Description: Grounded Learning is a machine learning approach that emphasizes the importance of real-world context in model training. This approach focuses on the idea that artificial intelligence (AI) models should be trained not only with data but also by considering the circumstances and conditions in which they will be applied. This means that learning occurs in environments that reflect real situations, allowing models to generalize better and adapt to variations in data. By integrating context, the aim is to improve the accuracy and relevance of predictions made by models. This approach is particularly relevant in various AI applications, where devices or systems process data locally and must make real-time decisions based on contextual information. In this sense, Grounded Learning becomes a key tool for developing more robust and efficient AI systems capable of operating in dynamic and changing environments, where information may be limited or incomplete. The ability of a model to learn from its immediate environment and adapt to new situations is fundamental to its success in practical applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
Description: Grounded Learning is a machine learning approach that emphasizes the importance of real-world context in model training. This approach focuses on the idea that artificial intelligence (AI) models should be trained not only with data but also by considering the circumstances and conditions in which they will be applied. This means that learning occurs […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"glossary-categories":[12178],"glossary-tags":[13134],"glossary-languages":[],"class_list":["post-198307","glossary","type-glossary","status-publish","hentry","glossary-categories-edge-ai-en","glossary-tags-edge-ai-en"],"post_title":"Grounded Learning ","post_content":"Description: Grounded Learning is a machine learning approach that emphasizes the importance of real-world context in model training. This approach focuses on the idea that artificial intelligence (AI) models should be trained not only with data but also by considering the circumstances and conditions in which they will be applied. This means that learning occurs in environments that reflect real situations, allowing models to generalize better and adapt to variations in data. By integrating context, the aim is to improve the accuracy and relevance of predictions made by models. This approach is particularly relevant in various AI applications, where devices or systems process data locally and must make real-time decisions based on contextual information. In this sense, Grounded Learning becomes a key tool for developing more robust and efficient AI systems capable of operating in dynamic and changing environments, where information may be limited or incomplete. The ability of a model to learn from its immediate environment and adapt to new situations is fundamental to its success in practical applications.","yoast_head":"\n