The topic of this chapter is artificial intelligence singularity. The artificial intelligence discussed in this chapter is general artificial intelligence that might appear as the result of numerous self-improvement cycles which result in a highly advanced version of the intelligence that people created. Using insights from classical philosophical anthropology into what characteristics are present in live beings author suggests that there are enough similarities between beings which are traditionally considered to be alive and the possible advanced general intelligence to claim that through analogy we should prepare ourselves to accept this hypothetical artificial intelligence as a being which is alive.
- Artificial intelligence
- Philosophical anthropology
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A note is needed here to say that there is no reason to think that advanced AI will have the same structure as human intelligence if it even ever happens, but since it is in human nature to present states of the world in a way that is closest to us, a certain degree of anthropomorphizing is hard to avoid.
In writing this part of the chapter I am heavily relying on Belic’s classical textbook [9, pp. 13–21] and his presentation of the argument for all the things that are being claimed about life in classical PA.
It is worth pointing out that this chapter and the systems of thought presented here are profoundly under the influence of Aristotelian, Thomistic, and scholastic traditions in philosophy.
A question which seems important to me regarding reproduction for which I do not have a clear idea how to answer is: Will an AI offspring be the same as an original AI or will it have elements which separate it from its parents? This question seems tricky because when there are different material elements to different beings, it seems pretty easy to differentiate one individual from another, but what happens when there is no material component needed?.
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Jalšenjak, B. (2020). The Artificial Intelligence Singularity: What It Is and What It Is Not. In: Skansi, S. (eds) Guide to Deep Learning Basics. Springer, Cham. https://doi.org/10.1007/978-3-030-37591-1_10
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Online ISBN: 978-3-030-37591-1