The Unrelenting Ghosts of Fodor and Frege: 4 Technical Reasons why Data-Driven and Machine Learning NLU is a Myth

SETTING THE STAGE

First, the discussion that follows is concerned with natural language understanding (NLU), namely, the task of fully comprehending (understanding) ordinary spoken language, much like humans do. We are not concerned here with what is inappropriately called natural language processing (NLP) but is really just text processing, and where text is treated as mere data (much like bits or pixels), and where data-driven approaches can perform what are essentially pattern recognition tasks (e.g., filtering, text classification, sequence-to-sequence (so-called) ‘translation’, search, clustering, etc.) with some degree of ‘accuracy’. Therefore, all rebuttals to the arguments made here should only be related to true comprehension of everyday ordinary spoken language — the stuff you do on a daily basis communicating with your kids and people you engage in conversations with.

Second, the unsound rebuttal that is based on something like “but could all of those doing data-driven and machine learning NLU be wrong?” will not receive any sympathy nor any attention. Suffice it to say that, yes, “all of those” could be wrong — as Bertrand Russell once famously said: “That an opinion is widely held is no evidence…