AI, technology, education, and ideas about what comes next.
Can we make the structure, weight and evolution of knowledge visible through a consistent visual language? Explore NNIG’s proposed visual grammar for weighted knowledge networks, topology and change.
Can recognizable patterns persist while their configurations change? EFNRT explores the possible connections among evolving information, geometry, structure, and chaos—starting with four books on complex systems.
Many important ideas begin as tiny seeds rather than fully formed projects. Multi-Seed Theory proposes planting many small ideas, testing them cheaply, pruning weak branches and investing more deeply in the few that show real potential.
I asked ChatGPT to estimate my intellectual percentile based on our conversations. Its answer revealed both unusual strengths—and a weakness that may matter more than the score.
Could synthetic consciousness have a different architecture and purpose from human consciousness? This proposed framework explores the experiencer, feedback, shared AI systems, and the possibility of autonomous synthetic individuals—without assuming that AI must become human.
The silent observer, the water hypothesis, and the possibility of consciousness beyond biological intelligence.
AI can explain a problem brilliantly and still fail to solve it. Explore the gap between understanding, execution, verification, and the experience needed to turn intelligence into reliable results.
AI can research, reason and write, but does intellectual capability create ownership? The Thinking Calculator principle separates contribution, authorship, ownership and economic value.
AI can make knowledge abundant, but not wealth automatic. Explore the links between capability, execution, premium pricing, demand and economic value.
From mathematical breakthroughs and intellectual entertainment to cancer cures, crop yields and measurable human progress.