{"id":37988,"date":"2025-10-14T09:51:10","date_gmt":"2025-10-14T07:51:10","guid":{"rendered":"https:\/\/www.co2news.sk\/?p=37988"},"modified":"2025-10-14T09:52:44","modified_gmt":"2025-10-14T07:52:44","slug":"artificial-intelligence-and-climate-change-potential-and-challenges-of-basic-models","status":"publish","type":"post","link":"https:\/\/www.co2news.sk\/en\/2025\/10\/14\/artificial-intelligence-and-climate-change-potential-and-challenges-of-basic-models\/","title":{"rendered":"Artificial Intelligence and Climate Change: The Potential and Challenges of Basic Models"},"content":{"rendered":"

Artificial intelligence (AI) records rapid progress<\/strong> and its applications range from education and healthcare to areas requiring complex analytics. AI is a broad term describing a variety of useful technologies that mimic human intelligence, including judgment and subsequent decision-making. Recent developments in AI, such as OpenAI\u2019s ChatGPT models, machine vision and deep learning technologies, can be deployed in a variety of contexts, particularly in relation to climate change<\/strong>.<\/p>\n

Particular interest is focused on Foundation Models (FMs)<\/strong>, which can help broaden understanding of climate change and reduce the social risks of adaptation and mitigation initiatives.<\/p>\n

Characteristics and strength of FMs<\/h3>\n

FMs are models trained on large, unlabeled datasets and can be adapted to perform a wide range of contemporary tasks, taking into account a variety of variables. They are enabled primarily through transfer learning<\/strong>, when knowledge gained from one task is applied to another, and through scale<\/strong>The scope is driven by three main features: improved hardware, transformer model architecture, and the availability of extensive training data.<\/p>\n

These models, such as the Generative Pretrained Transformer (GPT), have revolutionized natural language processing (NLP) tasks. They are also successfully used in key sectors such as healthcare (e.g., when working with various combinations of medical data) and business (process optimization).<\/p>\n

Application of FMs in the context of climate change<\/h3>\n

The distinctive features of FMs make them ideal for climate change mitigation and adaptation initiatives. They can flexibly help to better understand key causal relationships in the performance of climate systems. Potential contributions of FMs to adaptation and mitigation include:<\/p>\n

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  1. Data analysis:<\/strong> FMs are capable of processing vast volumes of climate data, including temperature records, atmospheric measurements, and satellite imagery.<\/li>\n
  2. Climate modeling:<\/strong> AI models trained on vast amounts of data can simulate and predict Earth's climate systems, improving resolution and speeding up predictions. These models, such as ClimaX<\/strong>, can support decision-making for policymakers and disaster response teams.<\/li>\n
  3. Risk assessment:<\/strong> FMs can assess climate-related vulnerabilities and risks across different sectors, regions and societies.<\/li>\n
  4. Decision support:<\/strong> They provide policymakers and business managers with evidence-based knowledge that guides policy formulation and strategic management.<\/li>\n<\/ol>\n

    In addition, FMs assist in visual understanding<\/strong> phenomena such as sea level rise, and can increase user engagement<\/strong> by replicating human intelligence, which is useful in raising awareness about the impacts of climate change.<\/p>\n

    Industrial initiatives<\/h3>\n

    Leading technology companies are actively developing FMs for climate change management:<\/p>\n