Not cheating on the Turing Test: towards grounded language learning in Artificial Intelligence

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Stellenbosch : Stellenbosch University
AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsomming
ENGLISH ABSTRACT: In this thesis, I carry out a novel and interdisciplinary analysis into various complex factors involved in human natural-language acquisition, use and comprehension, aimed at uncovering some of the basic requirements for if we were to try and develop artificially intelligent(AI)agents with similar capacities. Inspired by a recent publication wherein I explored the complexities and challenges involved in enabling AI systems to deal with the grammatical(i.e. syntactic and morphological) irregularities and ambiguities inherent in natural language (Alberts, 2019), I turn my focus here towards appropriately inferring the content of symbols themselves—as ‘grounded’ in real-world percepts, actions, and situations. I first introduce the key theoretical problems I aim to address in theories of mind and language. For background, I discuss the co-development of AI and the controverted strands of computational theories of mind in cognitive science, and the grounding problem(or ‘internalist trap’) faced by them. I then describe the approach I take to address the grounding problem in the rest of the thesis. This proceeds in chapter I.To unpack and address the issue, I offer a critical analysis of the relevant theoretical literature in philosophy of mind, psychology, cognitive science and (cognitive) linguistics in chapter II. I first evaluate the major philosophical/psychological debates regarding the nature of concepts; theories regarding how concepts are acquired, used, and represented in the mind; and, on that basis, offer my own account of conceptual structure, grounded in current (cognitively plausible) connectionist theories of thought. To further explicate how such concepts are acquired and communicated,I evaluate the relevant embodied (e.g. cognitive, perceptive, sensor imotor, affective, etc.) factors involved in grounded human (social) cognition, drawing from current scientific research in the areas of4E Cognition and social cognition. On that basis, I turn my focus specifically towards grounded theories of language, drawing from the cognitive linguistics programme that aims to develop a naturalised, cognitively plausible understanding of human concept/language acquisition and use. I conclude the chapter with a summary wherein I integrate my findings from these various disciplines, presenting a general theoretical basis upon which to evaluate more practical considerations for its implementation in AI—the topic of the following chapter.In chapter III, I offer an overview of the different major approaches(and their integrations)in the area of Natural Language Understanding in AI, evaluating their respective strengths and shortcomings in terms of specific models. I then offer a critical summary wherein I contrast and contextualise the different approaches in terms of the more fundamental theoretical convictions they seem to reflect. On that basis,in the final chapter, Ire-evaluate the aforementioned grounding problem and the different ways in which it has been interpreted in different (theoretical and practical) disciplines, distinguishing between a stronger and weaker reading. I then present arguments for why implementing the stronger version in AI seems, both practically and theoretically, problematic. Instead, drawing from the theoretical insights I gathered, I consider some of the key requirements for ‘grounding’ (in the weaker sense) as much as possible of natural language use with robotic AI agents, including implementational constraints that might need to be put in place to achieve this. Finally, I evaluate some of the key challenges that may be involved, if indeed the aim were to meet all the requirements specified.
Thesis (MA)--Stellenbosch University, 2020.
Philosophy of Mind, Philosophy of Language, Cognitive Linguistics, Natural Language Processing, 4E Cognition, Philosophy of Artificial Intelligence, The Symbol Grounding Problem, UCTD, Turing test, Computer security, Machine learning, Languages, Artificial, Cheating