Units and Topics
Intellectual and Historical Foundations
History of Computers and Computation
Computing in Humanities Disciplines
History of Humanities Computing
Textuality and Discourse Fields (summary)
Theories of Description and Classification
Digitization and Sampling (summary)
Structured Data (summary)
Geographic Information Systems (summary)
Algorithms and Data Structures (summary)
Digital Design and Production
Material Conditions of the Digital (summary)
Game Design (summary)
Computational Models of Intelligence (summary)
Artificial Intelligence (summary)
Expert Systems (summary)
Natural Language Processing (summary)
Social and Cultural Issues in Digital Media
As most artifacts studied by humanists are textual, a basic introduction to theories of textuality seems essential. Students will trace the history of theories of textuality and practices of editing, laying a theoretical foundation for the unit on digitization and sampling. We will note the historical shift of emphasis from "work" to "text" and will consider current predictions that digital technologies might precipitate the reverse-a return to emphasis on the "work." Attention will also be paid to the structuralist distinction between form and expression with the hope of reinserting rhetoric as a central issue.
In this unit students will learn the practical skills necessary to digitize artifacts and the critical skills necessary to evaluate the process and products of digitization. We will introduce hardware and software used to digitize artifacts and the data standards used by various text, image and audio file formats. This introduction will focus students on the choices involved in digitization and sampling. What is gained and lost in the process? Is it possible to completely represent a material object in digital form? The exercises and subsequent reflection will prompt discussions of best practices in the field of text and image digitization.
Project: Students will digitize a collection of "difficult" objects for presentation on the WWW and for preservation purposes. We will discuss the tradeoffs between file size, file format, and resolution/color depth.
Project: Students will digitize a collection of texts with the goal of producing an accurate, computer-readable text file. They will test two methods: manual transcription and OCR. The exercise should prompt discussion on issues mentioned above and prepare students for the next unit on data structures. The collection might include:
A. A handwritten document and a photocopy of a handwritten document
B. A few pages of a printed text with illustrations and/or hand annotations
C. An illuminated manuscript page
D. A photocopy of 6 different business cards
E. A facsimile of the opening pages of an early edition of some significant work of literature
F. A page of a newspaper with multiple columns
G. A small collection of ephemeral texts like flyers, posters, pamphlets, and so on
Most current applications of digital technology rely heavily on structured information, yet the systems of and assumptions underlying structured data are seldom investigated. In this unit, students study these foundations, beginning with the history systems of classification. The intersections of classification systems, philosophy and mathematics will also be investigated. After studying the traditions of classification, we will ask students to reflect on their own activities: should humanists transform artifacts into data? If so, how should they structure that data?
Students will be completing hands-on research, and these activities will prompt additional questions. For instance, what is the appropriate resolution of data? In dealing with dates, how do you record with enough granularity to guarantee precision without producing an information overload that leads to little insight? The benefits of structured data are fully realized only when the appropriate resolution is chosen.
We will also consider the intersections of structured information and research methodologies. Humanities scholars embarking on digital humanities research often worry that in structuring their data they necessarily impose a particular methodology on their research. They worry that structured data will require that they pose problems that are quantifiable. We will ask students to consider this conundrum, asking how we might pose problems that are computable without being quantitative.
After considering structured data as an abstract concept we will investigate the two most common systems for structuring data: databases and markup.
Project: Working with some common, familiar artifact-family tree, recipe, newspaper article-students will consider different options for structuring their data. Most work will focus on databases and markup, but other structures might be introduced as well.
Considered as one possible way to structure information, we will teach students to identify situations in which a database is the most appropriate tool. Students will learn the basics of the technology-relational databases, object-oriented databases and SQL-but will also learn to apply a critical eye toward the technologies. Again we will look at the social and intellectual histories of the technology. Who developed these tools and why? What are the intersections of set theory and relational databases? Through the hands-on exercises, students will investigate the complexities involved in actually entering humanities data into a database.
One other tool for structuring data, markup technologies are most often used when one sees a hierarchy inherent in the data. There is already much scholarship detailing the advantages and disadvantages of markup. The use of markup in humanities scholarship as also prompted further reflection by bibliographers and textual critics on the natures of text-is it really hierarchical? Students will tie this debate to our previous discussions of textuality and ask the question: To what extent have theories of textuality informed theories and applications of markup?
The first task in this unit is to question the cultural authority of maps: we will learn to read maps as interpretations rather than facts. Maps are most often generated to evidence to support an argument. Consistent with other units in this course, we will teach students to generate maps that ask questions. We will investigate the coordinate systems that underlay maps. In addition we will investigate perceptions of space in GIS and cartography and, perhaps, Art History. We might challenge students to distinguish between space and place.
Project: Students will draw a map of a familiar place. This could be a well-known place, or it could "Charlottesville"--but it has to be a place they have experienced. Students will visit the map library in search of various types of maps of their place. They will digitize these maps using two techniques: simple image scanning/raster and vector/GIS digitization from image scans. The goal is to get students to experience maps as structured data plus visualization-or many possible visualizations: hand-drawn, print, scanned image and GIS. We will emphasize the choices and classifications required by each technique as well as the resulting capabilities.
Project: Ask students to map some "overdetermined" place: Jerusalem, Harlem, the Pacific. Look at the history of maps of the places. Research the coordinate systems that have delimited these spaces.
In short, students will learn how computers work. We will define basic concepts such as "formal" and "algorithm" and discuss the fundamental nature of "computation." Looking at the history of computing, we will talk about the computer as both a conceptual and a mechanical device, focusing on the points at which the conceptual and the mechanical converge and diverge. We hope to lead students to a discussion of the nature of computation. What is computable? Can a computer generate meaningful inferences?
Project: Model a computer on paper (maybe the Altair or Knuth's MIX).
Here again students will learn both practical and analytical skills. We will teach basic object-oriented programming within a MOO environment. Through the programming exercises, students will learn basic principles such as encapsulation, composition, inheritance, and information hiding. We will also investigate the various social and institutional histories of programming languages. Students should also be able to place programming languages within a history of formal logic and analytical philosophy.
Project: Students will research the history of a programming language, operating system or Web standard.
Project: Students will learn basic programming through building and populating a shared space in a MOO environment.
In this unit we will treat digital art and research projects as cultural artifacts. Asking students to "read" these artifacts as artistic creations, we will discuss the technologies of electronic publishing and the material constraints they impose on production. In addition we will look at institutional constraints imposed by departments, libraries and other communities of digital production.
In our unit on interface we want students to understand the importance of interface within a broader history of computing and to grasp the challenges associated with designing effective and aesthetically appealing interfaces. We also want to broaden the sense of interface, remembering that we are surrounding by interfaces: books, dashboards, ATM machines, stovetops. In the knowledge representation seminar, we will focus on the information architecture side of interface design. How does one organize information in digital environments? How does one balance concerns for audience-ease of use-with concerns for aesthetics-interface as art? How does the metaphor of the "page" affect design in digital environments?
Since many of our students might one day build research archives, we will also evaluate current efforts to build information architectures and interfaces for projects at institutions such as IATH. In this more focused study we will ask students to consider what is involved in making an interface that acts as a provocation. How does one design an interface that will allow users to find what they're looking for AND to discover what they didn't even know existed?
Project: Radically redesign the computer interface. At one time computers interfaces looked much more like dashboards or instrument panels in airplanes. This seems rather foreign to us now. Make the interface foreign again.
Project: Document common interfaces. Pay particular attention to the balance between audience and aesthetics. For whom was the interface designed? Was it intended to be aesthetically pleasing? Is there evidence of other design constraints?
We will begin by exploring the distinctions between visualization and image. How does one read a visualization? We might query scholarship about iconography and emblems for a starting point. Do we have a grammar or an aesthetics for visualizations? Humanists rely on visualization algorithms from the sciences and the social sciences. Are these appropriate for our work? We will propose possible models of transformations, possible algorithms for generating visualizations from humanities data. Here we will also talk about notions of pattern. How are patterns produced? How do patterns mean?
Project: Ask students to generate a visual provocation, visualization (or an image of a visualization) that will help a researcher ask a question about an artifact.
Students will research game design and game criticism and its possible relations to knowledge representation. We will look at theories of agency and information in formal game theory and discuss their applications to research in humanities and in digital design. Current research in pattern languages and their application to game design will be reviewed with an eye toward establishing the connections between pattern languages and knowledge representation. Games also offer an opportunity to talk about the computer as a medium. How have digital technologies affected the experience of gaming? Finally we will talk about new investigations into the relations between gaming and hermeneutics. Is gaming a viable methodology?
Project: Ask students to design a pattern language.
Project: Ask students to generate a game design document.
Project: Articulate or translate a game from one medium to another.
The central question for this section is: "What is intelligence?" To answer this question we will review historical concepts of mind and intelligence, concepts that developed well before attempts to recreate intelligence in a machine. With this background we will explore classic debates in the field of artificial intelligence with an eye towards placing them in the context of ongoing debates in the humanities. From here we will investigate more contemporary research in the field, including proposals that artificial intelligence and computational modeling of human intelligence might be separated into distinct fields. Finally we will spend some time on natural language processing and expert systems, two partially successful implementations of computational models of human intelligence.
Students will explore various logic, rule-based and statistical models of intelligence. Focusing on the process of modelling and returning to previously-offered answers to the question of "what is computable," we will discuss the significance of decisions to model human intelligence in computational environments. Can the workings of the mind be modelled mathematically? Can the complexity of the brain be reduced to simple equations?
In this unit we will investigate the various schools and methods of applied research in artificial intelligence. While the previous unit concentrated on logic and rule-based models of intelligence, this unit will also cover theories of intelligence as an embodied phenomenon. The history of applied research in artificial intelligence indicates that "intelligence" is a moving target. As soon as a machine achieves some measure of "intelligence"-conversational aptitude; skill at chess-we immediately redefine "intelligence." Through examples of this phenomenon we will venture closer to a definition of intelligence. We will also investigate perhaps the more pressing question: why do we try to make machines into humans? What do we seek to gain from these experiments?
Students will explore literature about existing expert systems. Looking at successful systems, we will analyze the supporting computational models. Students will reflect on the distinctions between information, knowledge and wisdom with reference to their experience of expert systems.
Project: Students will build an expert system using pencil and paper. Then a classmate will try to use the system to answer a question.
NLP provides a forum for experimenting with a successful implementation of research into artificial intelligence and for exploring the components of intelligence. Practitioners of NLP argue that there are universal principles that unite all languages. Recent work in NLP has generated controversy over the nature of those principles: are they structural or statistical? NLP also highlights the distinction between treating text as data and text as language, returning us to our previous discussions of theories of textuality and of structured data. We will read in these areas, familiarizing ourselves with the debates. This unit will also challenge students to deal with texts in a new way. Are we to deal with text as language or as data?
Project: Students will create a corpus. We will encourage a range of data sets-magazine advertisements for women's cosmetics from the 1950's; lyrics for rock songs written 1955-75.
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This page was created on 10 August 2002.