Table of contentsIntroductionAI and machine learning used for space explorationNASA uses machine learning for space projectsConclusionIntroductionNASA never turns back to use AI and machine learning in the best possible way. Artificial intelligence and machine learning have had a profound influence on a wide range of fields and businesses, where they have paved the way for automating and optimizing operations as well as developing new opportunities commercial. However, due to rapid advancements, these technological innovations are being used in research and development outside of our atmosphere and in space. Now let's see how NASA is using AI and machine learning for various space and science projects. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayAI and machine learning used for space explorationNASA is constantly advancing applications of AI for research space, such as automating image analysis for galaxy, planet, and star classification, developing autonomous space probes capable of avoiding space debris without human intervention, using radio-based technology on AI to make communication networks more efficient and without disruptions. 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 trajectory. Some of the most significant advances in Mars exploration rely largely on autonomous robots. The Radiant Earth Foundation and NASA Earth Science Data Systems (ESDS) sponsored a professional workshop in January 2020 to explore advances in machine learning (ML) methods on NASA planets. Earth observation (EO) data. The event, which took place in Washington, DC, attracted 51 participants from government entities, nonprofit groups, universities and commercial enterprises. The session report (PDF) is now online and focuses on challenges, potential solutions, and best practices for integrating EO data into machine learning processes for science research and applications of the Earth. 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 AI 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 the use of ML. Open data, open source technology, community building, specialized algorithm development study, and reference-tagged samples are the building blocks of the mainstream. use of ML in Earth sciences. To this end, NASA's ESDS program has invested in machine learning-based technology and industry that focuses on data-driven science and operational efficiency. It is also planned to create.