Cultivated Wildness: Technodiversity and Wildness in Machines
Not on the map. Why?
Abstract (arXiv)
This paper investigates the idea of cultivated wildness at the intersection of landscape design and artificial intelligence. The paper posits that contemporary landscape practices should overcome the potentially single understanding on wilderness, and instead explore landscape strategies to cultivate new forms of wild places via ideas and concerns in contemporary Environmental Humanities, Science and Technology Studies, Ecological Sciences, and Landscape Architecture. Drawing cases in environmental engineering, computer science, and landscape architecture research, this paper explores a framework to construct wild places with intelligent machines. In this framework, machines are not understood as a layer of "digital infrastructure" that is used to extend localized human intelligence and agency. Rather machines are conceptualized as active agents who can participate in the intelligence of co-production. Recent developments in cybernetic technologies such as sensing networks, artificial intelligence, and cyberphysical systems can also contribute to establishing the framework. At the heart of this framework is "technodiversity," in parallel with biodiversity, since a singular vision on technological development driven by optimization and efficiency reinforces a monocultural approach that eliminates other possible relationships to construct with the environment. Thus, cultivated wildness is also about recognizing "wildness" in machines.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 60 references that are in the map. First posted on arXiv 3 May 2023.
Builds on
4 of 60 references.
References · 4
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
- 2018Hardware Conditioned Policies for Multi-Robot Transfer Learningcited by 46
- 2019MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamicscited by 18
Built on by
Citing papers per year: 2023: 1.
1 paper.