Abstract
Generative environments are a unique subset of contemporary generative media produced by systems that map the world as it is and as it could be. This analysis unpacks the mechanics and performance-based goals of generative environmental modeling through the lens of one industrial ecosystem: self-driving vehicles. Generative AI has transformed how we see, imagine and navigate the world, and the automotive industry provides one lens on how far generative AI can cut across an industrial pipeline. The Tesla and other autonomous and semi-autonomous vehicles serve as moments of objectified media convergence where big data, artificial intelligence and data analysis are asked to demonstrate their combined power and potential, and validate their authority through their incremental grasp of the world, largely gleaned through an aggregate of otherwise independent media systems that can see through data: optical, lidar, radar and auditory-based object recognition systems. Tesla and its competitors seek similar goals, as they look to manifest the general rules that underpin the civilized world and use these rules to allow a series of robotic architectures to successfully navigate that world. Tesla remains less a vehicular construct and more a data-driven company; but the company’s fleet is a significant feat of media convergence. Vehicle cameras and collision sensors form a circuit of relations with consumers. This study is a pathway to consider the potential futures of embodied generative media systems, and to understand how those systems are used to organize reality and establish new social contracts.
Presenters
Eric FreedmanExecutive Vice President for Academic Affairs and Provost, Academic Affairs, Truman State University, Armed Forces Americas, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Artificial Intelligence, Generative Media, Media, Environment
Digital Media
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