Is it possible not to cheat on the Turing Test: Exploring the potential and challenges for true natural language 'understanding' by computers

Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. The area of natural language understanding in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity in how 'understanding' is used in this and other disciplines have made it difficult to discern how close we actually are. A comprehensive, interdisciplinary overview of current approaches and remaining challenges is yet to be carried out. Beyond linguistic knowledge, this requires considering our species-specific capabilities to categorize, memorize, label and communicate our (sufficiently similar) embodied and situated experiences. Moreover, gauging the practical constraints requires critically analyzing the technical capabilities of current models, as well as deeper philosophical reflection on theoretical possibilities and limitations. In this paper, I unite all of these perspectives -- the philosophical, cognitive-linguistic, and technical -- to unpack the challenges involved in reaching true (human-like) language understanding. By unpacking the theoretical assumptions inherent in current approaches, I hope to illustrate how far we actually are from achieving this goal, if indeed it is the goal.