- Autumn 2024
Syllabus Description:
Artwork by artist collective Interspecifics - SPECULATIVE COMMUNICATIONS [2017]
Instructor
Laura Luna Castillo lcasti2@uw.edu
Office hours: Meeting after class or at Raitt 207, by appointment
TA
Sadaf Sadri ssadrii@uw.edu
By appointment
**Our classes will take place between three classrooms, check canvas calendar on information for each day:
205 Classroom in Raitt Hall
123 Computer Lab Raitt Hall - code to access 207319
129 New Media Performance Lab in Raitt Hall
MW 12:30-2:20 PM
- For final project we will hold an exhibition on week 10 of the quarter at the DXARTS gallery
Description
This studio art course is designed for students from diverse backgrounds and departments including DXARTS, Music, Art, Drama, Comparative Literature, Creative Writing, CSE, and the eScience Institute, among others, who wish to creatively and critically engage, transform and interpret archives and datasets. By studying developments in A.I. and Machine Learning, such as natural language processing (NLP) algorithms and text-to-image models, students will gain a strong understanding of the underlying datasets, biases, processes and mechanisms of such tools and their structures. Furthermore, students are challenged to build critical relationships with all kinds of archives and collections, as data sources to imagine creative outputs, through creative programming, data sonification/visualization and other algorithm-driven projects. Can an A.I. generated text or image be considered an artwork unto itself? Or must we engage in creative co-authorship with such systems, in order to consider a (partially) computer generated output to be a work of art?
This course requires no prior coding experience and encourages interdisciplinary research. Throughout the course, collaborative potentials will be explored through presentations by visiting artists and researchers working across the creative and technical intersections of A.I. and data-driven practices.
Weekly lectures and workshops will provide context and tools for prototyping individual and/or group projects. Topics include:
- Engaging with archives and their fractal narratives
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History of experimental text - examples of early NLP, Markov chains, and modern/postmodern/non-computational approaches to experimental text/poetry
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Pre-trained A.I. models - GPT3 and other NLP models
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Strategies for creatively/critically co-authoring with generative A.I. tools
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Ethics - understanding bias and the social implications of data collection and large Machine Learning models
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Tools and methodologies for creative coding, visualization and interactive systems
As this course will become an 'on-ramp' for DXARTS 481 Data-driven Art I, students will also learn some basics of the Python programming language for accessing APIs, organizing data, and automating processes
Assignments
There are some homework assignments, a midterm, and a substantial, creative final project.
Attendance and participation at the final session is mandatory. Your contribution to group discussions, labs and critiques is a key learning goal for the class.
Your grade will break down as described in the section below.
Grading
Grading of all assignments will be based upon the quality of concept, experimentation, work ethic, and realization. The overall class grade will be broken down as follows:
- Participation: 15% - There are many ways to participate: in-class participation, readings and discussions, critiques, labs, and more.
- Homework: 15%
- Midterm project: 35%
- Final project: 35%
Any missed assignments will lower your grade, so it is VERY important that all work is completed. Everything in the class builds on previous work, so do not fall behind.
Main Software
TOUCHDESIGNER
In this course, we will be using TOUCHDESIGNER, a visual programming software based in Python, which allows us to create and activate all kinds of data in the form of interactive 2D and 3D applications in real time.
Download the software here: https://derivative.ca/download
If you would like to know more about this software please visit: https://learn.derivative.ca/courses/100-fundamentals/
ANACONDA
In this course we will be using ANACONDA, which is an open-source distribution of the Python and R programming languages that's used for data science, machine learning, and artificial intelligence. This software will allow us to experiment with certain Machine Learning algorithms and interface them with real-time, creative applications.
We will download and install this tool during a lab session, so please don't install it yourself if you don't have experience working with Python and package managers.
Arduino
https://www.arduino.cc/en/software
**Optional tools to get familiar with:
Create an account for OpenAI: https://openai.com/
Create an account for RunwayML: https://runwayml.com/
**Class G-Drive folder - https://drive.google.com/drive/folders/1PWh5bh37iJ3tBPB9-zbYjUFKO81bCp7J?usp=sharing
This folder will contain patches, readings and other useful stuff, check it periodically.
Class mailing list
dxarts480a_au24@uw.edu (NOTE: you'll need to post to the class list using your UW NetID email)
DXARTS resources:
- Equipment: https://dxarts.washington.edu/equipment-reservations
- fill out, sign and email or print: Statement of Responsibility form
- DXARTS Facilities and Labs: https://dxarts.washington.edu/facilities
- DXARTS gallery: https://dxarts.washington.edu/facilities/dxarts-gallery
Note regarding Multiple Submissions
Students are generally NOT allowed to submit one project for credit in two different classes (see UW Policy on Academic Responsibility), without prior discussion with and permission from BOTH class professors.
Religious accommodation
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/).
DEI+A statement
DXARTS strives to create a safe, affirming, and welcoming space for all cultures, races, nationalities, sexes, gender identities, ages, religions, and economic statuses. We are committed to ensuring that the University of Washington’s DEI+A values are reflected through our diverse community of students, faculty and staff, our research and public-facing events, our course curriculum, our actions, and interactions within the department.
For more departmental and campus-wide resources, please visit the DXARTS DEI+A page.
COVID-related policies
As per UW policy, masks/face coverings are recommended inside campus buildings - see UW policy here. As this class is conducted in-person, students are expected to participate in class to fully benefit from course activities and meet the course’s learning objectives. Students should only register for this class if they are able to attend in-person. To protect their fellow students, faculty, and staff, students who feel ill or exhibit possible COVID symptoms should not come to class. When absent, it is the responsibility of the student to inform the instructor in advance (or as close to the class period as possible in the case of an unexpected absence), and to request appropriate make-up work as per policies established in the syllabus. What make-up work is possible, or how assignments or course grading might be modified to accommodate missed work, is the prerogative of the instructor. For chronic absences, the instructor may negotiate an incomplete grade after the 8 th week, or recommend the student contact their academic adviser to consider a hardship withdrawal (known as a Registrar Drop).