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

Where Are We Now, and Where Are We Going? As 2024 draws to a close, the “Metaverse” stands as one of those tech visions that, like flying cars or jetpacks, often feels like it's perennially "just around the corner." But in this moment of reflection, we see the outlines of something less like sci-fi fantasy and more like…well, a digital neighborhood—a place where virtual reality (VR), augmented reality (AR), and our physical world coalesce into a shared digital landscape that’s both fantastic and strangely relatable. The concept of the Metaverse, like many disruptive ideas, has been met with both hype and skepticism. But in 2024, it’s fair to say we’re no longer just dreaming about virtual escapism or crypto-fueled economies. Today’s Metaverse vision is coalescing around shared digital spaces, enhanced productivity, and immersive social interactions. Let's explore what some of the heavy hitters—Meta and NVIDIA—are up to in this ever-evolving space. Meta’s Metaverse Str...

  A review of his three latest books (2005 to 2023) Ah, Ray Kurzweil, the consummate oracle of our techno-future — our silicon Nostradamus, if you will. There’s something magnetic about his audaciously optimistic vision of where technology is headed and how it will shape not only our future but the nature of life itself. “The Singularity is Near,” “How to Create a Mind,” and “The Singularity is Nearer” are all entries in his evolving manifesto, charting the future through increasingly bold claims and confident proclamations. I finished reading his latest book with the revised timeline for the oncoming Singularity, probably two or three months back. This weekend, I quickly browsed through the main topics of his first two books. I read The Singularity almost twenty years ago and then How to Create a Mind in early 2013 or so. A refresher was needed. So, let’s dive into each one and see what Kurzweil’s crystal ball tells us — all while trying to do justice to the signature verbosity of...

  The Enduring Relevance of C in the Era of Large Language Models Back in October 2011, when we lost Dennis Ritchie, the world was a very different place. I remember that time clearly — we were all excited about the iPhone 4S, cloud computing was just starting to take off, and if you’d mentioned “large language models” to most developers, you’d have gotten blank stares. That same month we lost Steve Jobs. Later that month, I delivered a eulogy for DMR at our local C/C++ meetup. I have put the text at the end of this article. You can also find it in the archive —  https://www.sanjaysays.com/2011/10/ Now, sitting here in 2024, surrounded by AI breakthroughs and specialized hardware accelerators, I find myself thinking about how Ritchie’s creation — the C programming language — isn’t just surviving; it’s thriving at the very heart of these innovations. You know what’s fascinating? While all the AI headlines focus on Python code and fancy neural networks, C is quietly powering the...

  A very short piece Viruses have often been portrayed as some of the greatest threats to human civilization, and for good reason. Throughout history, viral outbreaks have brought devastation and profound societal changes. The recent COVID-19 pandemic is a stark reminder of the destructive potential of viruses. It claimed millions of lives globally, disrupted economies, and altered daily life in ways unimaginable before 2020. Beyond its immediate health impacts, COVID-19 highlighted the fragility of human systems in the face of infectious diseases, underscoring the critical need for robust healthcare infrastructure, rapid vaccine development, and international cooperation. The pandemic also showcased the incredible advancements in biotechnology and the human capacity for innovation. One of the key scientific breakthroughs that helped combat COVID-19 was the use of AlphaFold 2, an artificial intelligence system developed by DeepMind to predict protein structures with remarkable accu...

  This is written for my friends with no AIML background who are killing it in the stock market, especially with Tech Stocks. You know how everyone jokes that NVIDIA is the only company actually making money from AI? There’s a really interesting reason for that, and it’s all about these things called transformers (not the robot kind!). Think of transformers like super-efficient brains that get smarter the more data you feed them. Unlike older AI models that had limits, these ones just keep getting better with more information. Pretty cool, right? It’s a pretty sweet deal for NVIDIA — they basically hit the tech lottery by having exactly what everyone needed right when they needed it. And now they’re so far ahead, it’s super hard for anyone else to catch up! Now, here’s where NVIDIA got lucky — their graphics cards (GPUs) turned out to be perfect for running these transformer AIs. It’s like they accidentally built the perfect tool for the job before anyone knew what the job would be...