|In Memoriam – Glyn Salton-Cox
The English Department is devastated to announce the death over the New Year of our colleague Glyn Salton-Cox. To his family, loved ones, and friends here, in his native Britain, and throughout the world, we offer our deepest and most heartfelt condolences. Glyn was a brilliant scholar, a very popular teacher, and the kindest of colleagues.
The Department of English invites you to a commemoration of our colleague Glyn Salton-Cox on Friday, March 3d, 2023.
We will gather in the Faculty Club’s Betty Elings Wells Pavilion at 3:00 pm and then move to the Terrace at 4:00 pm for a reception. Please let us know of any accessibility requests.
Machine Reading & Writing
- Course Number: ENGL 146MR
Check on GOLD.
- Advisory Enrollment Information:
May be repeated for credit provided the letter designations are different.
- Catalog Course Entry: ENGL 146AA-ZZ
- Quarter: Winter 2021
When I first taught this class, in Fall 2016, machine writing was just starting to generate widespread attention, and in the run up to the election that year we had more than one occasion to watch the drama of the Twitter bots unfold in real time. Now the notion that algorithmically generated content will be—indeed has been—radically transformative will perhaps be unsurprising. It is no longer spectacular news when a novel written by a human in collaboration with a neural network wins a prize; autocompose, autocorrect, and autoreply features can be found on every platform; chatbots are so ubiquitous as to be often unseen; and “automated reporting” bots have expanded well beyond earthquakes, homicides, and sporting events. (In fact, can you be entirely sure that a human has written this course description?) So too machine reading—as a practice that encompasses everything from keyword search in a document to QR codes and facial recognition—is so common as to seem ordinary. Researchers now mine large textual corpora for grammatical and semantic patterns, quantifying such matters as word usage, punctuation, and character relationships (see also: forensic stylistics and copyright infringement detection). Artists experiment with machine reading as well, as in Ben Fry’s Valence reading Mark Twain’s The Innocents Abroad; Daniel C. Howe and John Cayley’s Readers Project; or Shakespeare Machine, a data analysis and visualization of Shakespeare’s plays.
While the technologies of machine reading and writing have quickly advanced, however, our critical understanding of them has not. In what sense, and with what consequences, have they been transformative? How should a discipline devoted to reading and writing respond to the outsourcing of these activities to algorithmic systems? What do large language models, machine readers, text generators, literary automata, and ‘robot novelists’ mean for literature, and textual analysis? How should we think now about some of the foundational concepts for literary studies, e.g. authorship, style, and voice? How do we know who or what is writing, and does it matter? What new reading practices have emerged, or do we need now? What is at stake in the rise of machine readers and writers—aesthetically, socially, and politically?