From Baby Steps to Machine Learning: The Mystery of Pattern Recognition
Observing his younger brother touching a hot stove and getting burned, the author draws a parallel to machine learning and pattern recognition. A baby's initial understanding of "hot" is built through experience, associating sensory inputs, similar to creating space embeddings in machine learning. As new experiences (like touching a radiator) arise, the baby updates its mental model, a Bayesian update adjusting its understanding of "hot." This highlights how both humans and machine learning rely on pattern recognition: compressing information, generalizing knowledge, and adapting to new evidence. However, humans can also over-find patterns (apophenia), seeing connections where none exist. The author concludes by emphasizing the importance of quiet reflection for fostering creativity and pattern formation.