MITM3 | Artificial Intelligence and Machine Learning in Planetary Science

MITM3

Artificial Intelligence and Machine Learning in Planetary Science
Co-organized by TP/SB/ODAA
Conveners: Valerio Carruba, Evgeny Smirnov, Dagmara Oszkiewicz | Co-conveners: Bryce Bolin, Safwan Aljbaae, Gabriel Caritá, Antti Penttilä, Hanna Klimczak-Plucińska, Kat Volk, Rita C. Domingos, Hauke Hussmann, Mariela Huaman, Mario D'Amore

Artificial intelligence (AI) refers to the development of computer software capable of performing tasks that would typically require human intelligence. Machine learning (ML) is a branch of computer science that explores algorithms that can learn from data. It is primarily divided into supervised learning, where the algorithm is presented with examples of labeled entries and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, allowing it to autonomously identify structures. Deep learning is a branch of machine learning based on multiple layers of artificial neural networks, which are computing systems inspired by the biological neural networks found in animal brains. This session aims to provide a forum for discussing recent advancements in the applications of AI and ML to planetary science.

Artificial intelligence (AI) refers to the development of computer software capable of performing tasks that would typically require human intelligence. Machine learning (ML) is a branch of computer science that explores algorithms that can learn from data. It is primarily divided into supervised learning, where the algorithm is presented with examples of labeled entries and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, allowing it to autonomously identify structures. Deep learning is a branch of machine learning based on multiple layers of artificial neural networks, which are computing systems inspired by the biological neural networks found in animal brains. This session aims to provide a forum for discussing recent advancements in the applications of AI and ML to planetary science.