Astrobiology In The Time of Artificial Intelligence
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.
Caleb Scharf
Comments: 17 pages, no figures. Accepted and published by The Astrobiology Journal, June 2026
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2606.23854 [astro-ph.IM] (or arXiv:2606.23854v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2606.23854
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Related DOI:
https://doi.org/10.1177/15311074261463025
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Submission history
From: Caleb A. Scharf
[v1] Mon, 22 Jun 2026 18:44:25 UTC (119 KB)
https://arxiv.org/abs/2606.23854
Astrobiology, AI,
