Hyperobjects refer to objects or phenomena that are so vast in their temporal and spatial dimensions that they evade conventional understanding and manipulation, directly influencing AI's approach to data complexity, distributed systems, and potentially even ethical considerations. In AI, hyperobjects might relate to data sets or algorithms that interact across extensive scales, necessitating new frameworks and methodologies to address the challenges they present. They emphasize the need for innovative thinking in managing AI systems that interact with global networks, extensive databases, or climate-scale models, where standard techniques fall short due to the inability to adequately process or comprehend such massive dimensions all at once.
The term "hyperobject" was first used by Timothy Morton in 2008 within the realm of ecological theory and gained prominence in AI discourse as scholars began to apply the concept to describe increasingly complex systems and networks within the field.
Timothy Morton originally formulated the concept of hyperobjects, and his work laid the groundwork for its application across multiple disciplines, including AI, where researchers continue to explore its implications for understanding large-scale, interconnected systems.