JOURNAL OF DIALECTICS OF NATURE
A Comprehensive, Academic Journal of the Philosophy, History, Sociology and Cultural Studies of Science and Technology
Author Information
WU Xiaoan
Information
<p>Department of Philosophy</p><p>Capital Normality University</p><p>nantongwu@gmail.com</p>
Research Articles
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Simpson's Paradox: Clash of Logic-based Account and Causal Account Abstract: There are two kinds of accounts (logic-based and causal) of Simpson's paradox. one from philosopher Bandyoapdhyay, the other from computer scientist Judea Pearl. Bandyoapdhyay tried to show that there are three questions associated with Simpson's paradox that need adequate analysis: (i) Why is SP paradoxical? (ii) What conditions generate SP? (iii) How to proceed when confronted with SP? Developing a logic-based account of SP, he tried to argue that there are no causal factors at play in answering questions (i) and (ii) . Causality enters only in the third question. Comparing with these two accounts, I prove that Bandyoapdhyay's criticism of Pearl is not correct, his account of SP also faces many problems.Issue:Volume 40, lssue 5, May 2018Page: 53-59
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Mini-Turing Test and Children’s Education Abstract: There is considerable evidence to prove that causal learning and causal understanding ability have greatly enhanced our ability to manipulate the physical world and they are important factors that distinguish humans from other primates. How to get stupid robots to think causally, answer the questions raised with “why” and even understand the meaning of such questions is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, we must start by imitating the intelligence of children, so he proposed a “causal inference engine” to help future artificial intelligence conduct causal inference, pass the mini- Turing test, and even become a moral subject who can discern good from evil. This paper attempts to provide some different thinking dimensions for the development of children’s education from the basic assumptions and construction goals of artificial intelligence, and at the same time reflects on the current practice of artificial intelligence through children’s education. Key Words: Causal inference engine; Counterfactuals; Free will; Science educationIssue:Volume 44, lssue 9, September 2022Page: 27-37
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Large Language Models and the Ladder of Causation Abstract: Judea Pearl’s three-tiered ladder of causation was once a widely accepted critique of AI and a guiding program for AI practice. However, with the emergence of some phenomenal large language models such as GPT-4, its AI critique has been thoroughly disproved, and the causal theory of Structural Equation Modeling behind it has also been faced with challenges. The goal of this paper is threefold: first, it intends to elaborate on the core elements of the causal triad and the inner mechanisms of the large language Model, and to show the sense in which the latter reinvents Pearl’s original conception. Second, it also clarifies the theoretical significance for structural causal models of the emergence of such a large-language model capable of “demonstrating causal competence,” which destroys Pearl’s critique but opens up new possibilities for causal research. Finally, and more importantly, the theoretical value of structural causal models remains, and the idea of equipping intelligences with causal inference engines to help them make causal inferences is not obsolete. Key Words: Structural causal models; Large language models; Ladder of causationIssue:Volume 47, lssue 8, August 2025Page: 10-19
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