Working on Geneosophy, which deals with comprehension and enables the formalization and verification of concepts such as life and intelligence.
The story of artificial intelligence is not just one of increasing computational power—it’s also a story about how machines relate to meaning. Or more precisely, how meaning has been progressively sidelined in favor of statistical pattern manipulation. To understand this, we can trace AI’s evolution through a
We live trapped in a circle of our own making. We reason with concepts about objects and other concepts. We understand objects through concepts. We create new concepts by combining existing objects and concepts. Around and around we go, never stepping outside this conceptual machinery to ask the most fundamental
We live in an age of breathtaking technical sophistication and profound existential confusion. We can manipulate matter at the atomic level but can't agree on what makes life worth living. We've mapped the human genome but struggle to understand human nature. We've built global
We live in a Kafkaesque paradox: the very people we turn to for wisdom about life’s biggest questions—meaning, society, the future of humanity—are systematically selected for their inability to think about such things. Consider this absurd pattern: A tech entrepreneur optimizes engagement algorithms for maximum user addiction,
What if I told you that the deepest mysteries plaguing human knowledge—consciousness emerging from matter, the mind-body problem, infinite regress, self-reference paradoxes—aren't profound features of reality waiting to be solved, but artifacts of a particular way of asking questions? Every academic discipline, from quantum
Something interesting hides in plain sight beneath our modern academic landscape. Physics, mathematics, biology, psychology, philosophy, these impressive disciplines that organize our understanding of the world, all rest upon a foundation they can no longer see. Like a great tree whose roots have been forgotten, our specialized fields of knowledge
Marcus Doubtful, a physicist turned philosopher. Richard Trad, a mathematician turned philosopher. Both have spent decades thinking about the foundations of knowledge, but they've reached very different conclusions. Richard: Marcus, I've been reading your recent work on disciplinary self-reference. While I find your historical analysis
I’m not questioning the usefulness of LLMs and AI in general. What I am questioning is its ability to achieve intelligence. Large Language Models (LLMs) are remarkable tools—tools to augment one’s intelligence—just as a search engine, or Wikipedia have done before them. I use them daily.
We believe we are at the cutting edge of history, but we may just be repeating its oldest mistakes. From the Sophists’ empty rhetoric to the Alchemists’ blind experimentation, humanity has a long history of mastering technique without possessing understanding. This dialogue explores how today’s AI-driven world is
If a machine can mimic a thought, does it mean it is thinking? This dialogue strips away the hype of modern AI to reveal the "Code That Could Never Think." By examining the structural limitations of computation, we explore the difference between a system that follows rules and
A debate between two scientists about whether traditional science can truly capture the concept of life.
Every year, thousands of bright minds enter neuroscience programs driven by a profound question: How does the brain work? They emerge years later with advanced degrees, sophisticated experimental techniques, and an uncomfortable realization—despite accumulating vast amounts of data about neural mechanisms, the fundamental mystery remains unsolved. We can map