The first casualties of AI were the coders. Software engineering made for easy benchmarks, since you can always tell whether a program runs or not, and there were untold programs posted to Github, just waiting to be harvested for model training. Entry-level engineers were among the first to be cut, as AI swept through the […]
The following is a summary of my dissertation project, written for a general audience. Consider this me dipping my toe into public writing. If you want to read or hear more, please be in touch! The new wave of AI — powered by Large Language Models — has a love affair with fiction. The models […]
I am Teddy Roland, a PhD candidate in English at the University of California, Santa Barbara, where I study contemporary American Literature and New Media. My research is characterized as “distant reading,” which uses computers and statistics for literary interpretation. Although the approach may appear strange at first, the questions I hope to answer are […]
A quick post to announce the public distribution of word embeddings trained from the Chicago Text Lab’s corpus of US novels. They will be hosted by this blog and can be downloaded from this link (download) or through the static Open Code & Data page. From the Chicago Text Lab’s description of the corpus, the […]
As a resource to future graduate students, I am sharing the reading list I compiled for my qualifying exam on Distant Reading. Below the list, you will find a user manual of sorts that explains the rationale for each of the selections. My goal for posting is by no means to assert an authoritative list, […]
In light of word embeddings’ recent popularity, I’ve been playing around with a version called Latent Semantic Analysis (LSA). Admittedly, LSA has fallen out of favor with the rise of neural embeddings like Word2Vec, but there are several virtues to LSA including decades of study by linguists and computer scientists. (For an introduction to LSA […]
The first day of Alan Liu’s Introduction to the Digital Humanities seminar opens with a provocation. At one end of the projection screen is the word DIGITAL and at the other HUMAN. Within the space they circumscribe, we organize and re-organize familiar terms from media studies: media, communication, information, and technology. What happens to these terms when they are […]
This post offers a brief reflection on the previous three on distant reading, topic modeling, and natural language processing. These were originally posted to the Digital Humanities at Berkeley blog. When I began writing a short series of blog posts for the Digital Humanities at Berkeley, the task had appeared straightforward: answer a few simple questions […]
This post originally appeared on the Digital Humanities at Berkeley blog. It is the second in what became an informal series. Images have been included in the body of this post, which we were unable to originally. For a brief reflection on the development of that project, see the more recent post, Reading Distant Readings. Computer reading can […]
This post originally appeared on the Digital Humanities at Berkeley blog. It is the second in what became an informal series. For a brief reflection on the development of that project, see the more recent post, Reading Distant Readings. One of the hardest questions we can pose to a computer is asking what a human-language text is about. […]