What AI and Machine learning Can Do for a Smart City?
Artificial Intelligence and Machine Learning algorithms have increasingly become an integral part of several industries. Now they are making their way to smart city initiatives, intending to automate and advance municipal activities and operations at large. Typically, a city when recognized as a smart city means that it is leveraging some kind of internet of things (IoT) and machine learning machinery to glean data from various points.
A smart city has various use cases for AI-driven and IoT-enabled technology, from maintaining a healthier environment to advancing public transport and safety. By leveraging AI and machine learning algorithms, along with IoT, a city can plan for better smart traffic solutions making sure that inhabitants get from one point to another as safely and efficiently as possible. Machine Learning collects data from numerous points and conveys it all to a central server for further implementation and once data is collected, it has to be utilized in making a city smarter.
Solving Urban Issues with AI and Machine Learning
Machine learning generally takes the data generated by several apps such as Health MD applications, internet-enabled cars, etc. and leverages it to identify patterns and learn how to optimize the given set of services. Its tools are able to personalize the smart city experience by aggregating information about the most used roads in a city and then apply it to a transportation system.
On the other hand, machine learning and AI can be helpful in waste collection and its proper management and disposal which is a vital municipal activity in a city. Thus, the technology for smart recycling and waste management provides a sustainable waste management system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives.
In this way, AI-powered computer vision systems, for instance, could enable computers to spot millions of elements of urban life in a chorus, including people, public workers, cars, accidents, fires, disasters, trash and much more. The system allows not only for autonomous monitoring but to make decisions based on the performance of each of these elements, changing behaviors over the course of each day or time, and responses to city systems by each element.
As AI and machine learning are transforming the way cities operate, deliver and maintain public amenities, the technologies come with some drawbacks. Thus, there is a need to consider about retrofitted solutions that can hold the smart city initiatives continuing. So, the current smart city programs using AI and ML seems to advance city services and lives, including transportation, lighting, safety, connectivity, health services, among others.
