The Application of Artificial Emotions in Artificial Intelligence
- DOI
- 10.2991/assehr.k.220105.090How to use a DOI?
- Keywords
- Artificial Intelligence; Neurophilosophy; Memory; Artificial Emotions
- Abstract
The current research effort on AI is mainly focused on how to more efficiently completing specific tasks. Some incredible achievements can already be seen in navigation, data prediction, personalized advertisement and more. However, researchers seem less motivated to study how AI behaviour can be made more like an actual human. The academic engagement on this specific issue is commonly seen in Philosophy and Cognitive Science. This paper shall attempt to establish a relationship between the human memory system and Artificial Intelligence, which helps us understand how human cognition can be reduced to a computational process. The paper introduces some historical psychologic studies of memory and learning, including Pavlovian Conditioning, H.M., Lashley’s Law of Mass Action. Some basic ideas in the philosophy of mind and neurophilosophy are also discussed to build the argument. The conclusion of this paper suggests that there is far more computational process hidden behind our daily behaviour than what we tend to perceive. From a behavioral standpoint, artificial intelligence is perfectly capable of simulating human actions. It is also worth noting that studying AI as a bionic technology allows a certain level of ignorance. The paper is built on the assumption that a human-like AI is possible without a complete and thorough understanding of brain science.
- Copyright
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yunze Gu PY - 2022 DA - 2022/01/17 TI - The Application of Artificial Emotions in Artificial Intelligence BT - Proceedings of the 2021 International Conference on Social Development and Media Communication (SDMC 2021) PB - Atlantis Press SP - 479 EP - 483 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220105.090 DO - 10.2991/assehr.k.220105.090 ID - Gu2022 ER -