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AI As A Tool To Overcome The Challenge Of Climate Change

GPA, Climate Change AI, and the Center for AI & Climate join forces to find AI-powered tools capable of slowing down climate change and reversing its trend. AI is one of the most advanced technological tools in the toolbox of researchers studying new methods to optimize the impact of human activities on the climate. In the contemporary context, it is urgently necessary to find practical solutions for climate change, the recommendations argued in the recent paper ” Climate Change, and AI ” promoted by the Global Partnership on AI Report (GPAI) are more appropriate than ever.

What AI Can Do For Climate Change

The responsible use of AI in the context of climate change is the keystone in developing new processes for activities with a strong environmental impact. But what can climate AI do in practice?

Maximize The Production Of Sustainable Energy Production Plants

The predictive models generated by neural networks are helpful for designers in evaluating the locations and layout of photovoltaic and wind power plants. During the useful life of a plant, advanced maintenance technologies such as SmartDrones and-powered monitoring consoles keep production efficiency high.

Monitor And Model Deforestation

The satellite maps on the visible spectrum and multi-spectrographic contain exciting data on the evolution of deforestation. Machine learning is particularly effective in transforming this data into predictive information and models.

Maximize The Energy Efficiency Of Buildings

Artificial intelligence helps achieve the best energy efficiency of buildings in the design and construction phases, predicting changes in heat flows as the materials and techniques change. Machine learning applied to the IoT guarantees substantial energy savings, learning new methods to avoid waste from consumer habits.

Create Reliable Evolutionary Models Of Climate Change

Many factors contribute to climate change. Identifying them and measuring their influence through simulations developed by AI allows researchers to understand the triggering phenomena better. These models also make it possible to associate initiatives relevant to climate change with the evolution of the practical situation.

The Tools For Developing AI-Powered Applications

The paper mentioned at the beginning provides a complete analysis of the tools necessary to support the development of AI-powered applications for climate change mitigation, supporting the recommendations with appropriate use cases. Enabling this transition means providing simplified access to data and hardware infrastructure for computations. Information sharing must be managed through open source portals that provide experts with all the tools necessary to make artificial intelligence evolve in support of climate-related analyses. 

Access to data alone is not enough. The various governments must guarantee sufficient funds for interdisciplinary research oriented to climate study. This research must be supported by an international collaboration between universities and various research centers worldwide to multiply the speed of evolution of intelligent systems. However, all these research projects cannot remain relegated to the academic field. The development of technologies must go hand in hand with integrating these systems into production environments. Starting from producing renewable energy and passing through intelligent cities, smart buildings, and IoT.

The Energy Consumption Of Neural Networks

The evolution of neural networks aims not only to improve their performance but also to make them more efficient, reducing their negative impact on climate change. Machine learning training methods are inefficient, requiring millions of iterations to learn a single characteristic of the data. According to an analysis by openAI, the amount of computational resources needed for training has increased by 300,000% from 2012 to today. 

An unsustainable rhythm leads to the massive consumption of electricity even for developing a single deep neural network. The future cannot and must not be linked to the current conception of artificial intelligence but to its evolution, a responsive learning system optimized in the consumption of computational resources and able to provide us with more tools to guarantee a future for our planet.

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