무위와 불교의 중도, 그리고 인공지능의 알고리즘에 관한 고찰

Abstract

The purpose of this paper is to consider the premise that there will be a strategic framework for a shared way of thinking among the Muwi(無爲) thoughts of the Tao Te Ching, which play a role in the ancient East Asian way of thinking, and the three fundamental truths of the Middle Path and the Bayes Theorem of the artificial intelligence algorithm. It is analyzed and suggested that the Buddhist system of thinking in the Samje Weonyung(三諦圓融) is in close contact with the ideology of the Tao Te Ching and the Bayes Theorem, one of the learning algorithms of artificial intelligence. This is an attempt to explore the convergent implications of the Buddhist way of thinking inherent in the scriptures represented by Yeosiamun(如是我聞), that is to say, from the current perspective, it is an attempt to reconstruct the view of the Hearing(聞) of Yeosiamun(如是我聞) into the wheel of the dharmacakra-pravartana(如是我轉) in line with the innovation of modern science and technology, as well as to reveal the strategic value of the Middle Path with the manifestation of Yeosiajeon(如是我顯). Based on those recognition, this study newly present the strategic value of the Buddhist thinking system from two perspectives. First, by revealing that in light of Chapter 1 of the Tao Te Ching, the Tao(道) is referred to as having the characteristics of Ga(可), and it is clarified that Tao's properties are variable, and that Tao operates as a significant interdependence between Mu(無) as a potential world and Yu(有) as a phenomenal world. In this way, it is judged that the principle of operation of Tao(道), which works as an interdependence between Mu(無) and Yu(有), can be easily passed on as a thinking system of the Samje Weonyung(三諦圓融). Tao(道) is defined as a Ga(可), that is, change itself, which is equivalent to the principle of Muwi-MuBulwi(無爲無不爲). It is reasonable to estimate that it also can be interpreted as the three principles of the Middle Path that play a major rule as a strategic framework for the Buddhist thinking system. Second, Bays Theorem, one of the AI algorithms defined as another consciousness, is also a decision-making theory that abandons subjectivity and moves toward objectivity, not just a computer operating system. It has been shown that the process can be subsumed into the three principles of the Middle Path in that it is a learning method that constantly modifies the intellectual system of consciousness based on observation, data, and events. The reasoning of this paper is that the prior probability of Bays Theorem can be represented as a framework for divided thinking, and that the total probability can be determined as a Gong(空), the likelihood probability as a Ga(假), and the posterior probability as a Jung(中). It can be said that it is a process of dismantling the discriminated cognition system caused by the fixed frame and fringe limitations through Gong(空), presenting an updated direction based on this, and realizing the Middle Path. It can be confirmed that the same mechanism of operation as AI's deep learning and reinforcement learning methods interact with the Middle Path thinking system.