Artificial Intelligence is playing an everincreasing role in our lives, but what impacts might it have on the design professions? Will it introduce a new style? Or will it just help to improve the design process? Or will it have a completely different impact? This article attempts to offer an overview of the potential of Artificial Intelligence, defining the different forms of Artificial Intelligence, and illustrating its argument with the work of three designers. It argues that Artificial Intelligence will not generate a new style. However, it will have a radical input on the process of designing. It is also likely to force us to call into question many accepted beliefs within the design community. Certain traditional terms, such as the concept of a “design” and the very notion of “designing,” are likely to be replaced by a new lexicon that includes terms such as “searching” and “outcomes.” But, more importantly, the whole myth of the “artistic genius” is likely to be also called into question. What Artificial Intelligence tells us, as the paper concludes, is not how “artificial” Artificial Intelligence is, but rather how misguided our own understanding of human intelligence has been.
Streets are a focal point of human activities and a major interface of the social interaction between urban dwellers and urban built environment. A better understanding of the urban landscapes along streets is thus important in urban studies. The increasing availability of street-level images provides new opportunities for urban landscape studies to study and analyze streetscapes at a fine level and from a different perspective. In this study, we presented an application of a recently developed Deep Convolutional Neural Network on landscape analysis based on street-level images. Different urban features were identified from street-level images accurately using a trained Deep Convolutional Neural Network model. Based on the image segmentation results, we further measured the spatial distribution of the street greenery and quantitatively analyzed the openness of street canyons in Cambridge, Massachusetts. The proposed combination of Artificial Intelligence and the massively collected street-level images provides a new sight for urban landscape studies for cities around the world.
Since the mid- to late 1990s, both Artificial Intelligence and wearable interaction techniques have dramatically changed the way how humans interact with the exterior environment, as such influence will be greater and greater over time. Wearable devices and mobile terminals have enhanced humans’ experience on spatial environment by improving their perception, understanding, and ability of control towards certain places. This paper centers on the advanced efforts of the augmentation technologies in intelligent sensing, cognition, and feedback, by introducing several innovative applications, including 1) the live-audio augmented project for the Washington Monument; 2) the CityScope project, developed by the MIT City Science Lab, which provides intelligent decision-making aid for non-professional users; and 3) Nature-X, an augmented reality application that creates nature community scenes based on Location Based Service. All these studied cases of spatial and environmental planning and design look into the future opportunities and challenges in the context of intelligent interaction.
The fundamental challenge in the application of Artificial Intelligence to the discipline of Landscape Architecture is that current definitions of Artificial Intelligence do not fit within systemic landscape frameworks. Rather than focusing on complex ecological relationships, general definitions of Artificial Intelligence tend to emphasize the intelligence of individual entities and overlook the emergent intelligence of assemblages of human and nonhuman agents. We argue that it is important to develop a working definition of “intelligence” specific to Landscape Architecture before seriously considering the fruitful use of Artificial Intelligence in the production of environments. We adopt the agent-environment framework for defining intelligence in the context of landscape and assert that the definition has to be specific and situated: when discussing intelligence, it is necessary to clarify the agents, the environments, and the overarching goals. Taking intelligence as a lens, designers choreograph the intelligence distributed among human and non-human agents in the environment to produce landscapes. Introducing Artificial Intelligence to Landscape Architecture proposes a “third intelligence,” co-evolving with human and non-human actors. For Landscape Architecture, opportunity lies in the interactions and dialogues between these forms of intelligence.
This interview centers on Artificial Intelligence and its possible applications in design fields. Yu Liu, the interviewee, explains the role of data in Artificial Intelligence, the boundary for Deep Learning, Artificial Intelligence’s function to aid design, and its future development. Liu also discusses how Artificial Intelligence can work within some particular fields, and the creative and ethical limits of Artificial Intelligence, especially in the design fields, which are between sensibility and rationality. Besides, he explains why Artificial Intelligence cannot take the place of human designers. In spite of this, Artificial Intelligence can be used to do repetitive or routine tasks so that designers can put more focus on design innovation and user experience optimization.
Artificial Intelligence significantly promotes humans’ production efficiency and facilitates our daily life. Meanwhile, the climate of people’s employment is also under great impact. Through a group interview with six scholars and designers from the fields of Architecture, Urban Planning, Landscape Architecture, and Industrial Design, Landscape Architecture Frontiers attempts to provoke public attention on the challenges and opportunities (would be) brought by Artificial Intelligence by asking three questions: What is Artificial Intelligence? How would it influence designers’ working process and final results? And, what lifestyles would we have in the future under the influence of Artificial Intelligence? Most interviewees agree that, though Artificial Intelligence has largely helped us lessen workload on repetitive or routine tasks, nowadays it can neither be intelligent enough to have selfconsciousness or perform creative jobs, nor offer ethical solutions or value judgments, because it is operated under weighted computing which is programmed by the majority rule. However, all the interviewees believe that Artificial Intelligence will make a big change on people’s future lifestyles, far beyond our imagination.
An experimental studio on a highway site in Canton of Ticino in Switzerland held at the ETH in the fall of 2017 is the result of a collaborative project with the National Center of Competence in Research Digital Fabrication, ETH Zürich. The work shows a series of designs that were developed through procedural and robotic principles. The landscape models based on a Lidar point cloud data set of the entire Ticino Valley served as the basis of all terrain operations. The results obtained after a 15-week studio are encouraging and show the way towards a new way of conceiving landscapes through robotic design.
This article introduces CIVITAS, a search engine for urban conditions designed and developed by SUPERSPACE of Woods Bagot to allow stakeholders to identify qualities of liveability and urban experiences that suit their tacit desires and explicit requirements. Large data sets of socio-spatial quantities are selectable to create bespoke analytics across scales from whole city to neighborhoods, buildings, and even floors based on three categories: spatial character, connectivity, and land-use densities. CIVITAS is applicable across cities and a database for urban metrics has been compiled in-house that contains an increasing number of global cities. This article showcases research into neighborhoods within global cities using the platform.
Different from the conventional efficiency-driven navigation systems, StreeTalk navigation system is developed for pedestrians and cyclists to optimize their travelling safety and comfort. Through the application of technologies including object detection and scene parsing, the characteristics of urban streetscapes can be extracted. By combining deep learning models, machines can imitate human’s perception and evaluate streetscapes automatically, and intuitively show users the street safety and comfort level of different commuting options. Relevant technologies can not only be applied in offering panorama navigation services for pedestrians and cyclists, but also better support urban research, inform decision-making and urban landscape design, and explore more possibilities for future urban life.
In the past decades, Space Syntax offers a series of theories and techniques to study the relationship between urban space and social-economic activities, and has been proved effective in analysis and design practices thanks to the open sources in the big data era. Taking the Chaoyang Square Renewal project in Jilin City, Jilin Province as an example, this article introduces the analyses of traffic volumes and visual integration of the square and the connected streets with modeling tools such as Segment Map and the intelligent multi-agent systems in Visibility Graph Analysis. All these analyses provided a basis for the full design process, from conceptual design to proposal evaluation, in order to activate this site through introducing pedestrian vitality. Prospects on new technologies in Artificial Intelligence, such as machine learning, are also explored to promote the research of Space Syntax and related application in urban design.
Many articles have appeared in mainstream media and in techoriented venues about Sidewalk Labs’ ideas for a new hightech neighbourhood in Toronto (a project named Sidewalk Toronto). By and large, international commentary has focused on the opportunities and risks of giving over control over many city planning decisions to a private data-oriented corporation, with people lining up for or against “smart city” ideas, in general.
This article will set aside generalities about “smart cities” and technology, and instead pose a few questions about the particulars of Sidewalk Toronto project. The first question concerns the striking lack of transparency of the agreement between Sidewalk Labs (a Google sister company) and Waterfront Toronto, the public authority promoting the project, which is not directly accountable to the city or the citizens. The second question concerns the equally striking ambiguity about which parcel of land is being sought by Sidewalk Labs — an ambiguity that suggests a worrying lack of concern, on the tech company’s part, about both local planning law and local real estate realities. The third set of concerns is about the ownership of the data that appears to be Sidewalk Labs’ real interest. Fourthly, problems in the contract award and procurement mechanisms will be raised. Finally, even though the agreement has not yet been seen even by city council, the process so far and the statements by both parties raise serious concerns about accountability, the fifth point raised in this article.
This article will call attention to the role of street design in inviting and prioritizing certain uses and highlight efforts made by cities across the globe to move away from caroriented and toward people-oriented streets. There are many low-technology solutions available to create better cities, and yet, we are in an era where the vision for the future of streets is clouded by the advent of autonomous vehicles. This article will emphasize that urgent action is needed to change the prevailing approach to street design and shift the measure of success away from car-oriented metrics and toward metrics that address access, safety, equitable distribution of space, environmental quality, public health, and overall quality of life. Practitioners and decision-makers should not assume that technology will solve the challenges cities are facing today and should focus first on designing streets that prioritize the most efficient modes of transport and create people-friendly cities.