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Artificial Intelligence Art

General data

Course ID: 1000-OOAIA
Erasmus code / ISCED: (unknown) / (unknown)
Course title: Artificial Intelligence Art
Name in Polish: Artificial Intelligence Art
Organizational unit: Faculty of Mathematics, Informatics, and Mechanics
Course groups: General university courses
General university courses at Faculty of Mathematics, Informatics, and Mechanics
ECTS credit allocation (and other scores): 6.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language: English
Type of course:

general courses

Mode:

Classroom

Short description:

This lab-focused course investigates how AI is disrupting the creative processes and changing how we view the world, with a particular interest in memory and algorithmic representation. The scope of the course ranges from the recent developments in creative deep learning technologies to the use of algorithmic tools in implementing interdisciplinary arts and science projects.

Recruitment for the course will be conducted separately, by applications. Students of mathematics, Computer Science, bioinformatics and Machine learning who pass this course may obtain OGUN (human/social science general university course) credits.

Full description:

Artificial intelligence (AI) has developed significantly in the recent times with a variety of deep learning architectures. In this rapid prototyping team-focused course, we study and reimagine the implications of AI, bias, and algorithmic justice within creative use and design process. Especially, during the semester, students will create interdisciplinary arts and design projects with the central theme that memory, dreams, and nation-state collective narratives being defined through rapidly developing technologies like GPT-3, Stable Diffusion, and others.

The focus will be on the challenges presented by the artistic creation of using AI language and vision models to define individual and collective visual memory, or other types of data and models. We will survey and have hands-on experiences with many AI tools, as demanded by students interests and projects, such as speech to text, text to image, image to image, and image to video, and others. For examples, sentences and phrases are translated into images extensively sourced from various repositories of databanks. Programming, prompt engineering, and experimenting with models, software, datasets will be central. In the process, the course will look at the issues of algorithmic bias, and the aesthetics of AI with a strong focus on current societal issues.

Through a team based rapid prototyping, Dr. Neo Christopher Chung from the University of Warsaw and Dr. Ellen Pearlman, visiting Fulbright Scholar in Art, Media, and New Technologies from MIT/ThoughtWorks Arts, will conduct this special colab atthe University of Warsaw. Find out more about the Art-A-Hack (https://artahack.io) rapid prototyping approach.

How to apply:

This special colab is by application only. Email to n.chung@uw.edu.pl and ellen@volumetric.co.

Describe your project idea or your particular interest in this colab concept. We welcome applications with diverse backgrounds. No prior knowledge with AI art necessary. When we receive your applications, we will match you up with others in team settings.

Full name

Email address

Your studies, skills, and background

Your project ideas or interests of how to contribute.

Bibliography:

Art Hack Practices by Victoria Bradbury, Suzy O’ Hara

Collective Wisdom: Co-Creating Media within Communities, across Disciplines and with Algorithms by Katerina Cizek, William Uricchio

AI Art: Machine Visions and Warped Dreams by Joanna Zylinska

Defining AI Arts: Three Proposals by Lev Manovich

Learning outcomes:

Understanding the creative AI tools and technologies

Learning how to work within interdiscplinary teams

Assessment methods and assessment criteria:

Course participation, homework, final project

Classes in period "Summer semester 2022/23" (future)

Time span: 2023-02-20 - 2023-06-18
Choosen plan division:


magnify
see course schedule
Type of class:
Lab, 45 hours more information
Coordinators: Neo Christopher Chung, Ellen Pearlman
Group instructors: Neo Christopher Chung, Ellen Pearlman
Students list: (inaccessible to you)
Examination: Grading
Notes:

Students of mathematics, Computer Science, bioinformatics and Machine learning who pass this course may obtain OGUN (human/social science general university course) credits.

Course descriptions are protected by copyright.
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