More than ever before, people today encounter a world which blurs the distinction between machines and humans. Every day social media and search engines harvest our data "intelligently". We are repeatedly told that machines are "intelligent", though the content of this "intelligence" is rarely discussed. The illusion of machine intelligence is fostered by the many machines which talk to us throughout every day. How similar is their language to ours, and what is the "thinking" behind it? This is not a simple question, particularly since modern linguistics and cognitive science often understand human cognition through the "Computational Theory of Mind". This module explores the profound implications of this view for linguistics in general, the recent and distant history of English, and our current socioeconomic reality.
Introduction to Syntax (LAN00011C)
Introduction to Phonetics and Phonology (LAN00009C)
|Spring Term 2022-23 to Summer Term 2022-23
This module introduces the Computational Theory of Mind, a key idea underlying much of linguistic theory. Students will learn how its foundational concepts inform the study of language, including the history of English and language variation. Students will explore the usefulness and limitations of computation as a device for understanding the human language faculty. Finally, we explore socioeconomic and ethical issues surrounding modern "AI", and linguistics' role in them. In addition, students will gain practical coding skills in Python, which they will find useful in their other work and as a highly lucrative transferable skill upon graduation.
By the end of the module:
The module has two thematic halves, corresponding to the Spring and Summer terms: the Conceptual portion, and the Practical portion.
During the Spring term, each "lecture" period will actually be a Lecture/Dialogue consist of two parts: (1) the module leader presenting a topic related to the readings, in depth; (2) a study group, dialoguing with the module leader on a Dialogue Question.
Seminars during the Spring term will will also consist of two parts: (1) an exercise based on the current week's lecture/dialogue material; and (2) work in study groups, preparing for next week's lecture/dialogue and Dialogue Question
During the Summer Term, the lecture period will be devoted to coding instruction, coding exercises, and your coding project. There will also be Practical Workshop periods for you to work on your coding project in your study groups, with one-on-one assistance from the module leader.
|% of module mark
Coding Project and Presentation
Note that the final assessment is group work, and each group will receive one mark for their project/presentation. In order to achieve a high combined mark, each group must present a working computer programme which includes some of the skills the module has covered, and all the members of the group must be able to explain components of the project. The combined mark for project+presentation ensures that all group members will contribute enough to the project to be able to discuss it with the module leader during the group presentation. The presentation will not be formal as such, but each group will run their programme in front of another group (which will help to test the programme), and each group will respond to some questions from the module leader about their programme.
The re-assessment for the essay is an essay, and the re-assessment for the coding project is an individual (not group) coding project, without an associated presentation.
|% of module mark
Individual Coding Project
Formative assessment and feedback:
Formative exercises done individually and in groups throughout the module.
Summative assessment and feedback:
Students will be given written feedback and marks for their essays and group coding projects and presentations.
These are not all required texts, though you will certainly encounter some readings from some of them. If you wish to start reading ahead of the module, I suggest you begin with Isac & Reiss (2013) below.
Feynman, Richard P. 1996. Feynman Lectures on Computation. Tony Hey and Robin W. Allen eds.
Cambridge, MA: Perseus.
Gallistel, Charles R., and Adam Philip King. 2011. Memory and the computational brain: Why cognitive
science will transform neuroscience. Vol. 6. John Wiley & Sons.
Isac, Daniela, and Charles Reiss. 2013. I-language: An introduction to linguistics as cognitive science.
Oxford University Press.
Kurt, Will. 2019. Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star
Wars, LEGO, and Rubber Ducks. No Starch Press.
von Neumann, John. 2012. The computer and the brain. Yale University Press. New introductions by
Patricia S. Churchland and Paul M. Churchland, and by Ray Kurzweil.
The texts above will be liberally supplemented with bespoke, textbook-style readings that the module leader has prepared for this module.