Machine Learning Is Not Magic

The popular narrative around machine learning oscillates between utopian promise and dystopian threat. ML will cure cancer! ML will destroy jobs! ML will achieve consciousness! The reality is far more mundane and far more interesting: machine learning is a statistical technique for finding patterns in data.

That is not a dismissal — finding patterns in data is extraordinarily useful. ML powers recommendation engines, fraud detection, medical imaging analysis, and language translation. But understanding what ML actually does (and does not do) is essential for using it responsibly.

ML models learn correlations, not causation. They reflect the biases in their training data. They are brittle outside their training distribution. They cannot explain their reasoning. These limitations are not bugs — they are fundamental characteristics of the approach. Pretending otherwise leads to disappointed expectations and real-world harm.