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About AI-Astrobiology

Astronomers study Andromeda (Messier 31, or M31) to understand the structure and evolution of our own spiral galaxy, the Milky Way. This new composite image contains data of M31 includes X-rays from Chandra and XMM-Newton, ultraviolet data from GALEX, optical data from astrophotographers using ground-based telescopes, infrared data from Spitzer, IRAS, COBE, Planck and Herschel, and radio data from the Westerbork Synthesis Radio Telescope. Each type of light reveals new information about this close galactic relative to the Milky Way.

AI-Astrobiology is an online resource and community hub to support the development and application of Artificial Intelligence/Machine Learning (AI/ML) tools across the areas of study within astrobiology. This effort is supported by the NASA Astrobiology Program and hosted by the Exobiology Branch at the NASA Ames Research Center in Mountain View, California.

The search for life in the universe and an understanding of the origin, evolution, and future of life involves a complex hierarchy of scientific areas in pursuit of an even more complex hierarchy of phenomena; from the complexity of molecular evolution to the integrated nature of livings systems and environments that span from the very local to planet-wide and to the breadth of stellar systems and galaxies.

Many of the theoretical questions and experimental and observational challenges of astrobiology call for scientific tools beyond the traditional. AI/ML approaches can provide those tools with capabilities in decoding and modeling highly non-linear correlative properties and high dimensionality/feature number in data.

Although AI/ML tool frameworks are increasingly accessible as off-the-shelf or open-source software, their choice and application involve a very steep human learning curve and the development of hands-on experience and skills to optimize tool structures and understand their limitations.

AI-Astrobiology is a ‘living’ resource that focuses on the needs of the astrobiology community by providing collated and curated information on AI/ML applications, learning materials, software resources, training data resources, and current literature relevant to the field.