Algorithmic Overfitting explores why learning systems—artificial and human—so often mistake noise for structure. Blending clear technical insight with reflective analysis, the book reveals how models latch onto the wrong patterns, why memorization masquerades as intelligence, and what real generalization actually requires. Through case studies, cognitive parallels, and a deep look at modern foundation models, it shows that failure isn’t just error—it’s a window into the hidden assumptions shaping every mind. This is a guide to understanding how systems learn, how they mislearn, and how we can build models—and habits of thought—that see the world more clearly.
