of these events are likel...

  Copyright: Sanjay Basu While my personal journey through Buddhism, Atheism, and Vedanta might seem uniquely modern, the questioning of divine existence has deep roots in Western thought, even in epochs when such questioning could be mortally dangerous. The pre-modern Western world, often painted as uniformly pious, harbored rich veins of religious skepticism that prefigured many contemporary atheistic arguments. The ancient Greeks provided the first systematic framework for questioning divine existence. While figures like Epicurus didn’t explicitly deny the gods’ existence, his materialistic philosophy suggested a universe that operated without divine intervention. His concept of gods who exist but don’t interfere with human affairs created a philosophical space between outright atheism and conventional religious belief — not unlike how Vedanta creates space between pure materialism and traditional theism. The medieval period, despite its reputation for religious conformity, saw ...

  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...