Kristina Šekrst

This paper argues that fungal mycelial networks exhibit minimal cognition through memory-integrated adaptive regulation. Drawing on cybernetic and enactivist frameworks, I develop a non-representational account of memory as the organism's capacity to modulate behavior based on temporally extended environmental coupling. I propose four operational criteria for minimal cognition: feedback-guided regulation of behavior, maintenance of internal viability conditions, structural modulation based on past environmental interactions, and plasticity across time scales that supports anticipatory adaptivity. Empirical evidence demonstrates that fungi meet all four criteria through distributed memory mechanisms: fungal networks exhibit directional regrowth toward previously encountered resources even after spatial displacement; stress priming persists across multiple cell divisions; Spitzenkörper-mediated directional persistence in constrained environments; and transgenerational memory through spore imprinting. These findings challenge representationalist assumptions in cognitive science by showing that memory and cognition can emerge from morphodynamic, biochemical, and electrophysiological processes without necessary neural substrates or symbolic representations. Fungal cognition demonstrates that the organizational principles underlying cognition – feedback-driven adaptation, norm-preservation, and historical coupling – can be realized in radically different material substrates, expanding our understanding of what counts as a cognitive system.

The second edition keeps everything from the first, including convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks, and autoencoders. It then covers the systems that have reshaped the field since: generative adversarial networks, the transformer architecture and its attention mechanism, the full training pipeline behind modern large language models (LLMs), prompt engineering with real-life guardrail scenarios, parameter-efficient fine-tuning with LoRA, retrieval-augmented generation with vector databases, knowledge graphs, and agentic AI systems illustrated through an industrial case study.

[Šekrst, Kristina; Kovačić, Ana, “Clause Encounters of the Third Kind: Can LLMs Replace Language Teachers?,” Forthcoming, in Matthew Dennis (ed.), Research and Education, in Philipp Hacker (ed.), Oxford Intersections: AI in Society. (Oxford, online edn., Oxford Academic, 2025).] While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback and methodological explanations that language learners need. The study tests multiple large language models on their ability to identify, correct, and explain common learner mistakes in English, by systematically varying model parameters to investigate how these technical adjustments affect output quality, pedagogical clarity, and consistency, along with using retrieval-augmented generation to query methodological data. The evaluation employs automated metrics (GLEU, BERTScore) but also human expert judgments to capture dimensions that purely computational measures miss: linguistic nuance, cultural sensitivity, and instructional appropriateness. While models demonstrate impressive surface-level correction abilities, their explanations often lack the terminological and domain knowledge that effective language teaching requires, suggesting that current enthusiasm for AI-assisted language learning may be outpacing our understanding of these systems’ actual pedagogical competence.

Astrobiology occupies an unusual position within the philosophy of science. Confronted with the n = 1 problem – having only a single example of life to study – it attempts to investigate life beyond Earth while relying entirely on Earth’s biosphere as its reference point, a constraint that creates unique epistemic challenges. Unlike traditional sciences with clear predictive frameworks, astrobiology operates as what we might call a transient science: a discipline functioning without foundational certainties, relying predominantly on abductive reasoning, and confronting hypotheses that may remain untestable for decades. It is, in essence, a science of absence – of evidence, certainty, and analogy – where progress lies in refining conceptual and experimental tools to recognize unfamiliar forms of life. This positions astrobiology alongside emerging fields like artificial intelligence and cognitive science within a broader transformation of how scientific knowledge is constructed when dealing with phenomena that transcend direct empirical access.

This paper examines Netflix's use of personalized thumbnails, framing the discussion within cybernetic and psychological contexts to explore the nature of intentionality in automated systems. While algorithms are designed to optimize user engagement, the ascription of "intentionality" to such systems requires careful delineation between human and machine agency. By analyzing the processes underlying automated personalization, this paper situates algorithmic behavior within a cybernetic framework, showing that while algorithmic ‘intentionality’ arises from emergent processes, it remains functionally distinct from moral agency. The discussion contributes to the broader debate on the ethical implications of algorithmic design and communication strategies.

This paper explores the intersection of AI hallucinations and the question of AI consciousness, examining whether the erroneous outputs generated by large language models (LLMs) could be mistaken for signs of emergent intelligence. AI hallucinations, which are false or unverifiable statements produced by LLMs, raise significant philosophical and ethical concerns. While these hallucinations may appear as data anomalies, they challenge our ability to discern whether LLMs are merely sophisticated simulators of intelligence or could develop genuine cognitive processes. By analyzing the causes of AI hallucinations, their impact on the perception of AI cognition, and the potential implications for AI consciousness, this paper contributes to the ongoing discourse on the nature of artificial intelligence and its future evolution.

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.

The purpose of this paper is to show that understanding AI hallucination requires an interdisciplinary approach that combines insights from epistemology and cognitive science to address the nature of AI-generated knowledge, with a terminological worry that concepts we often use might carry unnecessary presuppositions. Along with terminological issues, it is demonstrated that AI systems, comparable to human cognition, are susceptible to errors in judgement and reasoning, and proposes that epistemological frameworks, such as reliabilism, can be similarly applied to enhance the trustworthiness of AI outputs. This exploration seeks to deepen our understanding of the possibility of AI cognition and its implications for the broader philosophical questions of knowledge and intelligence.

The purpose of this paper is to describe the use of cognitive convenience in color naming and to find possible cognitive, physical, pragmatic, and logical reasons for such a phenomenon. By the term cognitive convenience, we mean the naming of or referring to objects of a certain color, for which their hue is not as important as their brightness, in which case, they might fall under another focal color. For example, in various languages, grapes are “white” and “black”, even though their real hue is usually a certain shade of green or purple. Along with a brief typological comparison of examples of cognitive convenience in unrelated languages, we report the results of a survey demonstrating that vagueness and brightness context influenced color naming, thus confirming our main hypotheses. We concluded that in conversation, the main criterion for choosing and identifying a referent of an NP with a color adjective—when the choice is based on color—is the proximity of its shade to the prototypical shade of the named color. In this process, contextual factors may affect the speaker’s and hearer’s preciseness. We claim that this phenomenon can be explained not only from a philosophical and pragmatic standpoint, but from an information-entropy standpoint as well. For an overall unifying theory, we will connect the informational entropy to the pragmatic notion of semantic vagueness and then inspect the overall choice of a fuzzy predicate logic that is able to incorporate such references.

This chapter explores narrativism and performativity in David Lynch's short films Darkened Room and Absurda, focusing on how personal identity is constructed through storytelling. Lynch's films often blur the boundaries of identity, with narrativist theories suggesting that identity is shaped by the stories characters and others tell about themselves. The chapter examines Lynch's use of performative utterances, where speech acts alter reality, and how narrative power is central to identity formation. By analyzing these works, the chapter highlights the unique way Lynch manipulates narrative to challenge traditional concepts of self and continuity.

Computational complexity has often been ignored in the philosophy of mind, in philosophical artificial intelligence studies. The purpose of this paper is threefold. First and foremost, to show the importance of complexity rather than computability in philosophical and AI problems. Second, to rephrase the notion of computability in terms of solvability, i.e., treating computability as non-sufficient for establishing intelligence. The Church-Turing thesis is therefore revisited and rephrased in order to capture the ontological background of spatial and temporal complexity. Third, to emphasize ontological differences between different time complexities, which seem to provide a solid base for a better understanding of artificial intelligence in general.

In artificial intelligence (AI), responses generated by machine-learning models (most often large language models) may be unfactual information presented as a fact. For example, a chatbot might state that the Mona Lisa was painted in 1815. Such phenomenon is called AI hallucinations, seeking inspiration from human psychology, with a great difference of AI ones being connected to unjustified beliefs (that is, AI “beliefs”) rather than perceptual failures). AI hallucinations may have their source in the data itself, that is, the source content, or in the training procedure, i.e. the way the knowledge was encoded in the model’s parameters, so that errors in encoding and decoding textual and non-textual representations can cause hallucinations. In this paper, we will observe how such errors come to life and how they might be mitigated. For this purpose, we will analyze the usability of justification logics, to behave as a proof checker for validating the correctness of large language models’ (LLM) responses. Justification logic was developed by S. Artemov, and later on mostly by Artemov and M. Fitting, deriving its main idea from the logic of proofs (LP): knowledge and belief modalities are seen as justification terms, i.e. t:X stands for t is a (proper) justification for X. Justification logic originated from attempts to create semantics for intuitionistic logic where proofs were the most proper justifications, but in further development, justification logic could be applied to different kinds of justifications). With the recent attempts to mitigate incorrect LLM responses, we will analyze various guardrails that are currently used for LLM responses, and see how the logic of justification may provide its benefits as an AI safety layer against false data.

Cyberspeak, the language of cybernetics, or its metalanguage to be more precise, consists of words that are both explaining and describing human/animal and machine forms of control and communication, while in newspeak, words were value-laden, which means they had strong positive or negative connotations connected to their use. For example, a 'spy' could only be a foreign agent, while a Russian one was a 'patriot'. First, it will be shown how there are still remnants of cyberspeak in modern science, pinpointing its cybernetic background and shaping our thoughts without us realizing they had any cybernetic origin whatsoever. Second, it will be investigated how newspeak, as its counterpart, can be analyzed from the theory of speech acts. Third, the current war in Ukraine allowed us a brief analysis of newspeak present in Russian public communication today (Putin's speeches), which will again be connected to their performative aspect.

In a Star Trek: The Next Generation episode, Cpt. Picard is captured and trapped on a planet with an alien captain who speaks a language incompatible with the universal translator, based on their societal historical metaphors. According to Shapiro (2004), the concept of a universal translator removes everything alien from alien languages, and since the Tamarian language refers only to their historical and cultural archetypes, Picard can only establish dialogue by invoking human analogues, such as Gilgamesh. The purpose of this paper is threefold. First, we will tie such communication to standard cognitive models in cognitive linguistics, pioneered by Lakoff and Johnson (1980) and Lakoff (1987). Second, we will show that metaphorical imaging resembling the Tamarian language is already present in our culture, by analyzing Dawkins's (1982) notion of a meme, who presupposes that memes as cultural living structures physically reside in the brain, and comparing that to a common format of a meme in pop culture. We will also contrast this concept with living natural languages such as Wolaytta, using a great number of proverbs, which can be seen as desemanticized metaphors that are used to embody different domains of the social realm of life (Make et al. 2014). Third, we will compare the notion of such a language with the current state of machine-learning and deep-learning techniques for natural language processing of metaphorical phrases, pinpointing that even deep learning today is powerless without sufficient training datasets (cf. Perifanos et al. 2020) or specific known word contexts and embeddings (Rei et al. 2017), compared to the same situation the universal translator was faced with when dealing with the Tamarian language.

Did you ever wonder why you are sometimes too tired to watch a film, and would rather watch some TV show? And then, you might end up watching five or six hours and binge watch an entire season, and yet feel too tired to commit yourself to a single 2-hour film piece. The purpose of this paper is threefold. First, I will try to investigate whether there are any ontological differences in the form of a film or a television show. Second, I will try to connect the newest neurological and psychological research regarding binging and attention span and see how seriality influences our brains. I will use the recent neurological findings to try to answer the question of why it seems easier to watch the same number of episodes rather than a single movie. Third, I will use David Lynch's and Mark Frost's third season of Twin Peaks as an example illustrating the blurry borders between a television show and a film, while contrasting it to the binge-watching phenomenon.

The goal of this paper is to see how mental and language representations are unique from a video-game perspective, using two main criteria. First, I will posit that the level of being both an interactive work of fiction and a self-involving interactive fiction belongs to a fuzzy interval and that some works – and, therefore, some video games – are more immersive than others. Second, I will observe how propositions tie the player’s representations of the real world and the game world. Starting from psychological theories of pretense in children’s make-believe games, I will then expand Nichols and Stich’s cognitive theory of pretense to include an extra layer related to the game world, i.e., player-specific representations that govern player-specific propositions. The representations dealing with the work world are the socially shared ones, while the possible-world representations, dealing with most of the game world, are player-specific and tied to unique language use.

In this paper, through the analysis of the division of different scientific fields, we deal with the nature of mathematics as a scientific discipline. Through the historical analysis of the division of science, but also the analysis of the nature of mathematics and the ontological status of the objects that mathematics deals with, we show that the now-established divisions among scientific fields are the result of social circumstances and that mathematics itself is closer to the humanities than the natural sciences.

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims.

The purpose of this paper is twofold. First, we will observe how May’s background in astrophysics influenced Queen's lyrics, such as 'Don't Stop Me Now' or 'Dead on Time'. Our goal is to see how physical and philosophical concepts of matter and time intersect with the common understanding of such phenomena, and how they differ from them. Second, we will focus on usually not that well-known song ‘39, which shows the entire point of the special theory of relativity through a prism of storytelling. Concepts of space and time will be analyzed from both absolute and relative perspectives, connecting them to historical theories of space and time.

Saul Goodman, Slipping Jimmy, Charlie Hustle, Gene Takavic, Viktor Saint Claire, and many others — all seem to be aliases of one James McGill. The characterization question, from the point of view of the metaphysics of identity, is trying to answer what determines personal identity. The notion of persistence describes necessary and sufficient conditions for a person to continue or cease to exist as a person. The practical importance of persistence includes both responsibility for a person's actions and the fact that identity matters practically because of a person's selfish care about his or her own future. From a Better Call Saul perspective, we are combining metaphysical issues of identity — that pinpoint what it takes for someone to remain the same person — with the pendulum of various James McGill personae. James McGill does seem to have both biological and psychological continuity while shifting from one character to another, but we will argue that psychological continuity is only relevant at the lowest level and that the narrativist account seems to better explain different Jimmy personae. Narrativism answers the persistence question by stating that we are persisting as the same person because of the stories we tell about ourselves, which influence our lives and our decisions. Identity is being constituted at a narrative level, that is — Jimmy is his (current) story, determining when he begins and ends. In this paper, by analyzing character development throughout the series, we are trying to show that James McGill may not always be equated with his aliases because of different spatial and temporal narratives, which also could open various ethical implications of responsibility for past actions.

First described by Galton in 1880 and then remaining unnoticed for a century, recent investigations in neuroscience have shown that a condition called aphantasia appears in certain individuals, which causes them to be unable to experience visual mental imagery. Comparing aphantasia to hyperphantasia – i.e., photo-like memory – and considering the neurological basis of perceptual phenomena, we are revisiting Hume's division of perceptions into impressions and ideas. By showing different vivacities of mental phenomena and comparing them to neurological research, we are stating that not only impressions and ideas differ "in the degrees of force and liveliness", but ideas and impressions amongst themselves as well. Such a gradual range of perceptions and mental images bears significant consequences for not only representational theory and historical interpretations, but linguistics and semiotics as well.

Films frequently employ nicknames not only for villains but also for non-criminal characters. In this paper, I present a classification of nicknames used in films, along with various examples, mostly from crime-related films. I argue that the use of nicknames in films is important not for the sake of reference, but for the sake of an additional narrative told by the nickname as a shorthand description of a character's background (cf. Tony “Two-Toes”, “Dirty” Harry, “Doc” Erwin or “Hatchet” Harry Lonsdale). The first role of nicknames is their use as a case of Russell's definite descriptions, which require context to be meaningful, in this case, the film story itself. Such descriptions do not need their object to necessarily exist, but they are still meaningful. This role will be tied to the pragmatic context employed. The second role of film nicknames is to concisely present the audience with a background story, by enriching the identity of a character with additional background information, without unnecessary storytelling. Such a device is connected to the philosophical theory of narrativism, providing an additional layer of the character’s identity.

The goal of this paper is to establish a hierarchical level of deception which does not apply only to humans and non-human animals, but also to the rest of the living world, including plants. We will follow the hierarchical categorization of deception, set forth by Mitchell (1986), in which the first level of deception starts with mimicry, while the last level of deception includes learning and intentionality, usually attributed to primates. We will show how such a hierarchy can be attributed to bacteria, plants, and fungi, see that self-deception is not inherent only to humans, and then connect the evolutionary roots of deception with the philosophical notion of intentionality.

The central aim of astrobiology is to study origins, evolution and distribution of life in the universe, combining data from various disciplines. However, I will argue that from a philosophical standpoint, astrobiology requires the affirmation of astrophilosophy. Fry (2015) claims that philosophical presuppositions guiding science are general, for example, we hold the notion that natural laws necessarily hold at the whole universe at large, and on the basis of the universal applicability of natural laws, the astrobiological research is conducted. Jakosky (2000) mentions that finding any life elsewhere that had an origin independent of that on Earth would be philosophically profound. Kolb (2005) covers various definitions of life and establishes thought experiments about the survival of life in the Solar System. Most of the (scarce) research has dealt with specific philosophical topics in astrobiology, instead of seeing it as a new philosophical discipline or perhaps a new subdiscipline for each of the classical philosophical disciplines. Persson (2013) has made a step towards this by systematizing the most important philosophical questions in astrobiology, along with Dunér (2013) emphasizing its importance for ethics, epistemology, semiotics and linguistics, and cognition. Persson mentions that the justification of resources is a question of ethics, but I would also like to add political philosophy, since not only is there a question of preference over "earthly manners" but also fair distribution of costs and the concept of ownership of the new worlds. The question of a new definition of life is another one, that I would argue holds one of the central places in metaphysics. Persson mentions various definitions of life and that such a discussion is important for both biology and philosophy, a notion I would like to add to by stating that the question of finding another life is also an ontological question, giving us either categorical similarity or dissimilarity, and establishing a new kind, which is of metaphysical importance. Persson also establishes the importance of is it possible to know whether we are alone or not, a question I would like to tie to the modern epistemological questions of possibility of knowledge (cf. Cassam, 2007). Persson also connects astrobiology with ethical questions such as biocentrism or colonization, which also might be tied to philosophy of mind, and philosophy of politics and culture respectively. One major step forward was established recently by Dick (2020), focusing on a new discipline combining astronomy, cosmology, and astrobiology, in which there is a focus on the nature of reasoning, the problematic nature of evidence, the influence of metaphysical preconceptions, the epistemological status of these disciplines, the role of technology, and the mutual interaction between these disciplines. The purpose of this paper is to see how the creation of astrophilosophy could better emphasize the role of philosophy today, and would it behave as a side-by-side major philosophical discipline or a complement to the current philosophical disciplines, for example, bringing new light into metaphysics, epistemology, ethics, or philosophy of mind. In the first case, we would make a metaphysical difference between human-related problems and other possible kinds, and in the second case, we would have to incorporate current astrobiological questions into modern philosophical dialogue, establishing, for example, metaphysics and epistemology of life, or, a possibility to incorporate non-human life into epistemology, philosophy of mind, and philosophy of biology. This goes hand to hand with Dunér's (2013) points of seeing the philosophy of astrobiology as both a type of individual and collective self-understanding, and conceptual analysis. That way, using astrophilosophical novelties, we can improve not only our current philosophical intuitions, but also expand the territory of philosophical subdisciplines to become more universal.

In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding.

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI.