IndexIntroductionAI and machine learning used for space explorationNASA uses machine learning for space projectsConclusionIntroductionNASA never goes back to use intelligence artificial and machine learning in the best possible way. Artificial intelligence and machine learning have had a profound influence on a wide range of industries and businesses, where they have paved the way for the automation and optimization of operations, as well as the development of new business opportunities. However, due to rapid advancements, these technological innovations are being used in research and development outside of our atmosphere and into space. Now let's take a quick look at how NASA uses artificial intelligence and machine learning for various space and earth science projects. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayAI and machine learning used for space explorationNASA is constantly advancing artificial intelligence applications for space research, such as automating image analysis for galaxy, planet, and star classification, developing autonomous space probes capable of avoiding space junk without human involvement, using artificial intelligence-based radio technology to make communication networks more effective and free of disturbances. However, creating autonomous landers (robots) that roam the surfaces of other planets is one of NASA's most critical AI applications. Without explicit orders from the control room, these autonomous robots must make judgments and avoid obstacles on uneven terrain while choosing the optimal path. Some of the most significant advances in Mars exploration have relied largely on autonomous robots. The Radiant Earth Foundation and NASA Earth Science Data Systems (ESDS) sponsored a workshop for practitioners in January 2020 to explore the advancement of machine learning (ML) methods on NASA robots. Earth observation (EO) data. The event, which took place in Washington, DC, attracted 51 participants from government agencies, nonprofit groups, universities and commercial companies. The session report (PDF) is now online and highlights challenges, potential solutions, and best practices for integrating EO data into machine learning processes for Earth science research and applications. The Advancing ML Tools for Earth Science workshop attracted 51 people from government agencies, non-profit organizations, universities and the commercial sector. The Radiant Earth Foundation provided this image. Machine learning is a type of artificial intelligence that can learn from data, recognize patterns, and make choices with little or no human interaction. Due to the abundance of publicly available EO data, Earth science fields are particularly well suited to making use of ML. Open data, open source technology, community building, specialized algorithm development studies, and labeled benchmark samples are the building blocks for the mainstream use of machine learning in Earth sciences. To that end, NASA's ESDS program has invested in machine learning-based technology and industry that focus on data-driven science and operational efficiency. There are also plans to create highly curated Earth science education reference datasets that could be used to accelerate advanced computer algorithms and benchmarking. However, they are also there.