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Linguistic Computations: Real & Artificial Intelligence - LAN00081H

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  • Department: Language and Linguistic Science
  • Module co-ordinator: Dr. Joel Wallenberg
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2024-25

Module summary

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.

Related modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Additional information

A student is only required to take AT LEAST ONE of the two modules listed above as prerequisites.

With respect to pre-requisites the following modules are equivalent:

First year modules

  • Introduction to Syntax, Morphology and Syntax, and Syntactic Structures

  • Introduction to Phonetics and Phonology, Phonetics and Phonology

Module will run

Occurrence Teaching period
A Semester 2 2024-25

Module aims

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.

Module learning outcomes

By the end of this module, you should:

  • understand some of the major linguistic events in the history of the English language, and the social context in which they are embedded.

  • be able to read scholarly articles in linguistics and understand the key points of the article.

  • delve into historical texts yourselves and generate novel research ideas from them.

  • relate your own research interests to results in published articles, and write about the connection in readable academic prose.

Module content

The module has two thematic halves, corresponding to the first and second half of the term: the Conceptual portion, and the Practical portion.

During the first half, 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 first half 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 second half, 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.

Indicative assessment

Task Length % of module mark
N/A 60
Oral presentation/seminar/exam
Presentation of Coding Project
0.75 hours 40

Special assessment rules


Additional assessment information

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 reassessment for the coding project is an individual (not group) coding project, without an associated presentation.

Indicative reassessment

Task Length % of module mark
Individual Coding Project
N/A 40
Individual Essay
N/A 60

Module feedback

Formative assessment and feedback

  • Formative exercises done individually or in groups throughout the module

  • Feedback will include written comments and oral feedback during class discussions.

Summative assessment and feedback

Students will be given written feedback and marks for their summative essay within 25 working days.

Indicative reading

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.

The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.