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The Paradigm Shift in Computing

  I found my thoughts on a fundamental paradigm shift in how we compute: my handwritten scribbles on my Samsung Note while I was attending Jensen’s digital keynote during GTC 2022. I expanded it below. We are witnessing a fundamental paradigm shift in computing technology. This paradigm shift in computing is transforming from hardware and operating systems (OS) to application program interfaces (APIs) powered by large language models (LLM) in a world that embraces natural language programming. No Moore’s Law-driven improvement in processor speed or memory size will alter the fact that our current computing landscape is being turned upside down. A new era of computing has arrived — one where Large Language Models (LLMs) and API-based services usher in a new way to interact with and program computers, abstracting away the utilization of hardware and operating system stack. So far, so good. Computing has had a technological lineage that operated in a bottom-up fashion: we first start with

Ikigai — A Path to Purpose and Passion

  An AI Product Manager’s Perspective In the quest for meaning and fulfillment in one’s life, many turn to the concept of Ikigai, a Japanese philosophy that translates roughly to “a reason for being.” The term combines two Japanese words: “iki” (生き), meaning life, and “gai” (甲斐), denoting worth or value. Ikigai is the intersection of what you love, what you are good at, what the world needs, and what you can be paid for. This concept has gained international attention, offering a blueprint for aligning passion and livelihood in a harmonious manner. Ikigai originates from the Japanese island of Okinawa, which is famous for its high number of centenarians. Researchers believe that having an Ikigai contributes to the longevity of the Okinawans, suggesting a deep link between a purpose-driven life and overall well-being. The philosophy behind Ikigai is holistic, urging individuals to find joy in everyday activities while contributing positively to their community. The methodology of discov

Unleashing the Power of 5G and 6G for the XR Revolution with NVIDIA Omniverse and NVIDIA GPUs

  At the same time, extended reality (XR) technologies, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), will radically change how we interact with digital media content, the success of an immersive XR revolution crucially depends on advanced wireless networks with the performance, reliability and scalability required to support such applications. This is why the fifth-generation (5G) and the forthcoming sixth-generation (6G) cellular networks will bring a wide range of capabilities that are tailor-made to support XR applications. 5G Networks: The Foundation for XR Growth 5G (fifth-generation) networks, now emerging globally and with peak data rates as high as 20 Gbps, latencies as low as 1 millisecond, and the capability to support up to 1 million devices per square kilometer, provides the basic performance and scale necessary for XR applications. Aside from looking good on the balance sheet, its huge bandwidth promises high-resolution, 360-degree streami

Responsible AI: Part -3 of 3

  In this final part of the series, we’ll delve into tools and frameworks related to artificial intelligence (AI). We’ll explore Responsible AI use cases across different industries. Let’s begin! 🌟 Tools and Frameworks The following tools and frameworks can help organizations ensure that AI systems are fair, transparent, and accountable: IBM AI OpenScale  is an open source platform that provides transparency and accountability in AI systems. It helps monitor, understand, and manage the performance of AI models in production. AI Fairness 360 (AIF360) is an open source toolkit that provides a comprehensive set of metrics and algorithms to help identify and address bias in AI systems. What-If Tool  is an open source platform that allows you to interactively analyze and understand the behavior of ML models. Oracle Cloud Infrastructure (OCI)  Data Science is a fully managed platform that teams of data scientists can use to build, train, deploy, and manage ML models by using Python and open