Fall 2018 - IAT 882 G100
Special Topics II (3)
Class Number: 9794
Delivery Method: In Person
How to use Artificial Intelligence and Machine Learning to partially or completely automate creative tasks? How to integrate AI, machine learning and intelligent data processing into art and design practices? Be it design, or artistic expression, the next generation of computer-based applications will embed AI and machine learning. Get onboard with this 4th wave of HCI research and practice with this special topics class.
This course proposes an introduction and overview of the history and practice of generative arts, computational creativity, and creative AI with an emphasis on the formal paradigms and algorithms used for generation. On the technical side, we will study core techniques from mathematics, artificial intelligence, and artificial life that are used by artists, designers and musicians across the creative industry. We will start with processes involving chance operations, chaos theory and fractals and move on to see how stochastic processes and rule-based approaches can be used to explore creative spaces. We will study agents and multi-agent systems and delve into cellular automata, and virtual ecosystems to explore their potential to create novel and valuable artifacts and aesthetic experiences. We will then study how evolutionary computing, neural networks, and procedural generation can produce original designs and artworks. We will survey how to formalize aesthetic measures and review how creative AI systems can be evaluated. Finally, we will discuss the philosophical, ethical, and societal implications of these game-changing approaches.
The class is illustrated by numerous examples from past and current productions across creative practices such as visual art, new media, music, poetry, literature, performing arts, design, architecture, games, robot-art, bio-art and net-art. Students get to develop their own artistic and design project using one or several techniques studied in class or produce original writing.Through your project, you will either design and build generative software or computer-assisted creation tools relevant to your research area, run experiments using existing, software or delve into theoretical research and critical thinking on this fast growing field. The interweaving of related theoretical and practical issues will help you situate your work within a larger perspective on design, art, science and technology.
COURSE-LEVEL EDUCATIONAL GOALS:
Looking as more specific learning outcomes, students attending this course will develop their ability to:
- Define and identify computational creativity, generative art, and creative AI
- Define and identify artificial intelligence, machine learning and artificial life
- Describe and discuss the various types of creativity, and their theorisations
- Describe and compare at least six different algorithms that have been used to design generative and computationally creative systems
- Apply the above knowledge to design and develop systems that automatize a wide variety of creative and design tasks.
- Discuss the challenges specific to the evaluation of generative creative systems
- Apply the above knowledge to design and implement empirical studies to evaluate creative AI system
- Identify and discuss ethical, philosophical, and societal issues associated with the rise of AI-based systems.
- Present creative AI systems and results in a scientific research paper
- Present their research, system or result in an oral presentation
- Theoretical survey and research (includes an in-class presentation) 30%
- Project (10% process, 10% result, 10% in-class presentation and demonstration) 30%
- Research Paper (refining and presenting all of the above) 30%
- Participation 10%
While the class is self-content, a high level of computational literacy is expected. As often for graduate classes, the main requirement is motivation and willingness to learn.
The only required reading will actually be the online video lectures of the Kadenze classes:
- Generative art and computational creativity
- Advanced generative art and computational creativity.
ReferencesThis is a graduate course. Actual chapters, journal articles and conference papers might be selected for reading as the course unfolds.
In addition to the reference material, students will be responsible for finding research papers and texts suitable for the work undertaken.
The following list is only indicative (and will be updated in class):
Art and Science: Metacreation: Art and Artificial Life, Whitelaw, M., MIT Press, 281 pp., 2004, ISBN 0-262-23234-0
Information Art: Intersections of Art, Science and Technology, Wilson, S., MIT Press, 945 pp. 2002.
Computers and Creativity, McCormack, J. and d’Inverno, M. (Eds.), Springer, 430 pp., 2012
Artificial Life Models in Software, Adamatzky, A., Komosinski, M. (Eds.), Springer, 344 pp., 2005, ISBN: 978-1-85233-945-6Creative Evolutionary Systems (With CD-ROM), Corne, D., W., Bentley, P. J. (Eds.), The Morgan Kaufmann Series in Artificial Intelligence, 684 pp., 2002, ISBN: 978-1558606739.
Proceedings of the Second Artificial Intelligence and Interactive Digital Entertainment International Conference (AIIDE 2006), Laird, J. and Schaeffer, J. (Eds.), 172 pp., 2007, ISBN 978-1-57735-280-8
Music and Artificial Intelligence, Anagnostopoulou, C. Ferrand, M. and Smaill, A. (Eds.), Proceedings of the Second International Conference on Music and Artificial Intelligence, ICMAI 2002, Edinburgh, Lecture Notes in Computer Science, vol. 2445, Springer, 2002, ISBN 3-540-44145-X.
Interactive Drama, Art and Artificial Intelligence. Mateas, M., Doctoral Thesis. UMI Order Number: AAI3121279., Carnegie Mellon University, 2002.
Evolutionary Computer Music, Miranda, E. R., Biles, J. A. (Eds.), Springer, 259 pp. With CD-ROM., 2007, ISBN: 978-1-84628-599-8.Artificial Life For Computer Graphics, Terzopoulos, D., in Communications of the ACM, Vol 42, No. 8, pp. 32-42, 1999. Science:
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Weiss, G. (Ed.), The MIT Press, 643 pp., 1999, ISBN: 978-0262232036
Machine Learning, Mitchell, T., McGraw Hill, 1997.Introduction to Evolutionary Computing, Eiben, A.E. and J.E. Smith, Springer, Berlin, 2003.
An Introduction to Genetic Algorithms, Mitchell, M., MIT Press, Boston, Mass., 2002.
Computational Intelligence: an introduction, Engelbrecht, A.P., John Wiley & Sons, Chichester, England, 2002.
Artificial Life: The Quest for a New Creation, Levy, S., Random House Value Publishing, 1994, ISBN: 978-0517118085.
Swarm Intelligence, Eberhart, R. C., Shi, Y., Kennedy, J., Morgan Kaufmann; The MIT Press, 272 pp., 2001, ISBN: 978-0262041942.
Links: Instructor's Web page:
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS