Open AccessDynamic Cognition Applied to Value Learning in Artificial Intelligence
Author(s) -
Nythamar de Oliveira,
Nicholas Kluge Corrêa
Publication year - 2021
Publication title -
aoristo
Language(s) - English
Resource type - Journals
ISSN - 2526-592X
DOI - 10.48075/aoristo.v4i2.27982
Subject(s) - artificial intelligence , computer science , marketing and artificial intelligence , cognition , prudence , field (mathematics) , value (mathematics) , artificial intelligence, situated approach , humanity , artificial general intelligence , intelligent decision support system , cognitive science , psychology , epistemology , machine learning , mathematics , philosophy , neuroscience , pure mathematics , theology
Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods.