Harnessing Applied AI for a smart Digital City (6. juni 2023)
The Institute for Energy Technology (IFE) has long been at the forefront of utilizing data-driven methods in the industry to address operational challenges and provide decision support. With a wealth of experience in validating and verifying these methods within specific contexts, IFE has become a leading institution in applied AI. By incorporating state-of-the-art technical solutions and methods, along with extensive experiments to understand human perception of new technologies, IFE continues to push the boundaries of applied AI.
What is Applied AI?
Applied AI involves leveraging artificial intelligence technologies to solve specific problems and address real-world challenges. Unlike basic research, which seeks to understand the nature of phenomena, applied research focuses on applying technology to provide practical solutions. Applied AI raises crucial questions concerning efficiency, regulation, privacy, and ethical considerations, exploring whether implementing a solution is both smart and right.
IFE’s Expertise in Applied AI
IFE’s expertise lies in the effective implementation of data-driven methods in various industrial settings. The institute’s deep understanding of maintenance and decision support enables them to develop robust solutions that enhance operational efficiency. By drawing on their experience and knowledge, IFE ensures that data-driven approaches can be successfully applied and validated within specific contexts.
Applied AI in the Context of a Smart City
In a smart, digital, modern city, applied AI plays a pivotal role in transforming urban landscapes and enhancing the lives of residents. With its ability to make accurate predictions and suggest optimal solutions, applied AI becomes a valuable tool for city planners and infrastructure management. By analyzing patterns from vast amounts of data, machine learning models can effectively anticipate the needs of both individuals and the city’s infrastructure, allowing for proactive decision-making.
Efficient Resource Allocation
Applied AI empowers city administrators to optimize resource allocation and solve complex challenges. For example, by using machine learning models to predict traffic density at intersections, cities can make informed decisions on traffic management, ensuring the smooth flow of vehicles, pedestrians, and cyclists. Furthermore, AI can help distribute the load and address issues such as optimizing public transportation routes to avoid overcrowding, guaranteeing timely arrivals, and reducing congestion.
Sustainable Urban Development
AI’s relevance in a city extends to environmental sustainability. By leveraging data-driven insights, cities can make informed decisions to reduce emissions and combat air pollution. Machine learning models can identify patterns and recommend strategies to minimize smog during peak periods. Additionally, AI can optimize energy consumption, aiding in the prediction and efficient distribution of electricity, ultimately contributing to a greener and more sustainable city.
Modifying Citizen Behavior
Applied AI’s utilization of big data enables city authorities to effectively influence citizen behavior, fostering positive changes. For instance, by adjusting toll charges based on traffic conditions and increasing waiting times at traffic lights, cities can encourage residents to reduce car usage and promote alternative modes of transportation. Data-driven approaches enable cities to create targeted interventions that incentivize sustainable practices and reduce environmental impact.
A good partner to have
IFE’s capabilities in applied AI makes us a valuable asset in the development of smart, digital cities. Through our extensive experience in data-driven methods and the validation of technologies, IFE is well-equipped to address operational challenges and provide decision support in various industrial sectors. By leveraging the power of applied AI, cities can enhance efficiency, promote sustainability, and improve the overall quality of life for their residents.