Though the realm of biology has long been under the philosophical rule of the mechanistic magisterium, recent years have seen a surprisingly steady rise in the usurping prowess of process ontology. According to its proponents, theoretical advances in the contemporary science of evo-devo have afforded that ontology a particularly powerful claim to the throne: in that increasingly empirically confirmed discipline, emergently autonomous, higher-order entities are the reigning explanantia. If we are to accept the election of evo-devo as our best conceptualisation (...) of the biological realm with metaphysical rigour, must we depose our mechanistic ontology for failing to properly “carve at the joints” of organisms? In this paper, I challenge the legitimacy of that claim: not only can the theoretical benefits offered by a process ontology be had without it, they cannot be sufficiently grounded without the metaphysical underpinning of the very mechanisms which processes purport to replace. The biological realm, I argue, remains one best understood as under the governance of mechanistic principles. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic or epistemic norms. Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation. Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic (...) accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more than regular univariate analyses, for—we are told—it not only can tell us which brain regions are engaged while processing particular stimuli, but also which patterns of neural activity represent the categories the stimuli are selected from. We disagree, and in the current paper we offer four conceptual challenges to the use (...) of MVPA to make claims about neural representation. Our view is that the use of MVPA to make claims about neural representation is problematic. (shrink)

In this paper, we examine how the availability of massive quantities of data i.e., the “Big Data” phenomenon, contributes to the creation, spread, and harms of online misinformation. Specifically, we argue that a factor in the problem of online misinformation is the evolved human instinct to recognize patterns. While the pattern-recognition instinct is a crucial evolutionary adaptation, we argue that in the age of Big Data, these capacities have, unfortunately, rendered us vulnerable. Given the ways in which online media (...) outlets profit from the spread of misinformation by preying on this pattern-finding instinct, we conceptualize the problem that we identify as a morally objectionable form of “epistemic exploitation.” As we argue, the consumer of digital misinformation is often exploited by having her pattern-recognition instinct used against her. This exploitation is morally objectionable because it deprives her of an epistemic good to which she has a right. This epistemic good is the integrity of the pattern-recognition instinct itself which, we argue, is a capacity that allows us to participate in uniquely human goods. While our primary goal is to bring attention to this form of epistemic exploitation, we conclude by briefly evaluating some general solutions to the growing problem of online misinformation. (shrink)

This work presents a post-positivist research framework to explain any surprising fact in the evolutionary path of a complex, dynamic and contingent social phenomenon. Primarily, it reconciles the ontological and epistemological assumptions of Critical Realism with the principles of American Pragmatism. Then, the research approach is presented: theoretical propositions about a social structure are translated into a set of grammar rules that acknowledges a pattern of sequences of events of either individual action or social interaction between actors within a (...) real social system. The result is a discrete mathematical model for a concrete category of social process based on these rules. Finally, data-grounded refinement of the theory is possible by the comparison between cases belonging to the same category, but differing about some contingent pattern of sequences of event outcomes. Consequently, their grammars differ in some pairs of context-sensitive rules that can explain this surprising fact, such that the derivation of this alternative historical trajectory of event outcomes becomes an extension to the early category of social process. In this sense, the proposed framework suggests there is a hierarchy of classes of grammars for middle-range explanations based upon the ontological assumption of the generative nature of social reality. (shrink)

Background: Tuberculosis is among the major causes of illness and death worldwide especially in Asia. Smoking is associated with recurrent tuberculosis and its related mortality. Also, it could affect clinical manifestations, bacteriological conversion and outcome of treatment. This study aimed to evaluate the pattern of tobacco smoking, history of previous quit attempts and attitude towards quitting in tuberculosis patients. Materials and Methods: It was a cross-sectional study done amongst tuberculosis patients presented to DOTS corner of Rajshahi Medical College Hospital. (...) 315 patients entered the study as "First Come First Serve". Selfreported questionnaires were designed according to the standard questionnaires of smoking pattern. Results: Sixty one percent (61%) patients (n=192) were smoker before the diagnosis of tuberculosis. 51.1% were current smokers at the time of interview and were continuing smoking after the diagnosis of tuberculosis. Only 10.2% discontinued smoking after diagnosis. 68.2% smokes less than 10 cigarettes per day. 53.1% was between 31-50 age group. 21-30 is the commonest age group (62%) to start smoking is and 66.1% smoked less than 10 years before diagnosis. To reduce stress was the most common cause to continue smoking (48.9%). 71.9% smokers showed keen interest to stop smoking. Conclusion: Considering the prevalence of smoking in tuberculosis patients, evaluation of tobacco smoking status in such patients and motivating them to quit smoking could be considered as important steps in their treatment process. (shrink)

While many different mechanisms contribute to the generation of spatial order in biological development, the formation of morphogenetic fields which in turn direct cell responses giving rise to pattern and form are of major importance and essential for embryogenesis and regeneration. Most likely the fields represent concentration patterns of substances produced by molecular kinetics. Short range autocatalytic activation in conjunction with longer range “lateral” inhibition or depletion effects is capable of generating such patterns (Gierer and Meinhardt, 1972). Non-linear reactions (...) are required, and mathematical criteria were derived to design molecular models capable of pattern generation. The classical embryological feature of proportion regulation can be incorporated into the models. The conditions are mathematically necessary for the simplest two-factor case, and are likely to be a fair approximation in multi-component systems in which activation and inhibition are systems parameters subsuming the action of several agents. Gradients, symmetric and periodic patterns, in one or two dimensions, stable or pulsing in time, can be generated on this basis. Our basic concept of autocatalysis in conjunction with lateral inhibition accounts for self-regulatory biological features, including the reproducible formation of structures from near-uniform initial conditions as required by the logic of the generation cycle. Real tissue form, for instance that of budding Hydra, may often be traced back to local curvature arising within an initially relatively flat cell sheet, the position of evagination being determined by morphogenetic fields. Shell theory developed for architecture may also be applied to such biological processes. (shrink)

The paper addresses the formation of striking patterns within originally near-homogenous tissue, the process prototypical for embryology, and represented in particularly purist form by cut sections of hydra regenerating, by internal reorganisation of the pre-existing tissue, a complete animal with head and foot. The essential requirements are autocatalytic, self-enhancing activation, combined with inhibitory or depletion effects of wider range – “lateral inhibition”. Not only de-novo-pattern formation, but also well known, striking features of developmental regulation such as induction, inhibition, and (...) proportion regulation can be explained on this basis. The theory provides a mathematical recipe for the construction of molecular models with criteria for the necessary non-linear interactions. It has since been widely applied to different developmental processes. (shrink)

The work of Gregory Bateson offers a metaphysical basis for a “process psychology,” that is, a view of psychological practice and research guided by an ontology of becoming—identifying change, difference, and relationship as the basic elements of a foundational metaphysics. This article explores the relevance of Bateson's recursive epistemology, his re-conception of the Great Chain of Being, a first-principles approach to defining the nature of mind, and understandings of interaction and difference, pattern and symmetry, interpretation and context. Bateson's philosophical (...) contributions will be drawn into relationship with Wittgenstein's philosophy of language as use, Melnyk's theory of causal levels of explanation, Korzybski's account of map and territory, the rejection of the heuristic rigidity of substantialist ontologies, and a cybernetics communication science-informed approach to contextual bi-directionality of causality. We thereby arrive at an understanding of Bateson's process psychology that, given its ecological-systemic nature, is explanatorily applicable across the mind sciences. This process psychology equips us to answer the question: What is mind? Not by explanatory appeal to substantial entities contained within mind, but instead by recourse to the contextually relevant patterns for understanding mind to a particular purpose. We have thereby attended to the gulf between heuristics and fundamentals, between psychological models and an onto-epistemic account of reality. Insufficient attention has been given to characterising the vital nature of Bateson's philosophical oeuvre to psychological practice. This article draws out Bateson's relevance to establishing foundational principles for a process psychology capable of reinvigorating psychological thought. (shrink)

The cognitive processing of spatial relations in Euclidean diagrams is central to the diagram-based geometric practice of Euclid's Elements. In this study, we investigate this processing through two dichotomies among spatial relations—metric vs topological and exact vs co-exact—introduced by Manders in his seminal epistemological analysis of Euclid's geometric practice. To this end, we carried out a two-part experiment where participants were asked to judge spatial relations in Euclidean diagrams in a visual half field task design. In the first (...) part, we tested whether the processing of metric vs topological relations yielded the same hemispheric specialization as the processing of coordinate vs categorical relations. In the second part, we investigated the specific performance patterns for the processing of five pairs of exact/co-exact relations, where stimuli for the co-exact relations were divided into three categories depending on their distance from the exact case. Regarding the processing of metric vs topological relations, hemispheric differences were found for only a few of the stimuli used, which may indicate that other processing mechanisms might be at play. Regarding the processing of exact vs co-exact relations, results show that the level of agreement among participants in judging co-exact relations decreases with the distance from the exact case, and this for the five pairs of exact/co-exact relations tested. The philosophical implications of these empirical findings for the epistemological analysis of Euclid's diagram-based geometric practice are spelled out and discussed. (shrink)

This paper presents the design and implementation of a pattern-based J2EE application development environment aimed at streamlining the development process, enhancing code quality, and improving maintainability. We begin with an exploration of the fundamental principles of J2EE and the role of design patterns in software engineering. This is followed by a detailed discussion on the selection and application of various design patterns specifically tailored for J2EE, such as Singleton, Factory, Data Access Object (DAO), and Model-View-Controller (MVC).

The Austrian School seems to remain outside the debate on the realism of economic models. In principle, given the association of the term “model” with the Chicago School, and also for understanding that Hayek had critized the model of perfect competition as unrealistic. Even though in previous opportunities we showed how the theory of market as a process could be understood as the model of the Austrian School, and that Hayek’s criticism to the model of perfect competition was not so (...) much of unrealistic, but that it was wrongly stated (Zanotti & Borella, 2015), and we developed the ontological foundation of the model in Hayek’s thought (Borella, 2019), in this paper we will show how it is posible to compare pattern predictions with models, and how in this sense, the Austrian School, also seems to get near, even without sufficiently noticing it, to the discussion on models in economics and their realism. Keywords: Realism of models- Hayek- pattern predictions-models . (shrink)

The present study collected a larger set of three-dimensional data on human crania from the Kofun period (as well as from previous periods, i.e., the Jomon and Yayoi periods) in the Japanese archipelago (AD 250 to around 700) than previous studies. Three-dimensional geometric morphometrics were employed to investigate human migration patterns in finer-grained phases. These results are consistent with those of previous studies, although some new patterns were discovered. These patterns were interpreted in terms of demic diffusion, archaeological findings, and (...) historical evidence. In particular, the present results suggest the presence of a gradual geological cline throughout the Kofun period, although the middle period did not display such a cline. This discrepancy might reflect social changes in the middle Kofun period, such as the construction of keyhole-shaped mounds in the peripheral regions. The present study implies that a broader investigation with a larger sample of human crania is essential to elucidating macro-level cultural evolutionary processes. (shrink)

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). (...) This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog). (shrink)

In “Rethinking Norm Psychology,” Cecilia Heyes offers an insightful critique of nativist approaches to the psychology of norms and then proposes a plausible alternative model grounded in the theory of cognitive gadgets. We are broadly sympathetic to both the critique and to the cognitive-gadgets model, though our own pluralistic approach to the psychology of norms (Westra & Andrews, 2022) leads us to think that the range of psychological and ecological processes that contributes to our norm psychology is even more diverse (...) than what Heyes proposes. Here, though, we focus on Heyes’s conceptual critique of the nativist model and its explanatory target—namely, a system for representing and processing rules. Heyes’s criticism of this representational rule-based conception of the psychology of norms is quite sound. However, we will argue that Heyes’s alternative behavioral explanandum for the psychology of norms also fails to escape the gravitational pull of a rule-based approach. (shrink)

Beginning with a survey of the shortcoming of theories of organology/media-as-externalization of mind/body—a philosophical-anthropological tradition that stretches from Plato through Ernst Kapp and finds its contemporary proponent in Bernard Stiegler—I propose that the phenomenological treatment of media as an outpouching and extension of mind qua intentionality is not sufficient to counter the ̳black-box‘ mystification of today‘s deep learning‘s algorithms. Focusing on a close study of Simondon‘s On the Existence of Technical Objectsand Individuation, I argue that the process-philosophical work of Gilbert (...) Simondon, with its critique of Norbert Wiener‘s first-order cybernetics, offers a precursor to the conception of second-order cybernetics (as endorsed byFrancisco Varela, Humberto Maturana, and Ricardo B. Uribe) and, specifically, its autopoietic treatment of information. It has been argued by those such as Frank Pasquale that neuro-inferential deep learning systems premised on predictive patterning, suchas AlphaGo Zero, have a veiled logic and, thus, are ̳black boxes‘. In detailing a philosophical-historical approach to demystify predictive patterning/processing and the logic of such deep learning algorithms, this paper attempts to shine a light on such systems and their inner workingsàla Simondon. (shrink)

In today’s data-intensive world, real-time insights have become essential for businesses and systems that demand timely and intelligent decision-making. Traditional batch processing techniques are increasingly inadequate for handling high-velocity data streams generated from IoT devices, social media platforms, financial transactions, and industrial systems. This paper presents an architectural approach to real-time data stream processing using Apache Kafka integrated with AI models to enable dynamic analytics and automated responses. The system leverages Kafka's distributed messaging capabilities for reliable data ingestion (...) and delivery, while AI models trained on historical and real-time data are used to extract meaningful patterns and predictions. The proposed solution is designed to ensure low-latency inference, scalable processing, and seamless integration with existing data infrastructure. (shrink)

This article describes the system of patterning involved in the distribution of prime numbers. The description of the system is based on the idea that two seemingly independent process are interacting in a non-dimensional state. And since the language of mathematical formulation is syntactically dimensional, it cannot describe the system as a whole. If we accept advance predictability as proof of the system in connection with viewing a model of the regularity of its relationships, rather than relying on a traditionally (...) formulated mathematical proof, the rigorous system of prime distribution is clearly evident. The philosophical implications are significant, since it points to a weakness in mathematical language and the formulation of mathematical proofs. -/- *This draft is based on ideas published in the author's memoir "The Image of Language" 2021:Artists Books Editions. It is planned to be published in a book by the author tentatively titled "Archetivity.". (shrink)

Metacognition is the capacity to evaluate the success of one's own cognitive processes in various domains; for example, memory and perception. It remains controversial whether metacognition relies on a domain-general resource that is applied to different tasks or if self-evaluative processes are domain specific. Here, we investigated this issue directly by examining the neural substrates engaged when metacognitive judgments were made by human participants of both sexes during perceptual and memory tasks matched for stimulus and performance characteristics. By comparing patterns (...) of fMRI activity while subjects evaluated their performance, we revealed both domain-specific and domain-general metacognitive representations. Multivoxel activity patterns in anterior prefrontal cortex predicted levels of confidence in a domain-specific fashion, whereas domain-general signals predicting confidence and accuracy were found in a widespread network in the frontal and posterior midline. The demonstration of domain-specific metacognitive representations suggests the presence of a content-rich mechanism available to introspection and cognitive control. (shrink)

The examination of anomaly process detection using negative selection algorithms and classification techniques focuses on enhancing the ability to identify deviations from expected patterns within complex data sets. Negative selection algorithms, inspired by biological immune systems, offer a novel approach to anomaly detection by efficiently distinguishing between normal and anomalous data points. When combined with various classification techniques, these algorithms can improve the accuracy and robustness of anomaly detection systems. This abstract explores the integration of negative selection algorithms with traditional (...) and advanced classification methods to optimize anomaly detection processes. By leveraging these combined approaches, the study aims to address challenges such as false positives, detection latency, and adaptability to diverse data environments. The findings suggest that the synergy of negative selection algorithms and classification techniques can lead to more precise and reliable detection of anomalies, providing valuable insights for applications across cybersecurity, finance, healthcare, and other critical fields. (shrink)

A set of visual search experiments tested the proposal that focused attention is needed to detect change. Displays were arrays of rectangles, with the target being the item that continually changed its orientation or contrast polarity. Five aspects of performance were examined: linearity of response, processing time, capacity, selectivity, and memory trace. Detection of change was found to be a self-terminating process requiring a time that increased linearly with the number of items in the display. Capacity for orientation was (...) found to be about 5 items, a value comparable to estimates of attentional capacity. Observers were able to filter out both static and dynamic variations in irrelevant properties. Analysis also indicated a memory for previously-attended locations. These results support the hypothesis that the process needed to detect change is much the same as the attentional process needed to detect complex static patterns. Interestingly, the features of orientation and polarity were found to be handled in somewhat different ways. Taken together, these results not only provide evidence that focused attention is needed to see change, but also show that change detection itself can provide new insights into the nature of attentional processing. (shrink)

This article proposes the model 'Sequence of Emotional Event Processing (SPEE)' as a complementary study to established approaches, such as psychoanalysis, attachment theory, and cognitive-behavioral therapy. Through clinical observation and the development of the theory 'The Anatomy of the Soul' (JUNIOR, 2024), a symbolic-emotional sequence was identified that allows understanding how emotional meanings are formed and how they can transform into behavioral patterns.

Climate Change is a long-term change in the statistical distribution of weather patterns over periods of time that range from decades to millions of years. It may be a change in the average weather conditions or a change in the distribution of weather events with respect to an average, for example, greater or fewer extreme weather events. It is of keen interest to identify climatological behaviour to discover spatial relationships in climate variables, so that the trend of the climatic changes (...) can be analyzed and studied. Data Mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It has also opened up exciting opportunities for exploring and analyzing new types of data and for analyzing old types of data in new ways. As Data Mining is an analytic process designed to explore data in search of consistent patterns and systematic relationships between variables and then to validate the findings by applying the detected patterns to new subsets of data, the present study has been conducted to apply various Data Mining techniques to the climatic data collected and to study the trend, pattern and finally to predict. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle patterns in large (...) datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

The paper deals with the topical issue of studying cyclic patterns in the economy and their practical application for the forecasts on the development of financial markets. The work aims to establish the features of the seasonal-cyclic patterns "The January barometer" and "The first five days of January" in the international financial markets in current conditions and to develop recommendations for the practical application of these patterns in the investment activities. The US stock market as an integral part of the (...) World financial market was chosen as a basis for research. The research was conducted by statistical processing of data on the values of the broad market index Standard & Poor's-500 for the period from 1950 to 2019. The study showed that the formation of forecasts about the annual growth or decline of the stock market index using the seasonal-cyclic patterns "The January barometer" and "The first five days of January" can show a high result in cases where the growth of the Standard & Poor's-500 index by the results of both the first five days of January and the results of the entire month of January of this year is a positive value. Otherwise, forecasting based on these instruments is not advisable. The best results (the forecast efficiency was 93.3%) were achieved with the joint application of both cyclic patterns. The practical application of the research results makes it possible to improve the efficiency of investment activities in international financial markets. (shrink)

The undergraduate philosophy major is often seen as an irrelevant degree. While this may be attributed to a number of causes, it is also occasion for academic philosophers to reevaluate pedagogical methods at the undergraduate level. The author evaluates typical pedagogical methods and argues that overemphasizing epistemological goals of philosophical investigation (e.g. truth and justification) instrumentalizes the process of inquiry and stifles students’ philosophical imagination, resulting in the impression of philosophy’s irrelevance. An alternative model is offered on the basis of (...) John Dewey’s pattern of inquiry. It is argued that a pedagogy that attends to Dewey’s five phases of inquiry promotes greater attention to the process of inquiry itself, which emphasizes knowledge as social, open to revision, and pertinent to students’ needs and interests. The author concludes by considering the philosophical implications of implementing such a pedagogy. (shrink)

With rapid changes in urban living today, peoples’ behavioural patterns and spatial practices undergo a constant process of adaptation and negotiation. Using “house” as a laboratory and everyday life and spatial relations of residents as a framework of analysis, the paper examines the spatial planning concepts in traditional and contemporary Iranian architecture and the associated socio-cultural practices. Discussions are drawn upon from a pilot study conducted in the city of Kerman, to investigate ways in which contemporary housing solutions can better (...) cater to the continually changing socio-cultural lifestyles of residents. Data collection for the study involved a series of participatory workshops and employed creative visual research methods, participant observation and semi structured interviews to examine the interlacing of everyday socio-spatial relations and changing perception of identity, belonging, socio-cultural and religious values and conflict. The inferences from the study showcases the emerging social and cultural needs and practices of people manifested through the complex relationship between residents, the places in which they live, and its spatial planning and organisation. For a better understanding of this complex relationship, the paper argues the need for resituating spatiality as a socio-cultural paradigm. (shrink)

This thesis investigates how recurrent face-to-face social interactions engender relatively invariant patterns of being together that cause those who instantiate them to act in ways that support their reproduction. Existing accounts within both cognitive science and sociology offer important insights into the consideration of patterns of being together. However, given their explanatory strategies, they struggle to integrate both ‘social’ and ‘individual’ levels of explanation. Herein a compatibilist account is developed, intended as a ‘third way’ that obviates the limitations of existing (...) accounts. This compatibilist account — by integrating insights from across disciplines and theoretical frameworks — develops a philosophical vocabulary with which to maintain explanatory consistency when articulating patterns of being together and moving between individual and social levels of explanation. It relies heavily on an extension of the enactive notion of autonomous habits to the social domain, redescribing patterns of being together as habituated participatory sense-making frames. Participatory sense-making frames result from processes of coenhabiting, i.e. processes of ongoing social habit making implicit in the dynamics of recurrent social interactions. Such processes are one primary means by which we produce and reproduce the relatively stable forms that characterise our shared worlds. These habituated frames embed much of the normativity of social life, and can serve our felicitous coordinations therein, allowing us to feel well situated, particularly in contexts within which we have some history of interacting. However, when they are not well aligned, they lead to tensions that result in either the production of novel frames or breakdowns in social interaction. The account developed has implications for many domains of human action, from psychotherapy to epistemology, and from critical studies to the development of political and ecological praxes. (shrink)

The terms ‘verification’ and ‘validation’ are widely used in science, both in the natural and the social sciences. They are extensively used in simulation, often associated with the need to evaluate models in different stages of the simulation development process. Frequently, terminological ambiguities arise when researchers conflate, along the simulation development process, the technical meanings of both terms with other meanings found in the philosophy of science and the social sciences. This article considers the problem of verification and validation in (...) social science simulation along five perspectives: The reasons to address terminological issues in simulation; the meaning of the terms in the philosophical sense of the problem of “truth”; the observation that some debates about these terms in simulation are inadvertently more terminological than epistemological; the meaning of the terms in the technical context of the simulation development process; and finally, a comprehensive outline of the relation between terminology used in simulation, different types of models used in the development process and different epistemological perspectives. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Progress in philosophy is difficult to achieve because our methods are evidentially and rhetorically weak. In the last two decades, experimental philosophers have begun to employ the methods of the social sciences to address philosophical questions. However, the adequacy of these methods has been called into question by repeated failures of replication. Experimental philosophers need to incorporate more robust methods to achieve a multi-modal perspective. In this chapter, we describe and showcase cutting-edge methods for data-mining and visualization. Big data is (...) a useful investigatory tool for moral psychology, and it fits well with the Ramsification method the first author advances in a series of recent papers. The guiding insight of these papers is that we can infer the meaning and structure of concepts from patterns of assertions and inferential associations in natural language. (shrink)

This book has argued that problems of religious luck, especially when operationalized into concerns about doxastic risk and responsibility, can be of shared interest to theologians, philosophers, and psychologists. We have pointed out counter-inductive thinking as a key feature of fideistic models of faith, and examined the implications of this point both for the social scientific study of fundamentalism, and for philosophers’ and theologians’ normative concerns with the reasonableness of a) exclusivist attitudes to religious multiplicity, and b) theologically-cast but bias-mirroring (...) trait-ascriptions to religious insiders and outsiders. It is important to keep the descriptive/explanatory and normative concerns properly separated, but philosophy of luck and risk are relevant to both. More specifically, inductive risky theological strategies,we have argued, are a relevant concern both descriptively and normatively. The descriptive/explanatory relevance of measures of high inductive risk connects it with cognitive and social psychology of religion, while its normative relevance connects with critical concerns with epistemology of testimony, the epistemology of disagreement, and the ethics of belief. A research program to examine fideistic orientation and its relation to epistemically risky doxastic strategies is one of potentially numerous research programs on which philosophers and psychologists might work collaboratively. So this concluding chapter of our study culminates with the outline of a proposed research program at the intersection of shared concerns. I term this research program CICI, because it examines what lies at the intersection of CSR’s standing interest in the appeal of counter-intuitive ideas, and our own study’s focus on the fideistic penchant for counter-inductive thinking. Religious Studies scholars typically focus on particular traditions and teachings, while CSR scholars tend to eschew such content-focused approaches in favor of a study of evolutionary and hence generic or trans-religious functions and processes. I argue that CICI has the added benefit of effectively mediating this generic-specific contrast between CSR and Religious Studies, allowing CSR research to be more closely connected with and relevant to comparative fundamentalism. (shrink)

This study explores the application of deep learning and image processing techniques for the detection of skin cancer. Leveraging convolutional neural networks (CNNs) and advanced image processing algorithms, the proposed system aims to accurately identify and classify skin lesions. The model is trained on a diverse dataset, encompassing various skin conditions, to enhance its diagnostic capabilities. Results demonstrate the potential for automated and reliable skin cancer detection, offering a promising approach for early diagnosis and improved patient outcomes. The (...) deep learning model is trained on a comprehensive dataset, including various types of skin lesions and conditions, to ensure robust performance across a spectrum of cases. Image preprocessing techniques are employed to enhance feature extraction and improve the model's ability to discern subtle patterns indicative of skin cancer. The study further investigates the interpretability of the deep learning model, employing techniques to visualize and understand the decision-making process. This transparency aids in building trust in the system's predictions and facilitates collaboration between AI and medical practitioners. As the landscape of healthcare continues to evolve, the combination of deep learning and image processing offers a scalable and efficient solution for skin cancer detection, fostering advancements in early intervention and personalized patient care. (shrink)

Abstract: The development of technology pushes human to increase productivity by changing manual system to automatic system. Modernization affect almost all aspect especially in art. Batik is a ancestor legacy of indonesian people that recogized by UNESCO as culture legacy of Indonesia yet batik industries are not develop as much. Modernization in batik art is expected to optimize its productivity. This research design algorithm and the program in MATLAB that used for controlling movement protype of batik maker 2 dof cartesian (...) robot. The input data is a picture that will be processed to obtain the order of coordinate edge. Then it procesed and sent to plantby using MATLAB based on Graphical User Interface (GUI). The 2 dof Cartesian robot and a pen (end effector) is used to draw a pattern. A servo motor is installed to activate end effector and two stepper motor coupled with thread mechanic that used to move it towards the X and Y axis. Testing is done by sending coordinat orders to the plant or by observing drawing process order. Obtained error from program and algorithm testing is 2,32% . (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate extensive preprocessing before being analyzed (...) by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

George Sword an Oglala Lakota (1846–1914) learned to write in order to transcribe and preserve his people’s oral narratives. In her book Delphine Red Shirt, also Oglala Lakota and a native speaker, examines the compositional processes of George Sword and shows how his writings reflect recurring themes and story patterns of the Lakota oral tradition. Her book invites further studies in several areas including literature, translation studies and more. My review of her book suggests some ways it could be used (...) as a primary resource book in developing curricula in Indigenous philosophy . (shrink)

The nucleus accumbens orchestrates processes related to reward and pleasure, including the addictive consequences of repeated reward (e.g., drug addiction and compulsive gambling) and the accompanying feelings of craving and anhedonia. The neurotransmitters dopamine and endogenous opiates play interactive roles in these processes. They are released by natural rewards (i.e., food, water, sex, money, play, etc.) and are released or mimicked by drugs of abuse. Repeated drug use induces conditioned down-regulation of these neurotransmitters, thus causing painful suppression of everyday pleasure. (...) As with many spiritual traditions, Buddhism provides strong advice against the pursuit of worldly pleasures to attain the ‘‘good life.’’ In contrast, many forms of meditation give rise to an immense and abiding joy. Most of these practices involve ‘‘stilling the mind,’’ whereby all content-laden thought (e.g., fantasies, daydreams, plans) ceases, and the mind enters a state of openness, formlessness, clarity, and bliss. This can be explained by the Buddhist suggestion that almost all of our everyday thoughts are a form of addiction. It follows that if we turn off this internal ‘‘gossip of ego,’’ we will find relief from the biochemical dopamine/opiate down-regulation, which is, perhaps, the perpetual concomitant of our daily rumination. (shrink)

Usually technological innovation and artistic work are seen as very distinctive practices, and innovation of technologies is understood in terms of design and human intention. Moreover, thinking about technological innovation is usually categorized as “technical” and disconnected from thinking about culture and the social. Drawing on work by Dewey, Heidegger, Latour, and Wittgenstein and responding to academic discourses about craft and design, ethics and responsible innovation, transdisciplinarity, and participation, this essay questions these assumptions and examines what kind of knowledge and (...) practices are involved in art and technological innovation. It argues that technological innovation is indeed “technical”, but, if conceptualized as techne, can be understood as art and performance. It is argued that in practice, innovative techne is not only connected to episteme as theoretical knowledge but also has the mode of poiesis: it is not just the outcome of human design and intention but rather involves a performative process in which there is a “dialogue” between form and matter and between creator and environment in which humans and non-humans participate. Moreover, this art is embedded in broader cultural patterns and grammars—ultimately a ‘form of life’—that shape and make possible the innovation. In that sense, there is no gap between science and society—a gap that is often assumed in STS and in, for instance, discourse on responsible innovation. It is concluded that technology and art were only relatively recently and unfortunately divorced, conceptually, but that in practices and performances they were always linked. If we understand technological innovation as a poetic, participative, and performative process, then bringing together technological innovation and artistic practices should not be seen as a marginal or luxury project but instead as one that is central, necessary, and vital for cultural-technological change. This conceptualization supports not only a different approach to innovation but has also social-transformative potential and has implications for ethics of technology and responsible innovation. (shrink)

The classification and prediction of blood group is most important aspect for the transfusion of blood. In present situations, they are done in laboratory using manual process. This is a time-consuming process and hence need manual energy. To overcome the constraints in the prediction of conventional methods in blood group, the artificial intelligence is implemented. This includes the image processing techniques with segmentation process to detect the classification of blood group. They are done through MATLAB simulations to detect the (...) blood components. Through collecting the blood samples and processing and classified the images with feature extraction leads to govern the variety of blood based on ABO and Rh group systems. To overcome the drawbacks in the conventional process, the developed methodology is implemented. This reduces various manual errors. Thus, the image processing technique with artificial intelligence helps to determine the classification of blood rapidly without any errors. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and efficiencies (...) of phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

This thesis uses a biosocial anthropological approach to explore the wide variety of human-environmental interactions exhibited by the Shuar of the Ecuadorian Amazon. Based on 10 months of ethnographic inquiry and deploying a comparative and evolutionary perspective, this thesis focuses primarily on the holistic nature of ancient subsistence patterns. I delve into the adaptive strategies exhibited by the contemporary Shuar and attempt to delineate the socio-ecological, ritual and cosmological significance of these patterns of behaviour. I suggest how subsistence adaptations, like (...) hunting, which have been greatly diminished in the Shuar communities, nevertheless reveal particular forms of cross-species interactions likely deeply rooted in coevolutionary processes. As clearly indicated by informants' oral histories, hunting patterns and meat-eating behaviour show complex trophic, socio-structural and ritual associations between humans and animal taxa. I then hypothesise that food taboos against eating particular animals (mostly large mammals) may have functioned as mechanisms for the preservation of prey and likely arose from ancient relationships with extinct megafauna. Further, concerning horticulture, a subsistence strategy still practised by the Shuar, I reveal how specific forms of human-plant interactions, such as those with Manihot esculenta Crantz and Guadua spp., inform patterns of patterns of growth, fertility and reproduction. This Amazonian form of human-plant coevolution exhibited by traditional horticulture underscores, in principle, basic and higher-order relational processes exemplified by the biosocial, ritual, ontological, and myth-cosmological associations between organismic kingdoms. Concerning the use of medicinal and psychoactive plants, I show how these kinds of botanical knowledge frequently intersect, thus representing adaptive behaviours highly dependent on embodied and cognitive engagements with plant materials. Both conceived as elementary forms of cure and primary triggers of metaphysical formulations, relationships with these elements of the plant kingdom are likely to be well-embedded in our evolutionary history. Lastly, the thesis' attempt at cross-species comparisons linked to different evolutionary periods (i.e., archaic hominins, extant forager-horticulturalists and non-human primates) may shed new light on the nature of complex adaptive patterns exhibited by Indigenous peoples in general and the Shuar in particular. (shrink)

This paper describes a method for the analysis of the evolutionary path of a complex, dynamic, and contingent social phenomenon in an empirical setting. Given empirical evidence of a surprising or anomalous fact, which contradicts the prediction of the wide-acknowledged theory, the goal is to formulate a plausible explanation based on the context of occurrence, taking a holistic and historical point of view. The procedure begins by translating theoretical propositions into grammar rules to describe patterns of either individual action or (...) interaction that may occur within the hypothesized social system. The result is a category of social process in which the objects are types of decision-making events carried by a stable community of actors over time, and the relationships between them are state transitions revealed in the sequences of event outcomes. Therefore, structural comparison between pairs of representative instances result in an extension of the category of social processes, relying on the configurations of contextual conditions that enable the occurrence of the new event outcome in specific empirical settings. (shrink)

This study was conducted to determine the problems faced by Syrian students under temporary protection in the educational process based on teachers' opinions and to provide solutions by examining the applications made by teachers to eliminate these problems. In this study, a phenomenological pattern, one of the qualitative research methods, was used. Research data was obtained through face-to-face interviews using semi-structured interview forms. The study group consists of 40 teachers working in kindergarten, elementary, secondary, and high schools, who were (...) reached using the easily accessible case sampling technique, one of the purposeful sampling methods. To achieve the research purpose, answers were sought to the following questions: 1. According to the teachers' opinions, what do Syrian immigrant students face the main problems at school? 2. What are the teachers’ practices to solve the problems that Syrian immigrant students face? The obtained data were analyzed using the content analysis method. According to the research findings, the main problems of Syrian immigrant students are classified as socio-cultural, language, family, COVID-19, psychological, structural conditions of schools, and institutional issues. Teachers’ practices to solve these problems students face are determined as strengthening the physical and social structure of the school, teaching Turkish as a second language, and the role of family in education. According to the study findings, strengthening the physical structures of the schools and providing the necessary tools and educational materials will make it easier for Syrian immigrant students to learn and succeed in solving the problems they face in schools. In addition, improving the professional qualifications of existing teachers has emerged as an essential need. For this purpose, they must be supported by in-service training on vocational, inclusiveness and multicultural education. School-family-student cooperation has emerged as an effective way to solve problems. It is seen that creating a positive school climate with the school-family-student collaboration created by the efforts of some teachers makes a positive contribution to school commitment and education. This dissemination will facilitate the solution of problems. Since the study results are obtained from the participants’ experiences and inferences, this study is expected to contribute to educational practitioners, policymakers, and related literature. (shrink)

(article sent to participants of the Lindau meeting where a talk on this subject was given) David Bohm suggested that some kind of implicate order underlies the manifest order observed in physical systems, while others have suggested that some kind of mind-like process underlies this order. In the following a more explicit picture is proposed, based on the existence of parallels between spontaneously fluctuating equilibrium states and life processes. Focus on the processes of natural language suggests a picture involving an (...) evolving ensemble of experts, each with its own goals but nevertheless acting in harmony with each other. The details of how such an ensemble might function and evolve can translate into aspects of the world of fundamental physics such as symmetry and symmetry breaking, and can be expected to be the source of explicit models. This picture differs from that of regular physics in that goal-directedness has an important role to play, contrasting with that of the conventional view which implies a meaningless universe. (shrink)

This study offers a biological classification of learning, categorizing the nervous system into waking, dream, and deep sleep states, all guided by the soul. It organizes intelligence based on processes of learning and unlearning, independent of age, while integrating evolutionary and social dimensions. The work also establishes Vedanta as a foundational framework, rejecting certain Western psychological concepts and instead incorporating the soul (consciousness) as an active agent working at the cellular level, specifically at the center of the nucleus.

Farmers are often concerned about plant disease since it can greatly affect crop productivity and quality. Expert manual inspection is required in traditional techniques of identifying plant diseases, which can be time- and money-consuming. Deep learning algorithms have made automated plant disease detection systems more practical. Convolutional neural networks (CNNs) are used in our proposed deep learning- based technique for the diagnosis of plant diseases. The suggested system uses plant photos as input to determine the presence of illnesses in the (...) plants and then provides treatment recommendations. The CNN can learn the visual patterns connected to various diseases because it is trained using a sizable dataset of photos of healthy and diseased plants. This system also includes IOT so that after the result is acquired, it will communicate via IOT. Several publicly accessible datasets of plant photos were used in our studies to gauge the performance of the suggested approach. The experimental results show that the suggested approach detects several plant diseases, such as tomato bacterial spot, potato early blight, and apple black rot, with high accuracy. Overall, our approach shows the promise of deep learning methods for automatically detecting plant diseases, which can assist farmers in identifying and treating plant diseases more quickly and successfully. (shrink)

This essay explores the creation and evolution of life and consciousness through the lens of a biological framework for understanding the universe. The theory posits that the patterns inherent in biological systems mirror the underlying mathematical principles of the cosmos. Thus, every pattern that manifests from the universe’s “parent-pattern” contains a fundamental biological-pattern inherent to its function, revealing the objective nature and purpose of that thing. Examples include the way ocean currents resemble a circulatory system and how (...) socioeconomic phenomena mimic cellular order. These correspondences suggest that life and consciousness are products of the universe’s biologically-patterned processes, and understanding these patterns is crucial for humanity's survival, especially as human society and environment becomes more complex—requiring a truer understanding of reality reality and how to organize themselves within it. The paper further argues that historical and philosophical concepts, such as Atman and Brahman in the Upanishads, Pnimiyut and Chitzoniyut in Kabbalah (Judaism), Batin and Zahir in Sufism (Islam), and many more align with this framework. The essay emphasizes that aligning human society with these biological patterns, as seen in biomimicry, is necessary for continued survival and harmony with the universe. The essay provides scientific studies and analogies supporting these ideas, illustrating how biological patterns are reflected across different domains of knowledge. (shrink)

Pattern recognition is represented as the limit, to which an infinite Turing process converges. A Turing machine, in which the bits are substituted with qubits, is introduced. That quantum Turing machine can recognize two complementary patterns in any data. That ability of universal pattern recognition is interpreted as an intellect featuring any quantum computer. The property is valid only within a quantum computer: To utilize it, the observer should be sited inside it. Being outside it, the observer would (...) obtain quite different result depending on the degree of the entanglement of the quantum computer and observer. All extraordinary properties of a quantum computer are due to involving a converging infinite computational process contenting necessarily both a continuous advancing calculation and a leap to the limit. Three types of quantum computation can be distinguished according to whether the series is a finite one, an infinite rational or irrational number. (shrink)

What is reasoning? What is logic? What is math? Common sense tells us that concepts such as numbers, relations, and logical structures feel inherently familiar—almost intuitive. They seem so obvious, but why? Do they have deeper origins? What is the number? What is addition? Why do they work in this way? Basic axioms of math, their foundation seems to be very intuitive, but absolutely mysteriously appear to the human mind out of nowhere. In a way their true essence magically slips (...) away unnoticed from the consciousness, in an attempt to pinpoint its foundation. This paper delves into the fundamental nature of mathematics, logic, and rational reasoning, examining their "unreasonable effectiveness" in understanding and shaping the world. By drawing parallels between advancements in artificial neural networks and human cognition, the work introduces a novel perspective on intuition as the cornerstone of higher cognitive systems. Intuition is presented not only as a tool for pattern recognition but as the foundation upon which complex reasoning processes, such as mathematical abstraction and logical frameworks are constructed. This perspective redefines the role of consciousness, highlighting its capacity for innovation and exploration within abstract domains. Beyond theoretical insights, the paper outlines potential pathways for advancing artificial general intelligence by leveraging the interplay between intuition and conscious reasoning. (shrink)