AI enables more sustainable practices and policies, improving the lives of citizens. Learn how smart cities are using AI to enhance environmental stewardship.
In the arid landscape of ancient Sumer, a city called Uruk once rose to become one of the earliest known cities of modern civilization. This major center of Sumerian civilization was known for its advanced urban planning, sophisticated architecture and innovations in writing, agriculture and trade. The city was surrounded by massive walls and featured grand public buildings and temples, and a complex system of canals and irrigation. The growth and success of Uruk marked a significant milestone in human history, setting the stage for the development of urbanization and civilization.
Throughout history, cities like Uruk have been at the forefront of change and innovation. Modern cities continue to be centers of economic activity, knowledge generation, innovation and new technologies. The current response to global climate change is no exception. Cities are in a prime position to test new, more sustainable ways of working and living. Not only do they account for more than 70% of global CO2 emissions1, they are also home to more than half of the world’s population. To help solve the global climate crisis, more than 700 cities around the world have already committed to drastically reducing greenhouse gas emissions, aiming for net zero emissions by 20501).
These cities have taken the opportunity to enhance sustainability as a means to improve the way their citizens live, work and interact with each other — all while reducing emissions, creating new jobs and transforming city operations. But this is no easy task. Modern cities have evolved into massively interconnected networks of people, systems and processes. Many are using technology to become what we call “smart” or “digital” cities, accelerating their transformation into dynamic hubs of sustainability, efficiency and connectivity.
With the increased adoption of artificial intelligence (AI) across industries, cities can benefit massively by harnessing the power of complex AI algorithms to adopt a plethora of new solutions resulting in enhanced efficiencies in resource consumptions as well as improving citizen lives. AI-based data-driven decision-making can be leveraged to automate a wide variety of use cases in this regard, leading to higher optimizations with fewer human interventions.
Key sustainability use cases
With the AI era upon us, smart cities can harness their power for a wide variety of use cases. These use cases are not theoretical. Leading smart cities are already incorporating AI to make their cities even smarter. Some examples include:
- Singapore uses AI to optimize public transport, identifying opportunities to enhance traffic flow and reduce emissions from vehicles idling or taking longer routes to avoid traffic.
- Las Vegas is using AI-enabled urban digital twins to optimize energy use and enhance operating efficiencies in smart buildings.
- Ålesund, Norway is using AI-based urban digital twin visualization to highlight opportunities for emission reduction and offsets such as creating green spaces, optimizing traffic flows and identifying sustainable physical infrastructure projects.
How to adopt AI-enabled smart city use cases
Smart cities need to be smart about laying the foundation for their selected use cases. Considerations include:
- Focus on outcomes: Enthusiasm and adoption increase when people can understand the impact of the investment, for example, mobility-related use cases that lead to faster commute times and less pollution.
- Calculate the full costs: Creating AI at city scale requires a substantial investment. Once in use, AI workloads with powerful GPUs can consume more energy than a typical server. These costs need to be compared to the expected benefits.
- Create polices around ethical use: Cities need to craft policies that govern the use of AI, including how citizen data will be used and protected, how to avoid AI model bias and how to reduce the risk of AI model hallucinations.
AI-enabled smart city solution components
Once the considerations have been addressed, city IT teams can tackle the process of selecting AI infrastructure and related architectures. AI architectures comprise three layers:
- Data layer: This layer includes data sources and components needed to process the ingested data and the persistence layer to store the data for analysis.
- AI training layer: This layer includes model development tools, libraries, frameworks and training engines. Pretrained large language models (LLMs) can be used as a starting point for fine-tuning on specific data.
- AI inference layer: This layer includes the end-user applications that leverage the trained models.
A modular design built on hardware optimized for the specific use case — including support for GPUs — that is compact and efficient to reduce power and space requirements is ideal. Cities also need to deploy edge computing solutions to aggregate and analyze real-time data from devices and sensors at remote locations. And the entire city IT landscape needs to be protected with Zero Trust principles from the data center to edge and cloud.
Partner with an industry leader in sustainable AI
Working with a partner can considerably reduce the burden on city IT teams. Dell Technologies is ready for the age of AI-enabled smart cities with a portfolio of acceleration-optimized, purpose-built servers and solutions for AI and generative AI (GenAI). Built-in management and security and modular and scalable validated designs, and pretrained LLMs like Hugging Face and Meta complete the picture. Plus, the Dell Technologies infrastructure is power- and space-optimized to reduce both costs and carbon emissions.
1)Regeneration, Net Zero Cities, accessed September 2024.
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Autor(en)/Author(s): Vijay Gadwal
Quelle/Source: Forbes, 22.09.2024
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