A new, updated edition of the 1979 classic from one of the foremost authors in cognitive science and theoretical biology, with the original text as well as more than 200 citations to current scientific developments. Francisco Varela’s Principles of Biological Autonomy was a groundbreaking text when it was first published in 1979, putting forth a novel theory of how living systems produce and maintain themselves. This new edition, edited and annotated by cognitive scientists Ezequiel Di Paolo and Evan Thompson—revised and complemented with introductory essays for each part of the book—contains a wealth of ideas relevant to current projects in theoretical biology, cognitive science, systems theory, philosophy of mind, and philosophy of biology. Over 220 margin annotations supplement the reading of the text, linking to subsequent research and broader contemporary debates. This foundational book introduces the key concept of autonomy derived as an elaboration of the idea of autopoiesis (the self-production and self-distinction) of living organisms. Varela covers topics in systems theory, neuroscience, theories of perception, and immune networks and offers a participatory epistemology that goes on to be further developed in later enactive literature. These ideas are compelling not only for historical reasons but also because they still illuminate current efforts in developing the enactive approach toward wider and more challenging goals (including language, human cognition, ethics, and environmentalism).
Gilles Deleuze (1925-1995) was Professor of Philosophy at the University of Paris VIII. He is a key figure in poststructuralism, and one of the most influential philosophers of the twentieth century. Nietzsche and Philosophy has long been recognised as one of the most important accounts of Nietzsche's philosophy, acclaimed for its rare combination of scholarly rigour and imaginative interpretation. Yet this is more than a major work on Nietzsche; the book opened a whole new avenue in post-war thought. Here, Deleuze shows how Nietzsche began a new way of thinking which breaks with the dialectic as a method and escapes the confines of philosophy itself. Translated by Hugh Tomlinson.
This paper explores hallucination phenomena in large language models (LLMs) through the lens of language philosophy and psychoanalysis. By incorporating Lacan's concepts of the "chain of signifiers" and "suture points," we propose the Anchor-RAG framework as a novel approach to mitigate hallucinations. In contrast to the predominant reliance on trial-and-error experiments, constant adjustments of mathematical formulas, or resource-intensive methods that emphasize quantity over quality, our approach returns to the fundamental principles of linguistics to analyze the root causes of hallucinations in LLMs. Drawing from robust theoretical foundations, we derive algorithms and models that are not only effective in reducing hallucinations but also enhance LLM performance and improve output quality. This paper seeks to establish a comprehensive theoretical framework for understanding hallucinations in LLMs and aims to challenge the prevalent "guess-and-test" approach and rat race mentality in the field. We aspire to pave the way for a new era of interpretable LLMs, offering deeper insights into the inner workings of language-based AI systems.
The widespread application of artificial intelligence (AI) in various tasks, along with frequent reports of conflicts or violations involving AI, has sparked societal concerns about interactions with AI systems. Based on Wrightsman's Philosophies of Human Nature Scale (PHNS), a scale empirically validated over decades to effectively assess individuals' attitudes toward human nature, we design the standardized psychological scale specifically targeting large language models (LLM), named the Machine-based Philosophies of Human Nature Scale (M-PHNS). By evaluating LLMs' attitudes toward human nature across six dimensions, we reveal that current LLMs exhibit a systemic lack of trust in humans, and there is a significant negative correlation between the model's intelligence level and its trust in humans. Furthermore, we propose a mental loop learning framework, which enables LLM to continuously optimize its value system during virtual interactions by constructing moral scenarios, thereby improving its attitude toward human nature. Experiments demonstrate that mental loop learning significantly enhances their trust in humans compared to persona or instruction prompts. This finding highlights the potential of human-based psychological assessments for LLM, which can not only diagnose cognitive biases but also provide a potential solution for ethical learning in artificial intelligence. We release the M-PHNS evaluation code and data at https://github.com/kodenii/M-PHNS.
Although popularized AI fairness metrics, e.g., demographic parity, have uncovered bias in AI-assisted decision-making outcomes, they do not consider how much effort one has spent to get to where one is today in the input feature space. However, the notion of effort is important in how Philosophy and humans understand fairness. We propose a philosophy-informed approach to conceptualize and evaluate Effort-aware Fairness (EaF), grounded in the concept of Force, which represents the temporal trajectory of predictive features coupled with inertia. Besides theoretical formulation, our empirical contributions include: (1) a pre-registered human subjects experiment, which shows that for both stages of the (individual) fairness evaluation process, people consider the temporal trajectory of a predictive feature more than its aggregate value; (2) pipelines to compute Effort-aware Individual/Group Fairness in the criminal justice and personal finance contexts. Our work may enable AI model auditors to uncover and potentially correct unfair decisions against individuals who have spent significant efforts to improve but are still stuck with systemic disadvantages outside their control.
The article is dedicated to the 85th anniversary of Viktor P. Sheinov, a Belarusian-Russian scientist, Doctor of Sociological Sciences, Candidate of Physical and Mathematical Sciences, Academician of the International Academy of Information Technologies. The author believes that attention to Viktor P. Sheynov’s phenomenon is important not only from the point of view of the contribution of its bearer to various areas of intellectual activity, but also in connection with the search for resources for the renewal of domestic humanitarian science, and the significance of precedents for its effective implementation.
Abdoul Jalil Djiberou Mahamadou, Aloysius Ochasi, Russ B. Altman
Data are essential in developing healthcare artificial intelligence (AI) systems. However, patient data collection, access, and use raise ethical concerns, including informed consent, data bias, data protection and privacy, data ownership, and benefit sharing. Various ethical frameworks have been proposed to ensure the ethical use of healthcare data and AI, however, these frameworks often align with Western cultural values, social norms, and institutional contexts emphasizing individual autonomy and well-being. Ethical guidelines must reflect political and cultural settings to account for cultural diversity, inclusivity, and historical factors such as colonialism. Thus, this paper discusses healthcare data ethics in the AI era in Africa from the Ubuntu philosophy perspective. It focuses on the contrast between individualistic and communitarian approaches to data ethics. The proposed framework could inform stakeholders, including AI developers, healthcare providers, the public, and policy-makers about healthcare data ethical usage in AI in Africa.
This paper explores the ethical implications of integrating Artificial Intelligence (AI) in educational settings, from primary schools to universities, while drawing insights from ancient Greek philosophy to address emerging concerns. As AI technologies increasingly influence learning environments, they offer novel opportunities for personalized learning, efficient assessment, and data-driven decision-making. However, these advancements also raise critical ethical questions regarding data privacy, algorithmic bias, student autonomy, and the changing roles of educators. This research examines specific use cases of AI in education, analyzing both their potential benefits and drawbacks. By revisiting the philosophical principles of ancient Greek thinkers such as Socrates, Aristotle, and Plato, we discuss how their writings can guide the ethical implementation of AI in modern education. The paper argues that while AI presents significant challenges, a balanced approach informed by classical philosophical thought can lead to an ethically sound transformation of education. It emphasizes the evolving role of teachers as facilitators and the importance of fostering student initiative in AI-rich environments.
Given the strong mimetic and dramatic qualities found in Judith the authors make the suggestion that perhaps, before LXX Judith became a fixed, written text, the basic fabula might well have been part of an oral tradition. The authors accept that an appropriately written dramatic work, whether transmitted through reading or an oral presentation, by means of its performative qualities, has the potential to achieve immediacy. Here, the audience may become captivated with its own familiarity and memory of popular, communally shared narratives. Accordingly, this article attempts to find evidence in the Greek text of LXX Judith for a possible oral precursor. In this context, corroboration is sought for the employment of verbal aspect and mood of the Greek language as well as instances of drama, theatrics, bodily gestures, mnemonic devices or special emphasis on the employment of the senses such as sight, taste and smell. The authors suggest that based on an analysis of the text of Chapter 13, there is much circumstantial evidence for the Judith fabula once being an oral narrative – one that embodies the dramatic and even encourages audience participation. This characteristic strongly suggests the removal of the fourth wall – the notion of an imaginary boundary between any fictional work and its audience.
Contribution: This article shows that Judith 13 is indeed the climax of the narrative. However, it goes further. It is a vivid scene with various performative aspects. There are props, dialogue and audience participation. This research is cutting-edge and paves the way for new explorations.
The article aims to reveal the main features of the Bolshevik project to build
the Kingdom of Reason in the USSR, associated with the radical reformatting of the relationship between the rational and irrational in the establishment of the socialist society. Theoretical sources include the works of Karl Marx and Friedrich Engels, linking the construction of a new communist society to the demystification of social relations and the elimination of alienation and reification; the writings of Vladimir Lenin and other prominent figures of the Soviet state, devoted to the organization of planned economy and the widespread implementation of rational management methods; and the works of the outstanding educator Anton Makarenko, aimed at developing and implementing the idea of educating a new Soviet person. The article posits Bolshevism (Leninism) as the Russian version of socialist transformation, inheriting its fundamental ideas from the French Enlightenment, including the concept of the Kingdom of Reason, manifested in the system of rationally organized planning, accounting, and control. However, in practice, the development of this system, taken to its extreme forms, gave rise to a number
of utopian projects that became embodiments of irrational thinking. Thus, the original idea to build the Kingdom of Reason underwent a transformation into its opposite. Nevertheless, the authors of the article believe that the Soviet model of a rationally organized social structure can be considered, if not fully realized, then overall a fairly successful project of building the Kingdom of Reason in a socialist state and methodologically correct sociopedagogical system for shaping a new person.
String theorists are certain that they are practicing physicists. Yet, some of their recent critics deny this. This paper argues that this conflict is really about who holds authority in making rational judgment in theoretical physics. At bottom, the conflict centers on the question: who is a proper physicist? To illustrate and understand the differing opinions about proper practice and identity, we discuss different appreciations of epistemic virtues and explanation among string theorists and their critics, and how these have been sourced in accounts of Einstein's biography. Just as Einstein is claimed by both sides, historiography offers examples of both successful and unsuccessful non-empirical science. History of science also teaches that times of conflict are often times of innovation, in which novel scholarly identities may come into being. At the same time, since the contributions of Thomas Kuhn historians have developed a critical attitude towards formal attempts and methodological recipes for epistemic demarcation and justification of scientific practice. These are now, however, being considered in the debate on non-empirical physics.
El objetivo general de este texto es revisar el humor violento a través de teorías y funciones del humor, en primer lugar. En segundo lugar, y en concreto, realizar una caracterización de los tipos o las formas, y las expresiones o los modos, en su práctica cotidiana en general, y especialmente en la enseñanza aprendizaje.
Esto se realizó con la participación de jóvenes universitarios que expresaron opiniones y relataron experiencias, a través de una encuesta de grupos de enfoque y de narraciones. Como principal resultado es posible afirmar la vigencia del humor violento e incluso su magnitud en nuestros días, en la vida y en el espacio educativo.
Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar
et al.
Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective in solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains largely open. Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks. In this paper, we propose a novel design philosophy called democratized learning (Dem-AI) whose goal is to build large-scale distributed learning systems that rely on the self-organization of distributed learning agents that are well-connected, but limited in learning capabilities. Correspondingly, inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. As such, the Dem-AI learning system can evolve and regulate itself based on the underlying duality of two processes which we call specialized and generalized processes. In this regard, we present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields. Accordingly, we introduce four underlying mechanisms in the design such as plasticity-stability transition mechanism, self-organizing hierarchical structuring, specialized learning, and generalization. Finally, we establish possible extensions and new challenges for the existing learning approaches to provide better scalable, flexible, and more powerful learning systems with the new setting of Dem-AI.