Loading...“AI’s Utopian Visions: Exploring Sustainable Future Landscapes for the Built Environment”
This online resource serves as a platform for exploring the intersection of artificial intelligence (AI) and sustainable design, focusing on envisioning landscapes and topographies for the built environment. Through a series of interactive modules, students will delve into the potential of AI to inform and shape future urban landscapes that prioritize sustainability, resilience, and human well-being. By engaging with cutting-edge AI technologies and design principles, participants will gain a deeper understanding of how emerging technologies can be harnessed to create more livable, equitable, and environmentally focused places and spaces.
Project Statement:
“AI’s Utopian Visions” is a collaborative project designed to foster exploration and dialogue surrounding the role of AI in shaping sustainable future landscapes for the built environment. Through a combination of curated readings, multimedia content, interactive exercises, and visual AI & design workshops, students will embark on a journey to reimagine landscapes and topographies guided by principles of ecological resilience, social equity, and technological innovation thereby deepening comprehension of how we imagine the future of our environments and identities.
By engaging with real-world case studies and visionary design proposals, students will develop critical thinking skills and creative problem-solving abilities essential for addressing the complex challenges and future of sustainable constructed environments. Through an interdisciplinary lens spanning design, visual AIs, environmental studies, and humanities this resource delves into the profound impact of AI on contemporary culture’s view of our built environment.
Students explore critical analyses that unravel the complex interplay between nature and technology, human and nonhuman realms, and ecosystems and intelligent machines. Discover innovative landscape strategies inspired by design experiments, aimed at tackling pressing challenges like the climate crisis and social injustice and their repercussions on communities and urban landscapes. This project is a visual exploration of AI’s potential to create a more sustainable and inclusive future for our cities.
Tools: Visual AI Tools: Stable Diffusion (image), Gemini, DALL-E, Mid-journey (text to image), Artbreeder, Adobe Firefly (?) Poe AI, ChatGPT, Research Databases, optional: Design Software.
Recommended Disciplines: Design and Constructed Environments. Environmental Studies, Landscape Architecture, Arts & Humanities, Engineering & Sciences, Anthropology, Sociology, and Women and Gender Studies.
Recommended Course Size: 20-26 students
Roles: Faculty will be facilitators, Students will act as both learners and researchers, engaging in critical analyses and design experiments.
Classroom Time Needed: Flexible, with suggested modules spanning over several weeks or a semester-long course. Also can be done in two class periods, one for presentation and research and the other for creation and gallery display.
LEARNING OBJECTIVES:
- Analyze the interplay between nature, technology, and human/nonhuman realms in the constructed environment.
- Explore innovative landscape strategies inspired by design experiments to address pressing global challenges.
- Deepen comprehension of digital topographies constructed through practical engagement with visual AI tools.
PRE-WORK:
Students are encouraged to familiarize themselves with basic concepts of AI, ethics, and landscape theory to facilitate deeper engagement with course materials. See AI and the Enchanted Lamp: Prompting
INSTRUCTIONS
PRE-DAY 1: See AI and the Enchanted Lamp: Prompting
Daily projects can be condensed or expanded as the teacher needs:
Students research and participate in critical discussions to unravel the complexities and juxtaposition of AI’s impact on contemporary visions of sustainability and the environment. For example:
AI for Climate Change: Managing Floods Using AI Early Warning Systems
How AI Is Helping Communities Anticipate Floods
AI Is Accelerating the Loss of Our Scarcest Natural Resource: Water
AI’s excessive water consumption threatens to drown out its environmental contributions
a) Discussion: Facilitate small group discussions where students share their thoughts, create a list of inquiries and reflect on the questions.
b) Students write a short reflective writing exercise comparing the
group/class questions and personal perceptions and experiences
surrounding sustainability and the future of constructed environments.
Activity (30 minutes):
a) Have students gather all the reflections and responses that represent
different aspects and themes.
b) Co-create on a shared Miro board these responses as a collective archive
of knowledge: they can be text or images sorted into themes.
Engage in hands-on activities using visual AI tools to create and analyze digital representations of identity and landscapes.*
a) Creation of new images from gathered images or text on the miro boards,
students need to bring or have access to laptops for individual online work.
b) Demonstration: Using visual AI generator tools:
AI generators to use/explore: Stable Diffusion (image),
Gemini, DALL-E, Mid-journey (text to image), Artbreeder (image),
Adobe Firefly & others at the teacher’s discretion.
Collaborate with peers to develop innovative landscape strategies aimed at addressing sustainability and built environments.
a) Students Present work: Either digital on a large screen or print out 5
completed AI-generated images at 8.5×11”.
b) Pinup and Gallery walk- pinup (tape) to the wall stacked vertically
c) Each student will take Post-it notes and give feedback
d) Class discussion: Dig deeper into the strategies students present, breaking them down into examining “both sides of the coin”, for example:
Ethics and Impact of AI in Sustainable Design:
How can we ensure that AI-driven landscape strategies prioritize ethical considerations and equitable outcomes for all communities? Discuss the potential for AI to either bridge or widen gaps in environmental justice and access to sustainable living spaces.
Balancing Innovation and Tradition in AI-Driven Landscapes:
In what ways can AI be integrated into landscape strategies without overshadowing traditional, indigenous, or local knowledge that has historically contributed to sustainable practices? Reflect on the role of AI in complementing rather than replacing time-tested environmental wisdom.
Long-Term Sustainability vs. Short-Term Gains:
AI has the potential to revolutionize our approach to sustainable landscapes, but how do we ensure that these innovations focus on long-term sustainability rather than short-term efficiencies? Consider the risks of relying on AI for immediate results and discuss strategies to maintain a balance between immediate benefits and enduring ecological health.
e) Follow with a wrap-up reading:
“Problematizing AI Omnipresence in Landscape Architecture”
by Phillip Fernberg and Zihao Zhang
*Additional parallel and complimentary IRL activity forthcoming.
RESOURCES:
The Office of Speculative Ecologies (OSE)
Zihao Zhang and Shurui Zhang. (Forthcoming). “Before the After: Representing Climate Actions in the Age of AI.” In Representing Landscapes: Visualizing Climate Action. Nadia Amoroso (Ed.), Routledge.
Cantrell, Bradley, Zihao Zhang, and Xun Liu. 2021 “Artificial intelligence and machine learning in landscape architecture.” In The Routledge Companion to Artificial Intelligence in Architecture. Imdat As & Prithwish Basu. (Ed) Taylor & Francis Group. DOI: 10.4324/9780367824259-15
Journal Articles:
Zhang, Zihao, https://www.researchgate.net/profile/Zihao-Zhang-10
Zhang, Zihao, Susan L Epstein, Casey Breen, Sophia Xia, Zhigang Zhu, and Christian Volkmann. 2023. “Robots in the Garden: Artificial Intelligence and Adaptive Landscapes.” Journal of Digital Landscape Architecture 8–2023: 264–72. https://doi.org/doi:10.14627/537740028.
Zhang, Zihao. 2022. “Post-Digital Landscape and Post-Digital Culture.” Journal of Digital Landscape Architecture 7–2022 (June): 26–35. https://doi.org/doi:10.14627/537724004.
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