The research question centers on how artificial intelligence (AI) and human intelligence (HI) differ in their understanding and representation of the world. The study's context lies in the ongoing debate on the nature of intelligence and the extent to which AI can replicate or surpass HI. The purpose is to introduce a novel framework, distinguishing between "illustration" and "exhaustion," to analyze the distinct logics employed by AI and HI. The importance of this study stems from the increasing integration of AI into various aspects of human life and the need for a nuanced understanding of the implications of this integration. By applying the lens of ethnomethodology, the research aims to shed light on how AI systems account for and make their actions accountable, potentially revealing critical differences between human and machine intelligence that currently remain unexplored.

The literature review draws upon the works of key figures in AI (Minsky, McCarthy, Turing), examining their definitions of artificial intelligence and the underlying assumptions about the nature of intelligence. It further explores the philosophical roots of formalization in rationalism (Leibniz, Newton) and its implications for creating distance between humans and the world. The concept of simulation and hyperreality (Baudrillard) is discussed to understand how the absence of distance can lead to a sense of vertigo and the need for certainty. The review also incorporates Heidegger's work on technology and its mode of revealing, and Merleau-Ponty's notion of how humans grasp the world. The study integrates sociotechnical systems theory (Luhmann) and Dreyfus's argument on human minds tolerating ambiguity. The review explores the works of West and Travis on the concept of representation and its connection to Western philosophical traditions influencing AI development.

The primary methodology employed is ethnomethodology, focusing on how social members make social settings "observable and accountable." The author utilizes this framework to analyze the contrasting logics of illustration and exhaustion in AI and HI. The logic of illustration, characteristic of HI, involves creating distance between the observer and the observed to facilitate understanding. This logic embraces uncertainty, ambiguity, and incompleteness, acknowledging the ongoing, reflexive nature of human knowledge construction. In contrast, the logic of exhaustion, characteristic of AI, aims to eliminate distance through complete formalization. This approach prioritizes certainty, precision, and completeness, often neglecting the nuances and complexities inherent in human experience. The analysis draws upon various theoretical concepts, including Baudrillard's concept of hyperreality, Heidegger's work on technology, and Luhmann's social systems theory. The study also incorporates concepts from conversation analysis (CA) to examine the indexical and reflexive properties of natural language and compare them to the formal, rule-based language of AI algorithms. Specific examples, such as the use of emojis in communication and word vector technology in natural language processing (NLP), are used to illustrate the differences between the illustrative and exhaustive logics.

The key finding is the identification of two distinct logics: illustration and exhaustion. Human intelligence (HI) operates primarily through illustration, creating distance and embracing uncertainty to understand the world. AI, in contrast, operates through exhaustion, striving to eliminate distance and achieve complete formalization. This difference manifests in how AI and HI deal with ambiguity and incompleteness: HI tolerates ambiguity, while AI seeks to reduce it to certainty. The study reveals that HI's knowledge construction is reflexive and indexical, relying on context, interpretation, and the et cetera principle. AI's processes, on the other hand, lack reflexivity and are based on predetermined rules and exhaustive processing. The analysis of natural language processing (NLP) techniques, such as word vectors, further supports the argument that current AI approaches primarily rely on the logic of exhaustion, reducing the complexity of natural language to quantifiable data points. The comparison of default (AI) and de-fault (HI) from Stiegler's work highlights how AI systems operate within a closed, predetermined framework, unlike the open-ended exploration inherent in human intelligence. The analysis of conversation analysis rules and their application to human-machine interaction highlights the limitations of attempting to exhaust all possibilities of human conversation. The 'et cetera' principle, central to ethnomethodology, emphasizes that any rule-based system will inevitably encounter exceptions, further highlighting the limitations of the exhaustive logic in fully capturing human intelligence.

The findings address the research question by highlighting the fundamental differences between HI and AI's approaches to understanding the world. The contrasting logics of illustration and exhaustion provide a framework for understanding the strengths and limitations of both approaches. The significance of the results lies in providing a nuanced perspective on the capabilities and limitations of AI, moving beyond simplistic comparisons of human and machine intelligence. The results' relevance to the field of AI development lies in suggesting that a more balanced approach, incorporating the principles of illustration, might lead to more robust, adaptable, and human-centered AI systems. This requires moving beyond a solely exhaustive logic and integrating reflexive, indexical, and context-dependent processes into AI algorithms.

This paper contributes a novel framework distinguishing between the illustrative logic of human intelligence and the exhaustive logic of artificial intelligence. It underscores the need to consider the reflexive and indexical nature of human understanding when developing AI. Future research could explore how the principles of illustration can be incorporated into AI design to create more nuanced and adaptable systems. This might involve developing AI systems capable of embracing uncertainty, contextual understanding, and reflexive self-observation.

The study primarily relies on theoretical analysis and conceptual frameworks. Empirical studies examining specific AI systems would strengthen the conclusions. The framework's applicability to all types of AI systems requires further investigation. The philosophical nature of the study might not appeal to researchers solely focusing on the technical aspects of AI development.