Astrobiology in the Time of Artificial Intelligence

arXiv:2606.23854 · astro-ph.IM, astro-ph.EP, physics.data-an · Submitted 2026-06-22 · Read on arXiv

Caleb Scharf

astro-ph.IM, astro-ph.EP, physics.data-an

Submitted: 2026-06-22

Comments: 17 pages, no figures. Accepted and published by The Astrobiology Journal, June 2026

DOI: 10.1177/15311074261463025

License: http://creativecommons.org/licenses/by/4.0/

The gist: The Viking missions showcased multiple spaceflight technologies representing state-of-the-art capabilities: from digital line-scan imaging to the operation of complex onboard laboratories and

Terminology

Abstract

The Viking missions showcased multiple spaceflight technologies representing state-of-the-art capabilities: from digital line-scan imaging to the operation of complex onboard laboratories and software-controlled process autonomy. Since Viking, there have been extraordinary, and still accelerating, advancements in computing technology impacting science, society, and exploration. These developments have occurred in both hardware and software, resulting in increasingly capable devices, advanced programming tools, and algorithmic innovations. The subset of artificial intelligence known as machine learning has emerged as one of the most transformative of these developments, with major implications for space exploration and for improvements to the search for evidence of life beyond the Earth. Those improvements include the integration of data across different scales and increased sensitivity to complex features in data, as well as the generation of adaptive strategies for sampling environments. In this paper, the present and future nature of space exploration and astrobiological research is examined through the contextual lens of Viking, and through the history and possible future of artificial intelligence.

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