The American Planning Association’s Planning with the Artificial Intelligence Report defines AI as “the use of computers to mimic or improve human intelligence processes such as inference and empirical learning.” At its core, an AI program takes input (usually a large data set), analyzes it according to a set of rules, and generates output that includes decision-making and task execution.
The ideas behind AI date back to the ancient concepts of living statues and automata. By the Renaissance era, people were building rudimentary robots that could play music, write poetry, and draw and perform other tasks. Important distinctions: These automata carried out physical human actions, but they did not attempt to replicate human thoughts. By the 1950s, researchers had been planning “thinking machines,” but it took another decades for high-tech companies to fund and accept AI. Today, the field of AI covers a wide range of analytical methods that touch on many aspects of modern life.
“Weak AI” or artificial narrow intelligence (ANI) mechanizes limited human thought or behavior patterns, and generally lacks the capacity to store memory and data. An example is a computer that plays chess. “Strong AI” or artificial general information (AGI) is trained to learn about human behavior and perform self-assessment and error correction naturally. An example is a Level 5 autonomous vehicle that can handle all driving scenarios. Most experts say True Agi is still decades away. Common current uses of AI include chatbots and voice-controlled virtual assistants, and generator AI programs that create images or lighting based on existing information. This is an area that is heated by writers, artists, and people from other creative fields.
AI contains four important subfields:
Machine learning (ML) is defined by computers that detect and “learn” patterns as part of a decision-making process (for example, text analysis that detects the tone of a message) (ANN). Computers that interpret visual information (e.g. raw image processing that converts satellite images into maps)
Planning Application
By supporting labor-intensive analysis and research, AI can provide useful tools to enhance planning practices, allowing planners to focus on the “human elements” of their work. AI can process and organize large amounts of data, identify trends and patterns, and make more informed decisions. AI can also be used to improve or automate urban services such as traffic management, electrical grid management, and public health. The so-called Internet of Things (IoT) allows cities to collect more data than ever, using a variety of sensors and devices. This information can often be used to optimize real-time urban infrastructure such as energy grids and parking meters.
Cities use machine learning to map and catalog resources such as trees, water infrastructure, park access, and zoning districts to better understand spatial patterns and reveal areas with discrimination and underserved service. Natural language processing can be used to parse public comments and understand community sentiment. For example, cities can collect large amounts of feedback through public surveys and use AI to scan answers for positive, negative, or neutral language indicators.
Transportation plans can stand to benefit most from AI, mainly because the field generates many concrete data such as vehicle numbers and transit riderships. Transport planners use neural network technology to combine land use information, topography and other data to assess the best routes and design street alignments. The same technology applies to disaster and evacuation route planning, environmental impacts and road safety. However, transportation planners must also address the introduction of self-driving and delivery vehicles that require new levels of regulations and infrastructure.
assignment
AI can be a powerful tool, but cities need to be strategic in AI adoption in planning and operation. Like humans who get experience, AI is not only unaware of bias, error, and misuse, but biases present in human systems are burned into the technology itself. Due to its nature to rely on existing data to make decisions, AI can replicate not only risks, but historical patterns of inequality, discrimination and exclusion, and perpetuate harmful consequences. Planners should pay particular attention to how inputs and conditions are curated, and use broader knowledge to contextualize the information.
AI also presents privacy concerns about personal data, which presents transparency about how and why urban institutions use AI and how information on data protection safeguards is key to the general acceptance of technology. Furthermore, the complex nature of AI algorithms prevents us from understanding how we reached decisions, and accountability challenges are challenged in decision making.
It is important for planners to have a strong understanding of AI’s capabilities and their unique goals for using it. To put a seat at the table in a discussion about how AI is implemented, planners need to understand how it can help their needs and how to use it. Planners also need to be aware of the vast amount of data needed to effectively engage AI. This may not be particularly small cities with low resources. AI applications often require large amounts of energy and bandwidth, which can raise significant cost barriers.
Using AI tools, planners can quickly generate multiple design options for their projects to enhance public participation, or imagine potential scenarios to help them make more informed decisions. However, note that experts rely on rapidly produced visuals who believe in the complex reality of the project.
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