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Bench Talk for Design Engineers

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Bench Talk for Design Engineers | The Official Blog of Mouser Electronics


Smart Future on the Horizon: Cities Built on AI Wang Dongang

(Source: elenabsl/Shutterstock.com)

Better City, Better Life

In the summer of 2010, Shanghai was host to the 41st World Expo with the theme “Better City, Better Life.” This was an international high point for discussions around cultural exchange, social development, and especially, urban development. According to statistics from the United Nations and the World Bank, the percentage of city dwellers to the overall world population in 2010 was 51 percent, marking a historic shift of population from rural to urban centers. From that point forward, countries all over the world began to see the connection between improving the quality of life in cities to improving the lives of their citizens.

Ten years on, the global proportion of city dwellers has increased from 51 percent to 55 percent. Based on forecasts by the United Nations, this percentage will increase to 68 percent by 2050. City life will therefore become the default state of human civilization. The concentration of populations in cities will, on the one hand, bring many conveniences, but on the other hand, will introduce new challenges in housing, traffic, environmental damage, and resource conservation, to name a few. Many hope that emerging technologies can be used to solve these new challenges that are unique to cities, and this has given birth to the concept of the smart city. As part of the smart city’s conceptual framework, new technologies such as the internet, modernized industry, and artificial intelligence (AI) are hoped to be used to integrate city systems and services, boost the efficiency of resource utilization, and optimize city administration and services. This can help solve the problems faced by cities and improve the quality of life of their residents.

The smart city concept has been developing for more than a decade since IBM first proposed it in 2008. Some preliminary smart applications have already become a part of the everyday lives of city dwellers. Mapping software, such as Google Maps, combines geographical data and actual images of the city and uses algorithms to assist users to understand their city and plan specific routes, all from the comfort of their home. Uber in the US and DiDi in China leverage such services and integrate vehicle and user data with their recommendation algorithms to help users quickly catch a ride. In the field of security, China established its Skynet surveillance camera system in 2017, with more than 200 million cameras being put to its service by 2019. Similar surveillance networks are being quickly deployed in other places worldwide, such as the Domain Awareness System jointly built by the New York City Police Department and Microsoft. It consists of a vast number of cameras and sensors and back-end data processing systems that can be used to constantly monitor and swiftly respond to criminal activity.

These examples of smart city applications already use some AI algorithms such as recommendation algorithms, recognition algorithms, and prediction algorithms. But the vast majority of applications are concentrated around data collection, networking, and information sharing—such as e-government platforms, device remote control, and sensor arrays. As AI technology develops alongside the smart cities it is supporting, this data will be further leveraged through such AI functions as inference, prediction, and decision-making.

Application Scenarios in Smart Cities

With the rise of AI technology in 2012, many new technologies based on deep learning were introduced to help meet the everyday housing and transportation needs of urban residents, to help maintain the sustainability of environmental resources, and to help city administrators more quickly get a handle on information and communicate with residents. This has meant greater convenience and efficiency for urbanites.

Intelligent Transportation Systems

The biggest contributor to an intelligent transportation system has been the advent of autonomous driving systems. When self-driving vehicles become the main mode of urban transportation, it will guarantee better safety and provide greater efficiency as self-driving vehicles use big data and route-planning algorithms to automatically avoid congestion and automatically find optimal driving routes.

With this future in mind, the research and industrialization of self-driving vehicles are now fully underway. Waymo, a subsidiary of Alphabet, issued a self-driving vehicle safety report in October 2020. The report found that Waymo self-driving vehicles had already driven 24.1 billion kilometers on virtual roads and 32 million kilometers of autonomous driving on actual roads. In the 106,000 kilometers of real road testing over the past two years, only 18 actual collisions and 29 virtual collisions were recorded, and most were the result of other drivers not following traffic rules. This shows that autonomous driving technology is already becoming quite mature and can adeptly handle any simple road situation. However, it also shows that the vision for highly intelligent self-driving vehicles has not yet become a reality.

Although the day when self-driving vehicles can truly replace human drivers has not yet arrived, driving assistance technology and road control technology have already become a part of people's daily lives. Examples are technologies that use sensors, cameras, and control technologies to support features such as automatic reverse and parking and warnings about pedestrians, front and rear obstacles, and lane changes. An on-board computer can change the vehicle's path a few seconds ahead of time because of the comprehensive analysis of vehicle speed, distance, and sensor images, and this is a great boon for traffic safety. In terms of the road itself, AI algorithms have already been put to good use controlling traffic lights. The city of Hangzhou, China, tested its urban data brain on some roads in the Xiaoshan District in 2016. With AI algorithms analyzing vehicle data and road surveillance cameras intelligently controlling traffic lights, the speed of traffic was increased by 3 percent to 5 percent and even by 11 percent in some road sections.

Urban Sustainability

Another important role of smart cities is their protection of the urban environment and the optimization of urban resource allocation. AI can also assist in these areas.

This is true for urban electricity supply systems in particular. Urban electricity grids experience different power loads in different seasons, times of day, weather conditions, and regions. AI algorithms can combine this data with knowledge of electrical power to analyze the electrical grid's operating mode. This makes a data-driven health assessment of the grid that includes equipment status, network topology, and real-time operations possible. The health assessment allows operators to monitor and instantly discover problems in the power supply. Power grid equipment, such as power transmission lines and transformers, can also be more frequently monitored. Field robots collect images of equipment, which are analyzed with classification and integration algorithms to quickly discover any equipment failures—such as loose dampers and missing insulators—and risks from factors such as construction work, overgrown trees, and fireworks.

A network of sensors can monitor the urban environment. Barcelona, Spain, for example, installed more than 20,000 wireless sensors around the city to collect data about temperature, humidity, pollution, noise, and traffic flow. In the future, AI algorithms can run classification and regression analysis on this data to predict pollution, weather, and traffic situations. This will help city administrators take the appropriate measures as quickly as possible.

Garbage sorting is another area that can benefit from AI-based monitoring. Forecasts predict that the amount of trash produced by urban residents globally will increase from the current 2 billion tons a year to 3.4 billion tons in 2050. If this household waste were disposed of in landfills, it would displace billions of square meters of soil every year, which would have a massive impact on the world's environment. Intelligent garbage sorting can replace manual work and achieve superior results. Finland's Bin-e smart trash bins first use cameras to capture images of the trash and then use trained algorithms for image identification and physical object detection to analyze the contents of the bin. A mechanical system is finally used to sort and compress the garbage, while the bin's internal sensors can also notify the user and the waste management company to dispose of the waste promptly.

Information Service Systems

In smart cities, information is exchanged more efficiently and with greater transparency between city administrators and city residents. Realizing such benefits requires building the necessary data platforms and the deployment of information technology such as blockchains.

Blockchain technology features distributed storage and multi-party maintenance and is impervious to falsification. This ensures that information is valid and genuine and also increases the efficiency of point-to-point information transfers. Blockchain technology can facilitate the application of AI algorithms. Examples are:

  • The product tracking of intelligent logistics,
  • The data transfer of intelligent security systems and law enforcement systems using private blockchains, and
  • The secure communications in smart homes between Internet of Things (IoT) devices.

These applications depend on the efficient transmission of data guaranteed by blockchain technology.

Blockchains can also be used to protect and share data. In a city's e-government system, city residents can instantly view new or modified government policy, give instant feedback, and see other people’s comments. This will greatly enhance the communication between city administrators and residents. Health data includes various types of private patient information, and medical records are generally only kept on file by hospitals, so they are not easily accessible. With blockchain technology, patients can establish confidential electronic health records that can securely transfer between the patient and hospital in complete form. Blockchains can also help governments and the public respond quickly to sudden public health incidents. In response to the COVID-19 outbreak, the Chinese government introduced a health code system where each person can display their individual health status and see the local exposure risk. Blockchain technology guarantees data security and genuineness for the program, and AI algorithms analyze risk levels. The health code information service system enabled the Chinese government to respond quickly and get control of the pandemic.

Community Services

Intelligent healthcare has always been seen as a natural direction in the development of AI. Microsoft's Healthcare Bot is a chatbot that harnesses natural language processing and speech-recognition technology so that patients can get diagnosis and triage for simple conditions by talking with a chatbot online. In the field of imaging, Chinese companies such as YITUTech and Deepwise have developed intelligent diagnostic systems based on image classification and segmentation to help doctors quickly find tuberculosis and pinpoint cerebral hemorrhages in computed tomography (CT) scans and magnetic resonance imaging (MRI), which enhances diagnosis efficiency.

Smart-home technology will gradually replace traditional appliances in the home. As the IoT becomes more ubiquitous, everything in the house, from traditional home appliances to curtains, doors, and windows, will connect to the home data brain, and smart-voice controllers such as Alexa and Siri will recognize verbal commands and transmit these to the corresponding household devices. AI algorithms can also analyze daily living routines to control home appliances automatically. Smart devices have already begun to find their way into the daily lives of the average person. Take smart cameras as an example. Devices such as Nanit or Cubo AI (USA) integrate scene segmentation, behavior recognition, and facial recognition algorithms to help parents monitor their child's every move from infancy to childhood. They analyze the sleeping position of infants and provide warnings about dangerous situations such as climbing on the furniture or the detection of obstructions over an infant's mouth and nose.

In residential complexes, residents will enjoy conveniences such as intelligent logistics and unmanned supermarkets. Amazon’s warehouses, which are considered the world’s most efficient, use more than 15,000 robots working in 3D warehouses and logistics centers to convey and sort goods quickly. In terms of unmanned supermarkets, two years after Amazon started up operations with its Amazon Go markets, it opened up even bigger unmanned supermarkets called Amazon Go Grocery in 2020, not only increasing the size of the store but also adding more types of products and increasing quantities. Such well-known unmanned supermarkets combine computer vision, sensor technologies, and deep-learning algorithms to monitor the movement and interaction of multiple physical objects simultaneously. This results in the ability to record in detail images and data about each shopper's activities. Shoppers can simply take products off the shelves and place them in their bags without dealing with item scanning and checkout. Customers receive an accurate bill after they exit the supermarket.

Outlook and Challenges for Smart Cities

From this overview of smart city application scenarios, it's clear that AI technology has profoundly changed the relationship that people have with information. Data and information from cities train AI technology, and AI prediction, decision-making, judgment, and modeling can be widely applied across smart cities to serve the daily needs of residents better.

The changes brought to smart cities by the application of AI technology do not stop there. Even the city's basic functions are not immune from changes occurring in this area. AI technology, autonomous driving, and the IoT have changed the way connections are made between physical objects and people and between the physical objects themselves. The allocation of resources within a city and between cities no longer relies solely on manual input and labor. This lowers the costs of transporting goods to individual communities in the city. With the rise of 5G technology and shared office spaces, more and more people will be able to work and handle their affairs near where they live. Cities can naturally develop toward having multiple centers of activity, and each center can become a multi-purpose community that does not have to be either exclusively a residential or commercial area. This lowers the overall costs of getting around the city and also naturally reduces carbon emissions.

Changes will also occur to the types of occupations that the people in cities will be working in. AI technology will handle garbage sorting, traffic control, driving, and checkout, freeing up an abundance of human resources. Meanwhile, these AI technologies will also require large-scale data collection and continuous model training, triggering a need for more data engineers, sensor hardware engineers, and AI engineers. People who have a strong grasp of AI technology will be in great demand as AI is deployed in all sorts of fields such as healthcare, education, information management, construction, and real estate.

Of course, this kind of ideal smart city will not just spring up overnight, nor is it something that can easily be brought into being through top-down planning. AI technology develops cyclically, so city administrators should develop short- and long-term development plans. In the short-term, city administrators should support AI businesses that use deep-learning-based AI technologies to create applications in areas such as transportation, healthcare, and power, thus jointly forming intelligent infrastructure from the bottom up. In the long-term, AI technology will likely see revolutionary advances soon, yet information and data will always be inseparable from it. Therefore, administrators of future smart cities should digitize all city administrative functions and all city-related data. Such digitization will mean that cities will have a virtual replica of the physical city, allowing simulation for urban planning and forecasting for potential incidents. Digitization also builds the data foundation for further application of AI technologies, and it will provide advanced tools for urban planning and city construction.

In addition to AI technology, building smart cities will require developments in other basic technologies. One example is 5G technology, which is set to make a long-lasting impact. It transmits data at speeds that are 20 times faster than what 4G technology can handle, and it supports the simultaneous transmission of data from many different communication devices. The enormous input of data required by AI algorithms can be transferred to the cloud, processed, and instantly returned. This allows for the use of lightweight smart devices that do not need complicated processors. Meanwhile, connecting as much infrastructure equipment as possible to a smart network can finally achieve the Internet of Everything (IoE). Newly installed smart devices can also further promote the city's digitization so that its digitization and smartification can move forward in tandem.

Smart cities will still have some limitations. Massive differences in histories, cultures, planning, and management between cities mean that experience might not be readily replicable. For example, China will need to consider its very high population density and historic landmarks. In contrast, Australia would need to deal with the significant differences between coastal and interior cities. AI algorithms are always influenced by the data they rely on, and the process and results of their work reflect the prejudices of the data source to some degree or another. This requires city administrators and social workers to supervise the algorithms and the data collection to ensure that the results are fair for all segments of society. City residents will also have to relinquish some of their data privacy to enjoy the convenience provided by these algorithms. Therefore, the use of this private data will have to be protected by rigorous standards for data management. The actual environment of the city itself will also be a factor that limits the scope of its development. This means that, while developing big cities, governments should also place importance on building up remote regions and rural areas so that all population centers can enjoy the conveniences brought by AI technology.

Conclusion

Smart cities offer city residents the dream of fast and convenient city life, smart and efficient, and full of hope. This future certainly requires the helping hand of AI technology. Building smart cities will not be something that happens overnight. As AI technology is embedded in cities, residents will be gradually introduced to new concepts and new lifestyles that will not necessarily be easy to accept right away. However, the benefits to human civilization offered by this next great technological revolution are worthwhile.



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Wang Dongang is a PhD candidate in the University of Sydney. His research involves medical imaging, artificial intelligence, neuroscience and video analysis, and he is always devoting to implementing machine learning techniques into applications in daily life. He has published papers in top international conferences including CVPR and ECCV, and he serves as the reviewer for journals including IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions on Multimedia and conferences including AAAI and ICML. He is experienced in developing algorithms in machine learning and computer vision. He has cooperated with companies and institutes in China, US and Australia in projects including multi-view action recognition, road management based on surveillance videos and auto-triage system for brain CT.


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