Knowledge Alphabets H-Lab

The Knowledge Alphabets H-Lab is examining the problem of translation in natural and digital languages. We aim to redefine translation theory today in the light of new developments in artificial intelligence (AI), machine translation, biotranslation, aesthetic practices and forms of knowledge production that are translation-based, or that define translation in a particular way as epistemology, transference, methodology, and mode of interpretive cognition. We hope to achieve a better understanding of how translation works in AI, deep learning and predictive processing by focusing on the unit of translatability. We will investigate what a knowledge alphabet is today and how it is related (or not) to its particular medium, whether vowel, letter, script, alphanumeric cipher, algorithm, bitmap, pixel, meme, RNA molecule, semantic or syntactic linguistic function, transliterative icon, acoustic value, or meme.

Hands-on experiments in collaborative pedagogy, translation and intermedial practice, will allow us to enhance approaches to digitally accessible encyclopedic objects and allow faculty and students to think more deeply about how their training impacts the status of what the word “language” refers to in data science and biotechnological research. Foregrounded issues will include the instrumentalization of language as a medium of information transfer and transcoding (how it reconfigures the landscape of language and literature study); understanding the relationship (important in contemporary computer science in the field of machine translation) between natural and artificial languages; the way AI is influencing literary production (through programs like GPT-2 and Google Smart Compose); and the problem of what mathematicians and logicians sometimes call “indifferentism,” applied to cases where a term like “real numbers” would seem to refer to the same thing in math and in logic, but which in fact do not coincide. This we might think of as translation that is invisible yet hiding in plain sight.

In broadest terms, the H-Lab aims to define a professional growth-field at the disciplinary juncture of literature and media studies, humanities and computational sciences. Students and faculty in the humanities will obtain a better of sense of how machine translation, alpha-numeracy and deep learning operate and how they pertain to their future work both within and outside the academy. Those in computational disciplines will gain understanding of the complexities of translation as interpretation, code, natural language, morphing intelligence and biotechnological reproduction. Those in arts practice will gain experience in thinking about translation as a medium, as border checkpoint, and political cartography.

Lab Team

Emily Apter, faculty, Depts. of Comparative Literature and French
Aaron Doughty, graduate student, Dept. Media, Culture and Communication
Jeanne Etelain, graduate student, Dept. of French
Alexander R. Galloway, faculty, Dept. Media, Culture and Communication
Nicole Grimaldi, graduate student, Dept. of Comparative Literature
Nabil Hassein, graduate student, Dept. Media, Culture and Communication
Ivan Hofman, graduate student, Dept. of Comparative Literature
David Kanbergs, graduate student, Dept. of Middle Eastern and Islamic Studies
Nina Katchadourian, faculty, Gallatin
Sam Kellogg, graduate student, Dept. Media, Culture and Communication
Alexander Miller, graduate student, Dept. of Comparative Literature
Amanda Parmer, graduate student, Dept. Media, Culture and Communication
Caleb Salgado, graduate student, Dept. of French
Pierre Schwarzer, graduate student, Dept. of French
Meg Wiessner, graduate student, Dept. of Media, Culture and Communication
Yuanjun Song, graduate student, Dept. of Comparative Literature