arXiv:2407.18950v1 Announce Type: new
Abstract: Kant’s Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principle of human judgment. The technology of artificial intelligence (AI), based on functionalism, claims to simulate or replicate human judgment. To assess this claim, it is necessary to study whether AI judgment possesses the characteristics of human judgment. This paper argues that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant. Because the characteristics of judgment overlap, we can call this AI’s uncertainty. Then, I show that concepts without physical intuitions are not easy to explain when their functions are shown through vision. Finally, I illustrate that even if AI makes sentences through subject and predicate in natural language, which are components of judgment, it is difficult to determine whether AI understands the concepts to the level humans can accept. This shows that it is questionable whether the explanation through natural language is reliable.
Analyzing the Nature of Artificial Intelligence Judgments: A Critique of Kant’s Categories
In this thought-provoking paper, the author delves into the intriguing question of whether artificial intelligence (AI) possesses the same characteristics of judgment as humans, as proposed by Kant in his Critique of Pure Reason. The interdisciplinary nature of this discussion becomes evident as the fields of epistemology and AI intersect, providing fertile ground for analysis.
The author first introduces Kant’s table of categories, which aims to unravel the underlying structure of human judgment. Drawing from this framework, AI, based on functionalism, claims to replicate human judgment. However, the author posits that AI judgments exhibit a distinct form that cannot be easily reconciled with the characteristics outlined by Kant.
One key aspect highlighted is AI’s uncertainty, which arises from the overlapping characteristics of judgment. This concept sheds light on the unique nature of AI’s decision-making process, which differs from the certainty that humans possess in their judgments. This realization highlights the need to develop a nuanced understanding of AI’s cognitive processes beyond mere replication of human behavior.
Furthermore, the author emphasizes the challenges AI faces in explaining concepts without physical intuitions, particularly in the realm of vision. While AI has made remarkable progress in image recognition and analysis, the difficulty lies in comprehending the underlying mechanisms through which AI understands these concepts. This highlights the interdisciplinary nature of AI research, which necessitates the incorporation of diverse fields such as computer vision, neuroscience, and philosophy.
The paper also touches upon the limitations of AI’s ability to understand concepts on the same level as humans. Despite AI’s capability to construct sentences using subject and predicate components of natural language, it remains uncertain whether AI truly comprehends the concepts being conveyed. This raises important questions regarding the reliability of explanations provided by AI through natural language and the extent to which they align with human understanding.
In conclusion, this thought-provoking analysis challenges the notion that AI can perfectly simulate human judgment. By examining the differences between AI judgments and the characteristics proposed by Kant, the author sheds light on the multi-disciplinary nature of the concepts at play. The intersection of philosophy, AI, and cognitive science allows for a deeper understanding of the limitations and unique nature of AI’s cognitive processes, opening up new avenues for exploration and development in these fields.