Articles
Deep dives into how the brain actually computes, honest critiques of modern AI, and updates from Optimizing Mind.
The Compute Bill Nobody Budgeted For
Why AI transfer learning is breaking the bank: edge-case data demands and the vicious cycle of retraining make even top-layer training deceptively costly. The fix is algorithmic, not more GPUs—Flash Transfer Learning steps off the exponential treadmill with 1% of the data and zero catastrophic forgetting.
When AI Sours: How Efficient Training Changes the ROI Math
80–95% of enterprise AI projects fail to deliver measurable returns, and the hidden cost is not building the model—it is keeping it current. Why data balancing and casino-style rehearsal sour AI ROI, and how self-normalizing Flash Transfer Learning fixes the last mile.
The Lost Science of Cybernetics: Why AI Took a Wrong Turn in 1948 (And How We Fix It)
Before 1948, the science of the brain was cybernetics: feedback loops, homeostasis, and self-regulation. Then AI committed to passive, filter-based networks. Why that was a wrong turn, and how thermostatic regulatory feedback networks fix it—less data, sequential learning, and no catastrophic forgetting.
Rethinking How AI Brains Are Built: Welcome to Optimizing Mind
Why today’s filter-based deep learning is a structural bottleneck, the multidisciplinary path from engineering through neuroscience and medicine that led to reverse-engineering the brain’s computation, and why Optimizing Mind is building AI on thermostatic regulatory feedback.