JOURNAL OF DIALECTICS OF NATURE
A Comprehensive, Academic Journal of the Philosophy, History, Sociology and Cultural Studies of Science and Technology
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Reflections on the Empiricist Assumption of Large Language Models: A Study from the Perspective of Miki Kiyoshi’s Theory of Imagination Abstract: From the Japanese philosopher Miki Kiyoshi’s point of view, the empiricist notion of “experience” is too thin to accommodate temporal extension, reference to external objects, as well as the interplay between experience-owing agents and their environments. Once properly modified, the foregoing Miki’s criticism can be easily applied to the still-fashionable approach of Large Language Models (LLM) in AI. Paralleled with the empiricist philosophy, the LLM-builders construct the digital counterpart of the Humean notion of “impressions”, namely, “tokens” by treating the text as tokens. Moreover, similar to the Humean route-map of reconstructing “ideas” from the accumulations of “impressions”, LLM-builders also intend to reconstruct the semantic features of tokens via a proper statistical treatment of them, namely, a treatment routinely under the label of “word embedding”. Nonetheless, since such reductionism-oriented route-map has deliberately bypassed nearly all middle/high-level architecture required for a full-fledged notion of “cognition”, such rout-map could hardly account for why human’s cognitive machine can deliver creative decisions and be competent in counter-factual reasoning even when the size of the training data is much smaller than that is required by an empiricist theory. And the LLM-builders’ incompetence of accounting for all of this in turn philosophically explains the origin of the socalled “machine hallucination”. Key Words: Imagination; Empiricism; Large language models; Tokens; Word-Embeddingpage: 1-9Large 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 causationpage: 10-19The Possibility of Machine Intelligence: Research on Turing’s Concept of Intelligence Abstract: The Turing Test is often interpreted as an “operational” or “behaviorist” definition of intelligence, and the ability to pass the Turing Test is considered a criterion for determining whether a machine possesses intelligence. In fact, this is a misunderstanding. Turing never claimed that passing the Turing Test implies that a machine has intelligence. Attempting to directly address whether machines can think or possess intelligence was not the original purpose of the Turing Test. By examining Turing’s early conceptualization of intelligence, it becomes evident that he consistently maintained a firm belief in the possibility of machine intelligence, and he had already outlined potential approaches to achieving machine intelligence even before proposing the Turing Test. Key Words: Turing test; Machine intelligence; Turing; Intelligencepage: 20-27The Influence of Kant’s View of Logic on Thinking About Mathematical Foundation Abstract: From the perspective of intellectual history, logicism, formalism and intuitionism are intricately linked to Kant’s conception of mathematics. A fundamental aspect of Kant’s view of logic is the assertion that the applicability of logic is confined to possible experiences. The acceptance and subsequent critique of this view have catalyzed a significant inquiry into the foundations of mathematics. This paper investigated the impact of Kant’s view of logic on Hilbert’s theory of finite proofs, delineated the similarities and distinctions between Kant’s conception of mathematics and the formalist and platonist interpretations of mathematics, and elucidated that Kant’s logic provides the philosophical framework essential for comprehending Hilbert’s program. Furthermore, it also emphasized that a comprehensive understanding of Gödel’s incompleteness theorem necessitates an adequate grasp of Hilbert’s finite proofs theory. Ultimately, the author illustrated the enduring relevance of Kant’s view of logic in contemporary scientific inquiry, by exemplifing the double-slit experiment in quantum mechanics. Key Words: Kant; Logic; Formalism; Platonism; Double-Slit experimentpage: 28-36JOURNAL OF DIALECTICS OF NATURE
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