Francesco Bianchini

This paper examines how the progressive integration of AI into epistemic practices is reshaping human knowledge production and our broader interface with reality. Building on Bianchini (2025a) and engaging with Branda (2026), it argues that AI can no longer be regarded as an epistemically neutral tool, but actively influences human cognition in supportive, indifferent, or detrimental ways. The widespread adoption of Large Language Models across digital environments introduces risks of conceptual homogenization and threatens epistemic diversity and creative originality. Drawing on the System 0 framework proposed by Chiriatti et al. (2024) and its extension in Branda’s concept of the Thinkframe, the paper explores how AI participates in cognitive processes both at the individual and collective levels. While these frameworks illuminate important dynamics of human-AI co-creation, they require further theoretical refinement, particularly regarding the specific role of AI within distributed cognitive systems and its implications for epistemic autonomy and authority.

Generally speaking, model-based reasoning refers to every reasoning that involves model of reality or physical world, and it is especially involved in scientific discovery. Analogy is a cognitive process involved in scientific discovery as well as in everyday thinking. I suggest to consider analogy as a type of model-based reasoning and in relation with models. Analogy requires models in order to connect a source situation and a target situation. A model in an analogy is required to establish salient properties and, mostly, relations that allow transfer of knowledge from the source domain to the target domain. In another sense, analogy is the model itself, or better, analogy provides the elements of model of reality that enable the processes of scientific discovery or knowledge increase. My suggestion is that some insight on how an analogy is a model and is connected to model-based reasoning is provided by recently proposed theories about analogy as a categorization phenomenon. Seeing analogy as a categorization phenomenon is a fruitful attempt to solve the problem of feature relevance in analogies, especially in the case of conceptual innovation and knowledge increase in scientific domain.

In recent times, particularly in the last few years, we have observed the rise of numerous Artificial Intelligence and Natural Language Processing semantic technologies. These advancements have subtly yet profoundly transformed our understanding of knowledge and truth, and the mechanisms for expressing, preserving, and disseminating them. This article aims to explore the dual challenge of assessing the effects of Large Language Models and associated semantic technologies on text dissemination and production, especially across the Internet. It specifically examines the implications for trust in online knowledge repositories, the creation of indirect or deliberate forms of ignorance, and the general perception of AI as a critical component of autonomous systems from the users’ viewpoint. The discussion will also consider potential strategies to mitigate the epistemic risks posed by the employment of AI semantic tools, in both suitable and unsuitable scenarios. The suggested approach contributes to the debate on AI intelligence measurement, proposing the evaluation of an AI system’s expected intelligence (as perceived by users) as a means to address the challenges associated with the “knowledge” generated by these systems. My claim is that measuring the expected intelligence in AI systems places humans at the forefront of the issue without necessitating a precise definition of intelligence for AI systems. This approach preserves therefore the essential attribute of these systems: intelligence.

The book brings into relief the variety of approaches and disciplines that have informed the quest for a theory of cognition. The center of interest are the historical, geographical, and theoretical peripheries of classic AI's mainstream research program. The twelve chapters bring back into focus the variety of strategies and theoretical questions that researchers explored while working toward a scientific theory of cognition and pre-cognition. The volume is organized in four parts, each one including three essays. The first one deals with cybernetics, the approach that may be considered as the most important periphery of classic AI research. The second part focuses on the geographical periphery of AI research. It examines how the theories and techniques developed on AI's home ground were translated into countries with different cultures and traditions: Italy, France, and the Soviet Union. The third part focuses on AI's periphery understood in the cultural and historical meaning of the term. It contains essays that locate some of the central concepts of AI, like representation and computability, within a broader philosophical (Descartes, Aristotle, Leibniz) and technical background (programming theory and practice). The fourth and final part of the volume is focused directly on the limitation of Turing's classic computability theory and its possible alternatives, some of which were studied in the early years of AI's research (e.g. Ashby's re-entrant information model), while others have been intensely studied in recent times (quantum automata).

In this article, I deal with a conceptual issue concerning the framework of two special sciences: artificial intelligence and synthetic biology, i.e. the distinction between the natural and the artificial (a long-lasting topic of history of scientific though since the ancient philosophy). My claim is that the standard definition of the “artificial” is no longer useful to describe some present-day artificial sciences, as the boundary between the natural and the artificial is not so sharp and clear-cut as it was in the past. Artificial intelligence and synthetic biology, two disciplines with new technologies, new experimental methods, and new theoretical frameworks, all need a new, more specific, and refined definition of (the) “artificial”, which is also related to the use of the synthetic method to build real world entities and in open-ended (real or virtual) environments. The necessity of a new definition of the artificial is due to the close relationship of AI and synthetic biology with biology itself. They both are engineering sciences that are moving closer and closer, at least apparently, towards (natural) biology, although from different and opposite directions. I show how the new concept of the artificial is, therefore, the result of a new view on biology from an engineering and synthetic point of view, where the boundary between the natural and the artificial is far more blurred. From this, I try to formulate a brand-new, more useful definition for future understanding, practical, and epistemological purposes of these two artificial sciences.

The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested that the fundamental concept involved in Turing’s imitation game, conceived as a test for detecting the presence of intelligence in an artificial entity, is the concept of interaction, that Turing adopts in a wider, more intuitive and more fruitful sense than the one that is proper to the current research in interactive computing.

Generally speaking, model-based reasoning refers to every reasoning that involves model of reality or physical world, and it is especially involved in scientific discovery. Analogy is a cognitive process involved in scientific discovery as well as in everyday thinking. I suggest to consider analogy as a type of model-based reasoning and in relation with models. Analogy requires models in order to connect a source situation and a target situation. A model in an analogy is required to establish salient properties and, mostly, relations that allow transfer of knowledge from the source domain to the target domain. In another sense, analogy is the model itself, or better, analogy provides the elements of model of reality that enable the processes of scientific discovery or knowledge increase. My suggestion is that some insight on how an analogy is a model and is connected to model-based reasoning is provided by recently proposed theories about analogy as a categorization phenomenon. Seeing analogy as a categorization phenomenon is a fruitful attempt to solve the problem of feature relevance in analogies, especially in the case of conceptual innovation and knowledge increase in scientific domain.