Many cities are looking at how they can employ emerging technologies like machine learning to improve the environment. Carbon reduction programs aimed at the business and residential building sectors are a top priority. Tech Explorist brought this interesting topic to our attention in their article, “Machine Learning can be used to improve energy use in cities.”
Artificial intelligence (AI) is based on machine learning. Machine learning allows AI to grow and adapt to new situations. The machine first learns using teaching algorithms and later achieves self-sufficiency in performance. Machine learning, with its vast potential and capability, may now help specialists in environmental preservation and conservation.
In bigger cities, pollution is a huge problem. Internet of Things (IoT) receives data regarding city pollution from a variety of sources, including automobile emissions, airflow direction, traffic levels, and so on. Following the extraction of all necessary data, machine learning algorithms assess the data while adjusting the appropriate prediction models depending on a variety of factors such as the current season, the city’s various topologies, and so on. Machine learning algorithms can construct pollution estimates for various regions of the city using this study, alerting municipal officials in advance where a problem may arise.
Most organizations have little knowledge about AI systems and as a result, they are unprepared when they see AI results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security, safety, and especially the environment.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.