Dynamic Cognition Applied to Value Learning in Artificial Intelligence
- URL: http://arxiv.org/abs/2005.05538v6
- Date: Tue, 24 Aug 2021 01:15:40 GMT
- Title: Dynamic Cognition Applied to Value Learning in Artificial Intelligence
- Authors: Nythamar de Oliveira and Nicholas Kluge Corr\^ea
- Abstract summary: Several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence.
It is of utmost importance that artificial intelligent agents have their values aligned with human values.
A possible approach to this problem would be to use theoretical models such as SED.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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. A solution to this problem would be the
dynamic cognitive approach proposed by Dreyfus, whose phenomenological
philosophy defends that the human experience of being-in-the-world cannot be
represented by the symbolic or connectionist cognitive methods. A possible
approach to this problem would be to use theoretical models such as SED
(situated embodied dynamics) to address the values learning problem in AI.
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