Machine Reading & Writing

Course Number: ENGL 146MR
Prerequisites: Writing 2 or 50 or 109AA-ZZ or English 10 or upper-division standing
Advisory Enrollment Information: May be repeated for credit provided the letter designations are different.
Catalog Course Entry: ENGL 146AA-ZZ
Quarter: Spring 2019
Instructor: Raley, Rita
Day(s): TBD
Time: TBD
Location: TBD


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 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; 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 AI systems? What do machine readers, text generators, literary automata, and ‘robot novelists’ (more precisely, neural networks) 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?


// The course will be updated but the syllabus from Fall 2016 will give some indication of the material and questions we will discuss.


// No add codes will be distributed before the term begins. Those wishing to enroll but unable to do so should add their name to the wait list and plan to attend class during the first week.