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The search for extraterrestrial intelligence (SETI) is a field that has longbeen within the domain of traditional signal processing techniques. However,with the advent of powerful generative AI models, such as GPT-3, we are nowable to explore new ways of analyzing SETI data and potentially uncoverpreviously hidden signals. In this work, we present a novel approach for usinggenerative AI to analyze SETI data, with focus on data processing and machinelearning techniques. Our proposed method uses a combination of deep learningand generative models to analyze radio telescope data, with the goal ofidentifying potential signals from extraterrestrial civilizations. We alsodiscuss the challenges and limitations of using generative AI in SETI, as wellas potential future directions for this research. Our findings suggest thatgenerative AI has the potential to significantly improve the efficiency andeffectiveness of the search for extraterrestrial intelligence, and we encouragefurther exploration of this approach in the SETI community. (Disclosure: Forthe purpose of demonstration, the abstract and title were generated by ChatGPTand slightly modified by the lead author.

Exploring the Use of Generative AI in the Search for Extraterrestrial Intelligence (SETI)