How far are cities from adopting artificial intelligence?

How does artificial intelligence work, and how do we get to the stage where its promotion in cities and public spaces is the next step in the development of smart cities?

Artificial Intelligence Intelligent deployment is more than just choosing a solution

In the context of understanding how artificial intelligence works, there are two important aspects to consider: training and inference. Training is equivalent to teaching children. We train AI systems to recognize things like humans do. By repeatedly showing it images, it can learn and understand different concepts. For example, if we want to analyze traffic patterns or use space efficiently through movement, we need to repeatedly expose the AI ​​to images of buses, taxis, bicycles, etc., in various conditions such as day and night, rain and fog. Through this iterative process, artificial intelligence gradually acquires the ability to accurately identify objects and reaches a certain accuracy.

Once an AI model reaches a mature stage, it is packaged and deployed for inference. Inference is the second part of AI's job, applying learned knowledge to make educated guesses. As real-time data flows in, AI converts visual information into text or other forms of non-image data. This data, along with additional metadata such as timestamps and environmental factors, is processed using logic and business rules.

For efficient inference, high-performance computing is necessary, especially when dealing with complex models or large amounts of data. Due to the time required to process the data, traditional computational methods may not be sufficient. This is where accelerated computing and parallel processing come into play. This advanced computing power allows multiple AI models to run simultaneously. For example, a camera can be equipped with multiple models to detect not only vehicles, but also fire, smoke, fights, accidents, etc. This multi-modal deployment provides multiple conclusions from a single data source and requires significant processing power.

Deployment options include the edge, where high-volume AI systems are placed directly inside or near sensors, or data centers, where multiple cameras are connected to a central point, such as a server. These settings can be found in places such as stadiums or airports. Alternatively, cloud deployment can be chosen when time-critical processing is not required, and data can be transferred to a remote cloud server for analysis.

When it comes to the digital transformation of cities, we see a variety of terms used to describe this transformation, including smart cities, intelligent cities, cognitive cities and green cities. The initial focus is on using information technology to improve efficiency and reduce waste in urban environments, which face infrastructure challenges due to factors such as urbanization and migration. Traditional methods of scaling up infrastructure are proving insufficient, so smarter solutions are needed to optimize limited space and capacity.

The development of technology has played a vital role in this transformation. As connectivity became more ubiquitous, the initial focus was on connecting devices, resulting in IP-enabled solutions. This paved the way for bidirectional interrogation of devices, leading to the Internet of Things, smart devices, and the proliferation of data known as “datafication.” With the huge growth of connected IoT devices, as well as advances in mobile computing, cloud technology, and faster connections such as 4G and 5G, vast amounts of data are becoming available, creating new challenges in how to use data effectively.

Controversies have arisen around the concepts of big data, useful data and wasted data. In the quest for digital transformation, finding ways to extract value from massive amounts of data has become a pressing issue. The conclusion was that relying solely on data scientists to process and analyze data through traditional methods such as business intelligence platforms and query languages ​​such as SQL is not scalable. However, the emergence of edge computing has changed this situation by significantly reducing computing costs. Technologies such as GPU introduce parallel computing and accelerated computing, improving performance by 100 to 1,000 times.

Falling costs and increasing computing power have given rise to deep learning, which can teach machines to process data, no matter its size. Machines will learn how to process and analyze data, eliminating the need for massive labor and instead require ample computing power. The larger the data set, the faster the processing and the more significant the results. We have entered an era where the seemingly impossible can truly be accomplished.

The convergence of technologies such as 5G, deep learning and GPT AI has brought about a revolution. Artificial intelligence is now expected to drive innovation in the next 30, 40, 50 and even 60 years, just as the Internet drove the previous Same for 30 years. Artificial intelligence can now be integrated into a variety of applications, including autonomous vehicles and sensors. This integration requires the collaboration of different components and stakeholders to create a seamless and frictionless experience.

Cities have begun to embrace this technological shift, recognizing the potential of artificial intelligence to solve problems and create value for citizens. The focus has shifted from understanding AI as a concept to exploring its practical applications and impacts. Deploying AI in areas such as traffic management can significantly reduce accidents, sometimes by up to 70% depending on traffic flow and location, while factories can use AI to optimize machine performance, enhance safety and predict maintenance needs. Additionally, AI-assisted self-driving cars can enhance safety by proactively responding to potential risks.

Potential applications range from analyzing how people use roads and spaces, combining visual sensors with air quality monitoring, and integrating data with healthcare and emergency systems. This allows informed decisions to be made, such as dynamically changing traffic light patterns based on air quality and traffic conditions. However, integration into actual city operations involves more than just technical capabilities. It requires developing processes and managing change to ensure comfort and acceptance among city operators and decision-makers.

Cities are at different stages of adoption, with transportation, transport, airports, train stations and highways being key areas of note. Airports can optimize operations, enhance health and safety measures and manage risks by understanding people's behaviour. Train stations can monitor crowds, analyze barrier usage, inspect tracks and ensure health and safety through anonymous analysis. The list of potential applications and use cases is extensive and growing.

AI has the potential to shape innovation over the next half century

Will private sector use cases for AI in private infrastructure, such as airports, mature before public sector use cases?

The maturity of AI use cases in private infrastructure versus public sector use cases depends on the specific application. In the public sector, an example is roadside management, where monitoring and sustainability initiatives play an important role. By installing sensors throughout the city, a comprehensive view of city operations and conditions can be achieved, including waste, crime and traffic. There is growing demand for AI solutions in traffic management, including vehicle or pedestrian monitoring, illegal parking detection and parking lot management.

Public sector initiatives also aim to provide real-time information to citizens and decision-makers. For example, through the use of cameras, available parking spaces can be identified and communicated to citizens via apps or other platforms. Another use case involves waste management, where AI can detect overflowing bins and trigger alerts to the appropriate personnel. Overall, there is an increasing focus on traffic, mobility, sustainability, visual inspections related to urban management and services.

In contrast, the private sector tends to have an easier time adopting AI use cases due to their ROI-driven nature. Private companies are more inclined to invest and scale their AI solutions quickly if the value and benefits can be proven. However, the public sector often seeks value beyond financial returns. It considers how AI can improve services, enhance citizen well-being and promote safety. As a result, public sector procurement and budgeting processes are likely to be longer.

In this space, there are more than 150 startups providing artificial intelligence solutions for these types of use cases. Some start-ups have introduced innovative business models that allow cities to invest in projects in the form of capital expenditure (CapEx) or opt for an operational expenditure (OpEx) model. The OpEx model involves startups deploying and maintaining infrastructure while providing services through a software-as-a-service (SaaS) or data-as-a-service (DaaS) model. These startups focus on selling the value they provide rather than the device itself.

In terms of artificial intelligence, will it be easier to interact with one industry than another? One of them is more actively pursuing artificial intelligence solutions than the other?

In the difference between When it comes to industry AI collaboration, the ease of participation varies by region. Specifically, certain countries in Europe, the Middle East and Africa, such as the UK, Germany, France, Italy and Spain, are actively pursuing AI-powered solutions. Middle Eastern cities, in particular, will look to NVIDIA for specific goals and help achieving them, but this level of involvement is limited to a smaller percentage of cities.

If you consider the technological maturity of a city or the maturity curve of digital transformation, more advanced cities will proactively pursue artificial intelligence solutions. They’ve done their research, read success stories, and are eager to explore further. However, most cities (about 70%) are still in the process of learning AI and may lack the necessary infrastructure and understanding of how to start their AI journey.

Starting your AI journey is more than just buying a box or a solution. Many cities have expressed a desire to use their own data and develop their own models. Some cities have dedicated entities within them, such as IT or AI-driven teams, whose understanding and implementation of AI are mature.

In contrast, there are two other types of cities. The first group understands AI but lacks the resources and expertise to implement it. They look for off-the-shelf, off-the-shelf solutions. The second type of city requires both a better understanding of AI and the necessary resources to implement it. These cities are taking a more moderate and cautious approach, exploring AI solutions at a slower pace. Overall, a large portion of cities fall into the category of needing continuing education and lacking the resources to fully embrace AI.

Cities are at different stages of their AI journey, with as many as 70% still understanding its possibilities

In the past, first-tier cities were generally considered to have more resources, including city governments and Key drivers for local authorities’ interest in AI solutions. However, things have changed, and now the driving force behind AI adoption extends beyond a city’s size or resources. The issue now is talent and leadership.

There is a small town in Germany with a population of about 9,000 people. This town has people with extraordinary intelligence and visionary leadership who understand the value of artificial intelligence and therefore use computer vision technology to scan the entire town and create a digital twin. Sometimes, smaller cities may be more flexible and manageable, making it easier to implement AI solutions than larger, more complex cities.

The deployment of artificial intelligence in cities actually depends on a variety of factors. Talent and leadership that recognize the potential of technology play an important role. However, when we talk about “talent,” it’s important to remember that it’s not just about individuals. We are now seeing cities become first movers by investing in AI platforms and opening up opportunities for innovation and collaboration with universities and research institutions. The main barriers to start-ups and job creation in AI are infrastructure and data access. Forward-looking cities are solving this problem by investing in computing infrastructure through public-private partnerships or other models. The point is not who owns the platform, but the existence of the platform itself. By providing computing resources, providing relevant data, and promoting connections with universities and local communities, these cities are developing many local initiatives and upskilling their workforce to equip them with the skills of the future. This, in turn, creates jobs as start-ups emerge from these efforts.

It’s no longer just a big-city problem that’s igniting interest in artificial intelligence solutions. Cities large and small are actively exploring the potential of artificial intelligence, driven by talent, visionary leadership, and initiatives that foster innovation and collaboration.

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