Summary of Problematizing Ai Omnipresence in Landscape Architecture, by Phillip Fernberg et al.
Problematizing AI Omnipresence in Landscape Architecture
by Phillip Fernberg, Zihao Zhang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a framework for examining the role of artificial intelligence (AI) in landscape architecture, identifying five mental modes or archetypes that professionals may adopt when considering AI. Rather than viewing AI as solely accelerating or decelerating processes, these archetypes exist on a relational spectrum and can be switched between depending on context. The authors model this relationship using a causal loop diagram (CLD), arguing that adopting more nuanced approaches to AI could lead to new modes of practice in the digital economy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence is changing the landscape architecture profession. It suggests that instead of just thinking about AI as making things faster or slower, we should think about five different ways that people might use AI. These “archetypes” can be switched between depending on what’s happening, and they could help us come up with new ideas for working in a digital economy. |
