2021 digital city:Cities are an engine for human prosperity. By putting people and businesses in close proximity, cities serve as the vital hubs to exchange goods, services, and even ideas. Each year, more and more people move to cities and their surrounding metropolitan areas to take advantage of the opportunities available in these denser spaces.
Technology is essential to make cities work. While putting people in close proximity has certain advantages, there are also costs associated with fitting so many people and related activities into the same place. Whether it’s multistory buildings, aqueducts and water pipes, or lattice-like road networks, cities inspire people to develop new technologies that respond to the urban challenges of their day.
Today, we can see the responses made possible by the advances of the second industrial revolution, namely steel and electricity. Multistory buildings and skyscrapers responded to our demand for proximity to do business in the same locations. Electrified and subterranean railways offered faster travel for more people in tight, urban quarters.
The elevator, escalator, and advanced construction equipment allowed our buildings to grow taller and our subways to burrow deeper. Electric lighting turned our cities, suburbs, and even small towns into 24-hour activity centers. Air conditioning greatly improved livability in warmer locations, unlocking a population boom. Radios and television extended how far we can communicate and the fidelity of the messages we sent.
We are now in the midst of a new industrial era: the digital age. And like the industrial revolutions to precede it, the digital age doesn’t represent a single set of new products. Instead, the digital age represents an entirely new platform on top of which many everyday activities operate. Making all this possible are rapid advances in the power, portability, and price of computing and the emergence of reliable, high-volume digital telecommunications.
Some of the most important developments are taking place in the area of artificial intelligence (AI). At its most essential level, AI is a collection of programmed algorithms to mimic human decisionmaking. Definitions can vary widely on exactly what constitutes AI, what its applications will look like in the real world, the solutions AI applications will provide, and the new challenges those same applications will introduce. What is not in question is the heightened curiosity and eagerness to better understand AI to maximize its value to humanity and our planet.
How AI will function in the built environment certainly fits into that category—and for good reason. Even though AI is still in its infant stages, we already encounter it on a daily basis. When your video conference shifts the microphone to pick up the speaker’s voice, when your smartphone automatically reroutes you around traffic, when your thermostat automatically lowers the air conditioning on a cool day—that’s all AI in action.
This brief explores how AI and related applications can address some of the most pressing challenges facing cities and metropolitan areas. Like every form of technology to proceed it, society must be intentional with the exact challenges we want AI to solve and be considerate of the social groups and industries who stand to benefit from the applications we deliver. While AI is just in its early development, now is the ideal time to bring that intentionality to urban applications.
DEFINING ARTIFICIAL INTELLIGENCE IN AN URBAN CONTEXT
Data has always been central to how practitioners plan, construct, and operate built environment systems. At its core, constructing those physical systems requires extensive knowledge of various engineering, geographic, and design principles, all of which are powered by mathematics. Quantitative information and mathematical principles are essential to successfully bring large-scale projects from their blueprints to physical reality, and that was as true in the ancient world as it is today.
The digital age only intensifies the need to use data to manage the built environment. Seemingly every human activity in the 21st century creates a data trail: business transactions, phone calls and text messages, turn-by-turn navigation. If you own a cellphone, simply moving from neighborhood to neighborhood creates a data trail as you jump from one cell tower to the next.
Meanwhile, the equipment that constructs our buildings and infrastructure is now digitized, many of which can export data wirelessly. The computing industry also continues to innovate, creating ever-more processing power, storage capacity, and analytical software. We’re simply awash in data and processing power.The question is how to how to maximize data’s value.
As the production cost of environmental sensors and network devices continues to drop, the ability to use reliable mobile telecommunications and cloud computing is bringing the concept of the Internet of Things (or IoT) to life. Effectively, IoT represents the systems that will enable sensors deployed across various built environment systems and equipment to speak to one another, increasing both the volume and velocity of data movement and creating new opportunities to interconnect physical operations.
The emerging result is a new kind of data-driven approach to urban management, what many communities commonly refer to as smart city programs. While there is no single definition of a smart city program—and online listicles aside, there’s really no way to judge whether an entire municipality or metropolitan area is “smart”—the common element is the use of interconnected sensors, data management, and analytical platforms to enhance the quality and operation of built environment systems.
This is where artificial intelligence and machine learning come into play. My Brookings colleague Chris Meserole authored a piece that explains machine learning in greater detail, including how statistics inform algorithms’ estimates of probability.
The goal of machine learning is to replicate how humans would assess a given problem set using the best available data, primarily by building a layered network of small, discrete steps into a larger whole known as a neural network. As the algorithms continue to process more and more data, they learn which data better suits a given task. It’s beyond the scope of this brief to describe machine learning in greater detail, but you can learn more through Brookings’s Blueprint for the Future of AI.
In conjunction with machine learning, AI is well-suited to form the analytical foundation of smart city programs. Machine learning can process the enormous data volumes spit-off by built environment systems, creating automated, real-time reactions where appropriate and delivering manageable analytics for humans to consider. And since data volumes will continue to grow exponentially, local governments and their partners will be able to use AI to maximize opportunities from the data deluge. For these reasons, Gartner expects AI to become a critical feature of 30% of smart city applications by 2020, up from just 5% a few years prior.
But AI is relatively worthless without a set of intentional goals to complement it. Organizing, processing, analyzing, and even automatically acting on data is only a secondary set of actions. Instead, the initial task facing the individuals who plan, build, and manage physical systems is to determine the kind of outcomes they want machine-learning algorithms to pursue.