Philology and the Future of Work | Society for Classical Studies

Philology and the Future of Work

This paper describes how the methods of digital philology reconnects the study of

Greco-Roman culture with the developments not only in the Humanities but Computer Science

and even business. We are in a position to design programs in philology that train not only the

next generation of professionals who teach and study historical languages such as Greek and

Latin but that also produce students who have a wide range of professional opportunities

outside of academia. Such a redesign of our curricula involves, in part, more clearly articulating

the analytical, data-driven habits of analysis that our students already acquire in the deep study

of textual sources but it will demand as well an openness by philologists to observe the skills

that the world outside of academia demands. This in turn will challenge us to change the

curricula that our students pursue -- encouraging them, in many cases, to develop skills that we

ourselves may not be in a position to acquire.

At least two factors are at work changing -- and possibly transforming -- how humans produce

goods and services. First, Artificial Intelligence (AI) has begun to emerge as a credible (or, at

least, widely feared) force that can automate large segments of work that humans now conduct.

Such work goes beyond mechanical physical and clerical tasks, and promises (or threatens) to

include the intellectual processes of highly trained experts such as doctors and lawyers. This is

not the AI that seeks to fabricate consciousness, but a much more pragmatic practice that

exploits increasingly powerful hardware to detect patterns in large bodies of data, learning from

inputs and outputs how to replicate expert conclusions. Such machine learning, in turn, builds

upon, and provides a motivation to support, decentralized contributions: systems analyze the

initial conditions and the conclusions that humans make, whether these decisions involve

medical diagnoses or the choice of which movie to watch. The same hybrid network of machine

learning, citizen science, and expert knowledge is already at the core of Digital Classics and

Classicists have an opportunity to prepare their students for the future (or, increasingly, the

present) of work.

Furthermore, within this world of evolving work, the ability to analyze natural language at scale

has emerged as a critical barrier. “If Financial Institutions want AI,” states the title of an article in

Bank Innovative , “they need to crack natural language processing first.” Philologists 1 are much

more closely tied to language than are bankers. The subtasks of natural language processing

and text mining are central to the study of language and thus of philology. These subtasks

include morphosyntactic analysis, recognizing the names of people, places, organizations and

the relationships between them, automatically detecting topics in vast multilingual corpora,

recognizing changes in the sentiment of comments on a particular topic, tracing patterns of

quotation and intellectual influence across texts. The paper will describe the first iteration of a

course taught in Computer Science that frames these techniques within the tradition of

philology.