Recent Publications Related to Urban Planning
- “The Indispensable Role of Generative AI in Urban Planning” (2024-03-06) (https://www.altaconsulting.ca/post/the-indispensable-role-of-generative-ai-in-urban-planning)
This article discusses how generative AI is revolutionizing urban planning through data analysis, decision support, and enhanced communication.
- “Revolutionizing Urban Planning with Generative AI: A New Era of Smart Cities” (2023-06-20) (https://www.linkedin.com/pulse/revolutionizing-urban-planning-generative-ai-new-era-smart-chiancone)
This article explores how generative AI is transforming urban planning by optimizing infrastructure, enhancing urban design, and reducing environmental impacts.
- “How Generative AI can Help to Create More Livable and Healthy Urban Environments” (2023-04-12) (https://www.maket.ai/post/how-generative-ai-can-help-to-create-more-livable-and-healthy-urban-environments)
This article outlines how generative AI can be used to improve urban design, public transportation, and sustainability in cities.
- “Generative AI is here. This is how 5 cities plan to manage its use.” (2023-11-16)2
This article examines how cities like New York, Seattle, Tempe, and San Jose are developing policies and guidelines to govern the use of generative AI in urban planning. (https://www.smartcitiesdive.com/news/artificial-intelligence-how-cities-plan-tech/699906/)
- “Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning” (2023-05-01)
This paper explores the potential of integrating generative AI and large language models like ChatGPT into the urban planning process. (https://arxiv.org/abs/2304.03892)
- Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China (https://www.researchgate.net/publication/376008012)
- Generative urban design: A systematic review on problem formulation, design generation, and decision-making (https://www.sciencedirect.com/science/article/abs/pii/S0305900623000569)
- Optimizing urban layouts through computational generative design: density distribution and shape optimization (https://www.researchgate.net/publication/372992492)
- Generative AI-Powered Urban Digital Twins: Pioneering Environmental Solutions for Sustainable Smart Cities (https://www.researchgate.net/publication/374812243)
- Optimization of the location and design of urban green spaces
(https://arxiv.org/abs/2303.07202)
- SmartCityGPT’: How Generative AI Creates Smart and Sustainable Cities
(https://www.researchgate.net/publication/372947756)
- Algorithmic urban planning for smart and sustainable development: Systematic review of the literature (https://www.sciencedirect.com/science/article/pii/S2210670723001737)
- Large language model empowered participatory urban planning
(https://arxiv.org/abs/2402.01698)
- A Survey of Generative AI for Intelligent Transportation Systems (https://arxiv.org/html/2312.08248v1)
- A Survey of Generative AI for Intelligent Transportation Systems (https://arxiv.org/pdf/2312.08248)
- New generative and AI design methods for transportation systems and urban mobility design, planning, operation, and analysis: contribution to urban computing theory and methodology (https://www.researchgate.net/publication/377395649)
Whitepapers and Industry Reports
- “Generative Adversarial Nets” by Ian Goodfellow et al. – This seminal paper introduced Generative Adversarial Networks (GANs), a groundbreaking concept in the AI field, which has had a profound impact on the development of generative models. (https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf)
- “Attention Is All You Need” by Vaswani et al. – While primarily focused on transformer models, this paper has been fundamental for advancements in generative AI, especially in natural language processing and generative text applications. (https://arxiv.org/abs/1706.03762)
- “BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis” by Andrew Brock et al. – This paper is crucial for understanding the capabilities of GANs in generating high-quality images, influencing both academic research and practical applications in visual media. (https://voletiv.github.io/docs/presentations/20181030_Mila_BigGAN.pdf)
- OpenAI’s GPT Papers- These papers detail the development of OpenAI’s Generative Pre-trained Transformers, which have set new standards for generative models in text and are extensively used in various applications from writing assistance to code generation. (https://arxiv.org/abs/2303.08774 [GPT-4])
- “Large Language Models are Unsupervised Multi Task Learners” (https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
- “Variational Autoencoders” by Kingma and Welling – This foundational paper presents a staple architecture in generative AI, influencing how machines can learn to encode and generate new data points efficiently. (https://arxiv.org/abs/1906.02691)
- NVIDIA’s StyleGAN Papers – These include several papers detailing NVIDIA’s developments on generative adversarial networks that have enabled highly realistic image generation, revolutionizing fields such as design and entertainment. (https://research.nvidia.com/publication/2020-06_analyzing-and-improving-image-quality-stylegan, https://research.nvidia.com/publication/2022-05_stylegan-nada-clip-guided-domain-adaptation-image-generators)
- “Language Models are Few-Shot Learners” by Tom B. Brown et al. (OpenAI) – This report on GPT-3 explores the model’s few-shot learning capabilities, demonstrating the potential generative AI has to perform tasks with minimal human supervision. (https://arxiv.org/abs/2005.14165)
- McKinsey & Company Reports on AI and Analytics – McKinsey has published various reports that discuss the economic implications, trends, and future of AI technologies, including generative AI. (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction, https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america)
- “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation” – This report discusses potential malicious uses of AI technologies, including generative AI, providing insights into the risks and necessary safeguards. (https://www.researchgate.net/publication/323302750_The_Malicious_Use_of_Artificial_Intelligence_Forecasting_Prevention_and_Mitigation)
- World Economic Forum (WEF) Reports – WEF routinely releases reports on the impacts of AI on society, economy, and governance, including discussions on ethical implications and policy considerations for generative AI. (https://intelligence.weforum.org/topics/a1G680000008gwFEAQ)
- “Generative AI in Scholarly Communications: Ethical and Practical Guidelines for the Use of Generative AI in the Publication Process” – This white paper was released by STM (the International Association of Scientific, Technical and Medical Publishers) in December 2023. It provides guidance on the ethical and practical use of generative AI in scholarly publishing. (https://www.stm-assoc.org/wp-content/uploads/STM-GENERATIVE-AI-PAPER-2023.pdf)
- “Generative AI in the Enterprise” – This executive summary from Dell Technologies and NVIDIA discusses the growth of generative AI, its applications, and the need for enterprises to develop their own large language models. It introduces their joint “Project Helix” initiative to enable enterprise-grade generative AI. (https://infohub.delltechnologies.com/en-us/t/generative-ai-in-the-enterprise/)
- “6 Steps for Scaling Generative AI Across the Enterprise” – This report from the AI, Data & Analytics Network (ADA) outlines key considerations for enterprises looking to scale their use of generative AI, including issues of scalability, security, and the transition to production. (https://www.aidataanalytics.network/)
- “How Generative AI Will Transform the Enterprise” – Another report from ADA that examines the potential of generative AI to transform businesses, while also highlighting current limitations and the pace of technological change. (https://www.aidataanalytics.network/data-science-ai/whitepapers/2023-report-how-generative-ai-will-transform-the-enterprise)
- “GenAI Readiness Report: The Most AI-Mature Companies in 2024” – This report from HG Insights analyzes the AI maturity of Fortune 500 and Inc. 5000 companies, providing insights into generative AI adoption across different industries. (https://hginsights.com/blog/genai-ai-readiness)
- https://urbanai.fr/wp-content/uploads/2023/10/Generative-AI-Report.pdf
- “Urban Informatics and Big Data: A Report by the ESRC Cities Expert Group” – Although not exclusively about generative AI, this report discusses the role of big data and AI in urban development, which includes aspects of generative models for planning and simulation. (http://www.spatialcomplexity.info/files/2015/07/Urban-Informatics-and-Big-Data.pdf)
- “AI and Cities: UN-Habitat and Mila launch a collaborative White Paper” – This whitepaper, a collaboration between UN-Habitat and Mila, provides insights and recommendations on how AI systems, including generative AI, can be used to support sustainable urban development.(https://mila.quebec/en/news/ai-and-cities-un-habitat-and-mila-launch-a-collaborative-white-paper-on-the-use-and-potential)
- “Designing for the Public Sector with Generative AI” – This Deloitte report explores the potential applications of generative design, a form of generative AI, in the public sector, including urban planning and facility design. (https://www2.deloitte.com/us/en/pages/public-sector/solutions/designing-for-the-public-sector-with-generative-ai.html)
- “Generative AI and its potential environmental impact” – While not directly focused on urban planning, this Bosch blog post discusses the environmental implications of generative AI, which is relevant for considering the sustainability of generative AI applications in urban contexts. (https://blog.bosch-digital.com/generative-ai-and-its-potential-environmental-impact/)
- “Diffusion Models Beat GANs on Image Synthesis” (2021) by Prafulla Dhariwal and Alex Nichol – Introduced diffusion models as a promising alternative to GANs for image generation. (https://arxiv.org/abs/2105.05233)
- “Stable Diffusion” (2022) whitepaper by Stability AI – Described the technical details behind the powerful and open source Stable Diffusion text-to-image model. (https://arxiv.org/pdf/2403.03206)
- Computing Power and the Governance of Artificial Intelligence https://arxiv.org/pdf/2402.08797
- “LaMDA” (2022) by Google AI – Providing details on their large language model LaMDA focused on open-ended conversation. (https://arxiv.org/abs/2201.08239)
- MIT “On the Opportunities and Risks of Foundation Models” (2022) – Comprehensive report analyzing the societal impacts of large AI models like GPT-3. (https://arxiv.org/abs/2108.07258)
- OpenAI “Constitutional AI” (2023) – Proposing technical and governance approaches for developing safe and ethical AI systems at scale. (https://arxiv.org/abs/2212.08073)
