Why do machines learn, at all? (fohlen.dev)

The discussion revolves around the underlying reasons why machines learn, emphasizing a gap in existing literature that often addresses how to apply machine learning techniques without clarifying the fundamental “why.” The author highlights the significance of Statistical Learning Theory (SLT), which seeks to explain the optimal selection of functions based on empirical data distributions. It suggests that learning in machines is driven by the need for a generator of random data and a supervisor that dictates output, necessitating an inductive bias to bridge the gap between the true data-generating process (G) and machine output (S). This exploration is crucial for the AI/ML community as it shifts the focus from simply implementing machine learning algorithms to understanding the principles that govern their effectiveness. By clarifying that the best models are those with minimal bias and a strong resemblance to the underlying distributions, the author calls for a deeper examination of feature engineering and the inductive processes that shape learning outcomes. This insight not only enhances theoretical foundations but also encourages practical advancements in model development and application, underscoring that the data characteristics play a vital role in achieving reliable and accurate predictions.