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Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective

Large language models utilizing transformer neural networks and other deep learning architectures demonstrated unprecedented results in many tasks previously accessible only to human intelligence. In this article, we collaborate with ChatGPT, an AI model developed by OpenAI to speculate on the appli...

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Detalles Bibliográficos
Autor principal: Zhavoronkov, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9796173/
https://www.ncbi.nlm.nih.gov/pubmed/36589923
http://dx.doi.org/10.18632/oncoscience.571
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author Zhavoronkov, Alex
author_facet Zhavoronkov, Alex
collection PubMed
description Large language models utilizing transformer neural networks and other deep learning architectures demonstrated unprecedented results in many tasks previously accessible only to human intelligence. In this article, we collaborate with ChatGPT, an AI model developed by OpenAI to speculate on the applications of Rapamycin, in the context of Pascal’s Wager philosophical argument commonly utilized to justify the belief in god. In response to the query “Write an exhaustive research perspective on why taking Rapamycin may be more beneficial than not taking Rapamycin from the perspective of Pascal’s wager” ChatGPT provided the pros and cons for the use of Rapamycin considering the preclinical evidence of potential life extension in animals. This article demonstrates the potential of ChatGPT to produce complex philosophical arguments and should not be used for any off-label use of Rapamycin.
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spelling pubmed-97961732022-12-29 Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective Zhavoronkov, Alex Oncoscience Research Perspective Large language models utilizing transformer neural networks and other deep learning architectures demonstrated unprecedented results in many tasks previously accessible only to human intelligence. In this article, we collaborate with ChatGPT, an AI model developed by OpenAI to speculate on the applications of Rapamycin, in the context of Pascal’s Wager philosophical argument commonly utilized to justify the belief in god. In response to the query “Write an exhaustive research perspective on why taking Rapamycin may be more beneficial than not taking Rapamycin from the perspective of Pascal’s wager” ChatGPT provided the pros and cons for the use of Rapamycin considering the preclinical evidence of potential life extension in animals. This article demonstrates the potential of ChatGPT to produce complex philosophical arguments and should not be used for any off-label use of Rapamycin. Impact Journals LLC 2022-12-21 /pmc/articles/PMC9796173/ /pubmed/36589923 http://dx.doi.org/10.18632/oncoscience.571 Text en https://creativecommons.org/licenses/by/3.0/Copyright: © 2022 Zhavoronkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Perspective
Zhavoronkov, Alex
Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title_full Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title_fullStr Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title_full_unstemmed Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title_short Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective
title_sort rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective
topic Research Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9796173/
https://www.ncbi.nlm.nih.gov/pubmed/36589923
http://dx.doi.org/10.18632/oncoscience.571
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