Pandaemonium Arch., Machine Learning
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General
Course Long Title
Pandaemonium Arch., Machine Learning
Subject Code
ATEK
Course Number
639
School(s)
Program(s)
Art and Tech
Academic Level
GR - Graduate
Description
Pandaemonium Architecture was introduced in the 1958 Mechanisation of Thought Processes symposium as an early pattern recognition model for AI. Named after the demon-inhabited city in Milton's Paradise Lost, the Pandaemonium Architecture assemblage employs 'demons' -bits of information or code- that 'scream' in order to ascend a hierarchy of algorithmic hurtles. Artificial Intelligence and Machine Learning can be applied to any digital artistic medium, including video, sound, text, still images, and 3d modeling and printing. Further, AI and ML can be trained on almost any digital information, making it a powerful tool in the artist's arsenal. Diffusion Models and Generative Adversarial Nets combine predictive, generative, and
adversarial operations and function as rapidly iterated critique, analogous to hyperspeed natural selection, quickly evolving their objects to high levels of complexity. LLM’s employ large databases to generate texts, power chatbots, and even write code. Predictive Analytics employs game theory, statistical analysis, data analysis, scenario planning, and Modeling and Simulation to create accurate predictive models for different aspects of the future. Social Engineering operates on individuals and masses to “create” this future. These are the tools of technocracy. Artists should examine them and consider picking them up as well. Participants will explore the theory and history of AI and employ AI and ML, including local, server-side, and online AI's, as well as generative apps to make art. Labs will demonstrate the use of tools and resources to research and create AI and ML-based artworks and will allow time for participants to focus on individual or group Final Projects. Computer literacy, conceptual skills, and basic coding skills are required. Advanced coding will not be necessary but advanced coders are encouraged to participate.
adversarial operations and function as rapidly iterated critique, analogous to hyperspeed natural selection, quickly evolving their objects to high levels of complexity. LLM’s employ large databases to generate texts, power chatbots, and even write code. Predictive Analytics employs game theory, statistical analysis, data analysis, scenario planning, and Modeling and Simulation to create accurate predictive models for different aspects of the future. Social Engineering operates on individuals and masses to “create” this future. These are the tools of technocracy. Artists should examine them and consider picking them up as well. Participants will explore the theory and history of AI and employ AI and ML, including local, server-side, and online AI's, as well as generative apps to make art. Labs will demonstrate the use of tools and resources to research and create AI and ML-based artworks and will allow time for participants to focus on individual or group Final Projects. Computer literacy, conceptual skills, and basic coding skills are required. Advanced coding will not be necessary but advanced coders are encouraged to participate.
Registration Restrictions
RGATEK - Art and Technology Students Only