Natural Language and Artificial Intelligence

Can Artificial Intelligence truly imitate natural language without any flaws?

Delivered by the most outstanding academics in the UK and beyond, the British Academy’s flagship Lecture programme showcases the very best scholarship in the humanities and social sciences. This event is part of the Anna Morpurgo Davies Lectures series.

Large Language Models have shown remarkable abilities in natural language processing, tempting many to speak of them as if they used and understood language as humans do. However, doing so overlooks the distinction between the structural systems that support meaning and reasoning and the mechanisms for predicting what will come next in a text on the basis of similar passages in the vast amount of training data that LLMs encode. LLMs excel at prediction, and it is surprising how much can be done by memorization indexed by similarity alone. LLMs can answer abstruse questions, generate text of astonishing fluency on any subject in any style, and generate workable computer code in this way.

However, the limitations of LLMs are becoming increasingly clear. They struggle with sound logical inference, they may include convincing yet wholly inaccurate information, and they have difficulty in generalizing code beyond superficial similarity to examples they have encountered during training. This lecture will present recent research that highlights both the capabilities and the constraints of these systems. Its conclusion will be that the future of natural language processing lies in hybrid approaches that combine the precision and structure of symbolic reasoning with the power of recall and access by similarity of content of neural computation.

Speaker:

Professor Mark Steedman FBA, University of Edinburgh

Steedman's research in natural language processing (NLP) and Artificial Intelligence (AI) proceeds from the conviction that human language and cognition are inherently computational, and lies at the interdisciplinary interface of computer science, linguistics, and theoretical psychology. His research interests include: robust wide-coverage statistical semantic parsing; combined logical and distributional semantics for inference in open-domain question answering; temporal semantics; the structure and meaning of intonation in speech; and formal theory of grammar. He has pioneered the application of NLP methods to the analysis of music, and the use of AI models in understanding their common evolutionary origin in action-planning.His most widely recognised invention is Combinatory Categorial Grammar (CCG), a computationally practical theory of natural language grammar and processing (Steedman 1985b, 1987a, 1996a, 2000a, 2012a). This work has been recognized in its linguistic aspect by a Fellowship of the British Academy, and in its applied aspect, by Fellowships of the American Association for Artificial Intelligence (AAAI), the Association for Computational Linguistics (ACL), and the Cognitive Science Society. In 2018, Steedman received the Lifetime Achievement Award of the ACL.

Chair:

Professor Shalom Lappin FBA, Kings College London

Event timing:

• 15:45-16:15 Registration
• 16:15-17:00 Professor Mark Steedman's Lecture
• 17:00-17:15 Audience Q&A
• 17:15-18:15 Drinks Reception

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