If a city is called a smart city it is generally given that it has some kind of IoT and ML machinery in place collects data from various points and sends it all to a central server for further implementation. So, once this data is collected it has to be used in some way.
Consider that information can be used to spot trends when you have enough of it, as seen with big data. This means you can get a pretty clear picture of whatever you want to explore with these tools under your command. For example, having enough information about the grid system of roads in a city can help you eliminate the bottlenecks which can, in turn, reduce city traffic and pollution making it more sustainable and clean.
Art of decision making in artificial intelligence and machine learning
The ability to take optimization decisions can be greatly improved by using machine learning. Machine learning, a subset of artificial intelligence, takes the data generated by apps such as Pedometer, Health MD applications, and internet-enabled cars and uses this data to spot patterns and learn how to optimize the given set of services.
Consider an example of IoT development companies such as NVIDIA. NVIDIA developed smart videos to handle big data analytics and applied machine learning to video streams.
Recently, NVIDIA has partnered with 50 AI smart city partners to utilize technology and improve areas such as road transportation. This system is expected to replace human interpretation by replacing it with ML with increased accuracy and speed. This city brain will process a lot of people’s personal data, including their movements.
As written above, machine learning requires loads of data to process and interpret. This analysis of big data gives the city services important information that will help them serve the people better across different areas of service. One such field of service that is being explored is the personalization of services.
How AI and ML will personalize smart city experiences
Machine learning tools for personalization are already used in marketing. In this space, they are used to predict products one individual might like by connecting it with other similar products through algorithms such as KNN and K-Means.
The same can also be done for smart cities in a bit different implementation. For example, one can aggregate information about the most used roads in a city and then implement a transportation system that aims to divert surplus traffic from the busy roads during rush hours. In this and other similar ways IoT, big data and ML can have great potential in the implementation of smart city projects all over the world.
Even though the process and application of machine learning and artificial intelligence in a smart city seem good enough, they come with drawbacks. One major concern that is always looming over the heads of citizens is the concern of bias and privacy protection.
Disadvantages of ML and AI-powered smart cities
AI and ML tools can promote or even unintentionally spread bias that was accidentally skewed into the system.
For example, let us take the example of the Microsoft Tay Chatbot which was trained using Tweets from people. So when people started to use abusive and racist comments against the chatbot, it started to reciprocate the feeling. Although this might seem trivial, when such technologies are implemented in places like law enforcement, citizens will face the biggest threat from law enforcers themselves.
Similarly, one more concern is privacy protection. This is not rocket science. Since so much personal and private data is/will be processed through various systems of the city, there is always the fear of data leaks or data burglary leading to casualties of private space.
Not only this, if things such as political leanings, beliefs, and views of common folk come into the viewing of those in power, this boon can quickly become a bane by becoming a tool for suppression of voice in society.
Although the implementation of ML and AI seems to ease and improve city services and life in general for people, the consequences of a wrongful or even mistaken use should never be overlooked or downplayed.
Still, in its infancy, smart cities need to build a system of checks so that there is no room for a threat to privacy or bias from within the system.