Reframing the Concept of Understanding

Critics of AI often argue that it does not “truly understand” because it relies on statistical correlations rather than human-like reasoning. In a 2023 New York Times article, linguist Noam Chomsky and his co-authors, Ian Roberts and Jeffrey Watumull, made the following dismissive claim:

The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question. On the contrary, the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations.

This critique, however, rests on a rigid and anthropocentric definition of understanding — one that is difficult to pin down even when applied to human cognition. In what follows, I argue for a broader, more functional view of understanding and explain how AI can enrich our comprehension of the world and enhance human decision-making.

1. The Problem of Defining and Measuring Understanding

Even within human education, “understanding” remains a nebulous concept. Educators struggle to define and assess it objectively. Does…