Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence

TL;DR

This paper critically examines current natural language understanding in AI, integrating interdisciplinary insights to identify foundational requirements and challenges for developing systems with human-like language comprehension.

Contribution

It offers an interdisciplinary analysis of NLU, clarifies conceptual issues, and evaluates current approaches against cognitive science insights.

Findings

Highlights lack of conceptual clarity in NLU

Identifies key requirements for human-like language understanding

Reviews limitations of current AI language models

Abstract

Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.