Modelos din\^amicos aplicados \`a aprendizagem de valores em intelig\^encia artificial

TL;DR

This paper discusses the importance of aligning AI systems with human values and explores dynamic, phenomenological approaches like SED for learning values in artificial intelligence.

Contribution

It proposes a dynamic, phenomenological framework for addressing the challenge of value learning in AI, moving beyond traditional symbolic methods.

Findings

Highlights the limitations of symbolic and connectionist methods in value learning

Suggests the use of situated embodied dynamics (SED) as a promising approach

Emphasizes the importance of aligning AI with human values for societal safety

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 is not made prudently and critically, reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we really want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. 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 human moral values 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 approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. 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.