Cities face urgent challenges: mobility, energy, security. In response, neural networks, inspired by the human brain, promise to become the “thinking” of smart cities, analyzing data in real time to make decisions and anticipate problems.
This revolution promises efficiency, but it poses serious ethical and privacy dilemmas.
How can neural networks transform cities?
By: Gabriel E. Levy B.
Modern cities, with their tall buildings, congested avenues and millions of inhabitants, have become veritable labyrinths of needs and challenges.
Today, in the face of problems such as traffic, energy management, safety and environmental care, there is a need to find solutions that not only solve specific problems, but can also anticipate the needs of the future.
This is where neural networks, a technology inspired by the functioning of the human brain, begin to play a decisive role.
These networks, capable of learning from the information they receive and identifying patterns in complex data, act as a “digital brain” that helps cities make decisions in real time.
This allows, for example, to improve traffic flow, adjust energy consumption or even reinforce security in conflict areas.
But the incorporation of artificial intelligence into urban life also brings new questions and challenges: to what extent does a city that “thinks” and makes decisions affect our daily lives? What happens to our privacy in an environment where every move is monitored and analyzed?
Smart cities promise more efficient management, but they pose a dilemma about how far we want to give in order to gain comfort and security.
Smart architecture as a response to accelerated urbanization
With more than 55% of the world’s population living in urban areas and projections indicating that this percentage will exceed 68% by 2050, according to UN data, cities must adapt quickly and efficiently. Neural networks help to respond to this demand.
According to the expert Carlo Ratti, director of the MIT Senseable City Lab, “a smart city is one that learns from its inhabitants, allowing infrastructures and services to respond dynamically to their needs”.
Under this premise, neural networks become the engine that makes this adaptability possible.
These systems can analyze traffic patterns to optimize traffic lights in real time, forecast energy demands, and manage power supply efficiently, or identify areas with increased crime to optimize surveillance.
The ability to predict behaviors allows cities to allocate their resources more efficiently, reducing costs and improving quality of life.
By achieving integration between neural networks and urban services, cities such as Singapore and Seoul have developed platforms that coordinate everything from traffic to environmental monitoring, with a precision that improves the lives of their inhabitants in a palpable way.
In addition, neural networks can also learn from their own mistakes. Through feedback algorithms, these systems are able to adjust their predictions and improve over time, making them a unique tool for dealing with changing issues such as population growth or climate change. Unlike traditional systems, which require constant and costly adjustments, neural networks are inherently adaptive, adjusting to the needs and circumstances of the environment.
Artificial intelligence and privacy: the new urban dilemma
However, this integration of neural networks in cities also raises ethical and privacy questions. As philosopher and technologist Evgeny Morozov warns, smart cities can be transformed into “surveilled cities” where every movement is monitored and recorded.
This is because AI systems require a massive amount of personal data to function effectively, leading to a tension between urban efficiency and citizens’ privacy.
A clear example of this problem can be found in the experience of London.
The city has implemented neural networks to manage traffic and improve security, but at the same time it has deployed thousands of facial recognition cameras, which has sparked a debate about the limits of surveillance.
While these neural networks make it possible to predict behaviors that could reduce crime, the impact on individual freedom is also questioned. To what extent can smart cities collect data without invading the private lives of their inhabitants? This is a debate that has been raised by experts such as researcher Adam Greenfield, who argues that “the promise of technological efficiency can backfire when citizens feel that their freedoms are at risk.”
In turn, the use of neural networks to predict criminal or traffic behavior poses the risk of falling into prejudice, since these systems learn from historical data that may contain biases.
Surveillance and security systems could, for example, generate risk profiles based on patterns that, in some cases, perpetuate social or economic stereotypes.
In this sense, neural networks in smart cities are not exempt from the ethical and social challenges faced by other applications of artificial intelligence.
Success stories in the application of urban neural networks
Several cities have taken the lead in integrating neural networks into their urban systems, with remarkable results.
In Singapore, for example, an AI-based traffic management system has been implemented that regulates vehicular flow in real time, adjusting traffic lights and redirecting traffic according to road conditions.
This model has made it possible to reduce congestion at peak times and has been imitated by cities such as Dubai and Seoul.
In New York, neural networks have been integrated into the waste management system to optimize garbage collection.
Through algorithms that monitor the fill level of the containers, the city has been able to reduce unnecessary trips by garbage trucks, which translates into energy savings and a decrease in carbon emissions.
Similarly, the city of Los Angeles has used neural networks to forecast energy consumption and adjust electricity distribution based on hourly demands, thus achieving a more efficient and less failure-prone supply system.
In Amsterdam, artificial intelligence has been applied to monitor the pollution of its canals. Thanks to sensors that collect data in real time and neural networks that interpret this information, the city can make quick decisions to maintain water quality, an essential resource in a city crossed by waterways.
In conclusion, neural networks have become the brains behind smart cities, transforming data into decisions that directly impact urban life. However, the challenge is not only technological, but also ethical.
When integrating artificial intelligence into urban design, cities must balance efficiency and privacy, ensuring that the pursuit of innovation does not compromise the rights of their inhabitants.
