Studying at York
Linguistic Computations: Real and Artificial Intelligence - LAN00093M
- Department: Language and Linguistic Science
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
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.
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 the module:
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You should be able to identify a number of specific aspects of linguistic theory which derive from a computational view of language and cognition. You will also be able to translate simple text-processing tasks into algorithms, and transform them into Python programmes. Students will also be able to critique the use of "Artificial Intelligence" in today's society.
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As a Masters-level module, you should be able to further identify the broad research areas that a computational approach can be applied to, as well as the strengths and limitations of using such an approach. In particular, you should be able to identify syntactic and phonological processes that can or cannot be described by finite-state automata (regular grammars), pushdown automata (context-free grammars), or more context-sensitive computing machines.
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In addition, I hope you will achieve a more advanced outcome, and become able to integrate concepts from computational theory, information theory, probabilistic reasoning, and general cognitive science into your work for other modules. You may also implement some of these ideas in your coding projects, and see how Python can help you in other work.
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Finally, an extension outcome you might strive for: you will learn the basic logic and mathematics behind computational theory, probability theory, information theory, and Bayesian reasoning well enough in order to implement them in your coding projects and in future work.
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
None
Special assessment rules
None
Indicative reassessment
None
Module feedback
Formative assessment and feedback:
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Formative exercises done individually or in groups throughout the module
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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 work within the University mandated schedule.
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.