Problematizing AI Omnipresence in Landscape Architecture
- URL: http://arxiv.org/abs/2406.01421v1
- Date: Mon, 3 Jun 2024 15:20:05 GMT
- Title: Problematizing AI Omnipresence in Landscape Architecture
- Authors: Phillip Fernberg, Zihao Zhang,
- Abstract summary: This position paper argues for, and offers, a critical lens through which to examine the current AI frenzy in the landscape architecture profession.
The authors propose five archetypes or mental modes that landscape architects might inhabit when thinking about AI.
- Score: 6.046591474843391
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This position paper argues for, and offers, a critical lens through which to examine the current AI frenzy in the landscape architecture profession. In it, the authors propose five archetypes or mental modes that landscape architects might inhabit when thinking about AI. Rather than limiting judgments of AI use to a single axis of acceleration, these archetypes and corresponding narratives exist along a relational spectrum and are permeable, allowing LAs to take on and switch between them according to context. We model these relationships between the archetypes and their contributions to AI advancement using a causal loop diagram (CLD), and with those interactions argue that more nuanced ways of approaching AI might also open new modes of practice in the new digital economy.
Related papers
- Future Illiteracies -- Architectural Epistemology and Artificial Intelligence [0.0]
We argue that when architects approach AI passively, without actively engaging their own creative faculties, they risk becoming passive users locked in an endless loop of horizontal expansion without meaningful vertical growth.<n>By examining the AI of architecture in the age, this paper calls for a paradigm where AI serves as a tool for vertical and horizontal growth, contingent on human creativity and agency.
arXiv Detail & Related papers (2025-07-31T11:15:39Z) - Composable Building Blocks for Controllable and Transparent Interactive AI Systems [0.8192907805418583]
Black box problem of AI models continues to spread throughout interactive system as a whole.<n>XAI techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models.<n>We propose an approach to represent interactive systems as sequences of structural building blocks.
arXiv Detail & Related papers (2025-06-02T21:10:51Z) - Beyond the Human-AI Binaries: Advanced Writers' Self-Directed Use of Generative AI in Academic Writing [16.24460569356749]
The study explores the self-directed use of Generative AI (GAI) in academic writing among advanced L2 English writers.<n>The findings revealed a spectrum of approaches to GAI, ranging from prescriptive to dialogic uses.<n>We highlight the ways AI disrupts traditional notions of authorship, text, and learning.
arXiv Detail & Related papers (2025-05-17T22:48:44Z) - AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.
The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
arXiv Detail & Related papers (2025-03-04T03:55:38Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - AI Thinking: A framework for rethinking artificial intelligence in practice [2.9805831933488127]
A growing range of disciplines are now involved in studying, developing, and assessing the use of AI in practice.
New, interdisciplinary approaches are needed to bridge competing conceptualisations of AI in practice.
I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives.
arXiv Detail & Related papers (2024-08-26T04:41:21Z) - Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - The AI Alignment Paradox [10.674155943520729]
The better we align AI models with our values, the easier we may make it for adversaries to misalign the models.
With AI's increasing real-world impact, it is imperative that a broad community of researchers be aware of the AI alignment paradox.
arXiv Detail & Related papers (2024-05-31T14:06:24Z) - A call for embodied AI [1.7544885995294304]
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence.
By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures.
This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
arXiv Detail & Related papers (2024-02-06T09:11:20Z) - Demanding and Designing Aligned Cognitive Architectures [0.0]
With AI systems becoming more powerful and pervasive, there is increasing debate about keeping their actions aligned with the broader goals and needs of humanity.
This multi-disciplinary and multi-stakeholder debate must resolve many issues, here we examine three of them.
The first issue is to clarify what demands stakeholders might usefully make on the designers of AI systems, useful because the technology exists to implement them.
The second issue is to move beyond an analytical framing that treats useful intelligence as being reward only.
arXiv Detail & Related papers (2021-12-19T16:49:28Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - A general framework for scientifically inspired explanations in AI [76.48625630211943]
We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented.
This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
arXiv Detail & Related papers (2020-03-02T10:32:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.