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Selling Dreams in the Age of Speculative Technology

In today’s financial markets, a troubling asymmetry has emerged between those who sell technological dreams and those who buy them. Founders, CEOs, and early investors often understand how distant true commercialization remains, while many young retail investors—driven by optimism and headlines—see only the promise, not the timeline. This imbalance creates a structural divide: the informed side monetizes expectations, while the uninformed side absorbs the losses. Quantum computing offers a striking example. The 2025 Nobel Prize in Physics was awarded to John Clarke, Michel H. Devoret, and John M. Martinis “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” Their work revealed that quantum effects—once confined to the microscopic world—can emerge in circuits large enough to see, laying the foundation for today’s superconducting qubits. Quantum systems promise radically new ways of computation through superposition, entangleme...
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AI-Supervised Home Palliative Care: A Comfort-First and Cost-Effective Alternative to Hospital-Based End-of-Life Care

This blog is based on my personal experience caring for my palliative mother, who at that time was expected to live less than two months. I am not a doctor. It reflects on how end-of-life care often brings unnecessary discomfort to patients, even when death is near. Palliative care should focus on comfort, dignity, and relief from symptoms—not on prolonging life through medical intervention. Yet, hospital routines designed for safety can easily go too far. Nurses and doctors are required to follow strict protocols that call for frequent vital checks, blood tests, and continuous monitoring. Much of this comes from fear of legal responsibility rather than medical need. As a result, even patients in their final hours are often subjected to repeated procedures that offer no benefit but cause distress. Many remain connected to machines until their last moments. Families watch their loved ones in pain, realizing that such interventions contradict the essence of palliative care. The system ne...
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Beyond the Comfort Zone: Rethinking Higher Education in the Age of AI

This piece offers a personal reflection on the relevance of today’s university system in a world where AI increasingly shapes how knowledge is delivered and how research is conducted. Rather than revisiting familiar debates, the focus here is on less-discussed inefficiencies that have gradually taken root within higher education. A helpful parallel can be found in the world of Go (baduk). Before AlphaGo, Go education relied on apprenticeship: students trained under veteran instructors who refined their technique and supported them through setbacks. This changed dramatically after AlphaGo’s 2016 victory over Lee Sedol. AI tools such as KataGo and Leela Zero now guide much of young players’ learning, offering strategies that challenge and often surpass long-held conventions. One Korean prodigy reportedly trained almost exclusively with AI for a year, developing an unconventional style that quickly carried him to the top. Human mentors still matter, but their role has shifted toward inter...
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Physics-Informed Neural Networks: Fundamental Limitations and Conditional Usefulness

!!!Needs revision!!! Physics-Informed Neural Networks (PINNs) aim to approximate the solution \( u \) of a differential equation defined over spatial coordinates \( x \in \mathbb{R}^d \) (e.g., \( x = (x_1, x_2, x_3) \)), and, when applicable,  time \( t \), by representing \( u \) with a neural network \( u_\theta \), where \( \theta \) denotes the trainable weights and biases. Training involves minimizing a composite loss function  $\mathcal{L}(\theta) = \mathcal{L}_{\text{PDE}} + \mathcal{L}_{\text{BC}} + \mathcal{L}_{\text{IC}} + \mathcal{L}_{\text{data}},$ which enforces the governing PDE, boundary conditions, initial conditions, and any available observational data.   However, PINNs minimize the PDE residual only indirectly—by adjusting the neural network parameters rather than manipulating solution components or their derivatives in a controlled, explicit manner. This leads to several fundamental inefficiencies. Since the solution is represented by a neural n...
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Biased Warnings: Examining the Risks of Unverified AI Speculation

The motivation for this blog stems from a recent article about Geoffrey Hinton, a recipient of the Nobel Prize in Physics and a renowned figure in artificial intelligence, who once again issued an alarmist warning about AI. According to reports from foreign media outlets, including the British daily The Guardian on December 27, 2024, Hinton appeared on BBC Radio, stating, "There is a possibility that humanity will go extinct within 30 years." He estimated a 10–20% chance that AI could destroy humanity within the next three decades and predicted that powerful AI, surpassing human capabilities, could emerge within 20 years and potentially gain control over humanity. A similar pattern was observed with the late Stephen Hawking, a celebrated physicist known for his work on black holes and the Big Bang theory, who also issued extreme warnings about AI without providing sufficient evidence. While Hinton’s groundbreaking academic contributions to AI are undisputed, his consistently...
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Mathematics as the Invisible Architect: Bridging Natural Phenomena and Practical Applications

Mathematics: The Invisible Driver of Civilization Mathematics, alongside philosophy, has systematically shaped human cognitive abilities, driving the progress of human civilization. Over history, it has evolved to address societal needs while simultaneously advancing as an "invisible culture" through individual and collective intellectual efforts. Fundamental concepts such as distance and space have been refined with mathematical tools, enabling simplified representations of complex phenomena and fostering systematic understanding. Mathematical tools, including Maxwell's equations for electromagnetism, the Navier-Stokes equations for fluid dynamics, elasticity equations for material properties, and the heat conduction equation, are grounded in conservation laws. They describe relationships between physical quantities over time and space and have broad applications, such as fingerprint recognition, voice analysis, data compression, medical imaging, cryptography, animation,...
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Advantages and Limitations of Deep Networks as Local Interpolators, Not Global Approximators

This blog addresses a common misconception in the mathematics community: the belief that deep networks can serve as global approximators of a target function across the entire input domain. I write this post to emphasize the importance of understanding the limitations of deep networks' global approximation capabilities, rather than blindly accepting such claims, and to highlight how their strengths as local interpolators can be effectively leveraged. To clarify, deep networks are fundamentally limited in their ability to learn most globally defined mathematical transforms, such as the Fourier transform, Radon transform, and Laplace transform , particularly in high-dimensional settings. (I am aware of papers claiming that deep networks can learn the Fourier transform, but these are limited to low-dimensional cases with small pixel counts.) The misconception often stems from the influence of the Barron space framework, which provides a theoretical basis for function approximation. Wh...
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