This research aims to determine the effectiveness of the self-directed learning model and the discovery learning model assisted by focusky media on the historical analysis abilities and learning outcomes of students in history subjects. This type of research is a quasi-experiment with a sample size of 65 students at SMA Negeri 1 Bangorejo. The results of the t-test of historical analysis ability and learning outcomes, it shows that there is a significant difference with the difference (...) in the average posttest score for historical analysis ability 2.733 and the difference in the average posttest score for learning outcomes 3.059. Then, the historical analysis effectiveness test, it shows a medium category of 0.050, while the learning outcomes show a medium category of 0.053. This shows that experiment class 1 is better than experiment class 2 and the self-directed learning model has proven to be effective in improving historical analysis skills and learning outcomes. (shrink)

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the research landscape has transforming almost every extending field, including pharmaceutical research. The idea of drug discovery itself is very conventional and has long been criticized for being overly lengthy and expensive, which sometimes may take more than 10 years and billions of dollars to develop a certain drug. AI and ML formulate the future of the drug discovery process by using big data to provide preliminary (...) drug candidates more effectively. This paper overviews the innovations defined by AI and ML in the field of drug discovery, major achievements, techniques, and use cases. Additionally, we explore how AI algorithms can enter biological data, inspect drug-target relations, determine optimal drug design, and potentially recompose famous drugs. This means through using big data with the help of AI in the process of research, previously undisclosed patterns that help in developing effective treatments for patients are found. The paper also addresses the related issues and limitations in applying AI in this domain, namely, data quality issues, the interpretability of AI solutions, and some ethical concerns. Herein, to provide a concrete foundation to the concepts mentioned so far, we discuss the different AI applications and case studies in drug discovery from a survey of the available literature. The article also provides information regarding the methodologies used in AI-enabled drug discovery like deep learning, reinforcement learning, and natural language processing. Moreover, we compare the use of conventional and artificial intelligence methods, while demonstrating what is good and what maybe in both. The results section offers a review and an integration of the most recent objectives and recommendations for subsequent research instruction. Therefore, despite these prohibitive AI and ML forecasts for drug discovery improvement, continuous interaction between computational scientists, biologists, and regulatory authorities is functional to fully unlock this potential. (shrink)

Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including (...) data quality, regulatory hurdles, and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health. (shrink)

Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building (...) of simulative machine models before the advent of cybernetics are been investigated for the first time, on the basis of little known, and sometimes completely forgotten or unpublished, texts and figures. These pre-cybernetics projects can be considered as steps toward the “discovery” of a modelling methodology that has been fully developed by those more recent research programs, and that shares some of their central goals and key methodological proposals. -/- More info in Springer link: http://www.springer.com/new+%26+forthcoming+titles+%28default%29/book/978-1-4020-0606-7 -/- This book is the English translation of La scoperta dell'artificiale, Dunod/Masson, Milan, 1998. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the (...) proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

In this essay I argue that theorists such as Kohn and Mitra have been too hasty in pronouncing the superiority of problem-based learning over direct instruction.

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them (...) from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

The Movie Recommendation System using Machine Learning Techniques is a data-driven approach designed to provide personalized movie suggestions based on user preferences and historical data. This system leverages advanced machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid methods, to predict the most relevant movies for individual users. The system's primary goal is to enhance user experience by recommending movies that align with their tastes, thereby promoting user engagement and satisfaction. The recommendation process starts by collecting user (...) data, such as viewing history, ratings, and genre preferences, which are then processed to generate personalized suggestions. Collaborative filtering analyzes the behavior of similar users to recommend movies based on common interests, while content-based filtering focuses on movie features such as genre, director, actors, and plot summary. By combining both methods, the hybrid approach further refines the recommendation, ensuring diverse and accurate results. The model's effectiveness is evaluated using standard metrics like precision, recall, and F1-score, demonstrating its ability to accurately predict user preferences. The system can be integrated into various platforms, offering real-time recommendations and improving user satisfaction. This project can be expanded by incorporating more sophisticated algorithms, such as deep learning, to further enhance recommendation accuracy and scalability. Overall, this movie recommendation system aims to optimize content discovery and cater to diverse audience needs. (shrink)

Much of my working life has been devoted to trying to get across the point that we urgently need to bring about a revolution in the aims and methods of academic inquiry, so that the basic aim becomes to seek and promote wisdom rather than just acquire knowledge.

This developmental study seeks to develop a Project-Based learning prototype that integrates sustainable agriculture in teaching mathematics. Data was gathered through the in-depth interviews using the developed semi-structured questionnaire. The study used the Clarke and Braun thematic analysis to analyze the data collected. The researchers found the following themes: Profitability of Aquaponics, Requisite for Computational Skills, Integration of Aquaponics in Teaching Unit Conversion, and Promoting Competencies through Self-discovery as Elements in Crafting Teaching-Learning Package for Sustainable Agriculture. Based (...) on the result, the researchers developed a Project-Based Learning Prototype that integrates Sustainable Agriculture in teaching mathematics at the grade 7 level. It has been concluded that the proposed teaching-learning prototype should promote competencies through self-discovery, that is, using real-life situations rather than hypothetical ones to engage students in meaningful projects, develop critical thinking and problem-solving skills, and prepare them for future work and careers. Further, it is recommended that future research may investigate the effectiveness and implementation of the developed teaching-learning prototype. Also, school administrators should ensure educators have access to adequate resources, training, and help to implement aquaponics education effectively. (shrink)

This thesis examines the prospects for mechanical procedures that can identify true, complete, universal, first-order logical theories on the basis of a complete enumeration of true atomic sentences. A sense of identification is defined that is more general than those which are usually studied in the learning theoretic and inductive inference literature. Some identification algorithms based on confirmation relations familiar in the philosophy of science are presented. Each of these algorithms is shown to identify all purely universal theories without (...) function symbols. It is demonstrated that no procedure can solve this universal theory inference problem in the more usual senses of identification. The question of efficiency for theory inference systems is addressed, and some definitions of limiting complexity are examined. It is shown that several aspects of obvious strategies for solving the universal theory inference problem are NP-hard. Finally, some non-worst case heuristic search strategies are examined in light of these NP-completeness results. These strategies are based upon an isomorphism between clausal entailments of a certain class and partition lattices, and are applicable to the improvement of earlier work on language acquisition and logical inductive inference. (shrink)

This chapter explores the personal and professional obstacles faced by Humanities and Social Science scholars contemplating pre-publication of their scholarly work in an on-line network. Borrowing a theoretical framework from the radical educational theorist Ivan Illich, it also develops the idea that pre-publication networks offer higher education a bottom-up, decentralized alternative to business-modeled e-learning. If learners would only embrace this more anarchical medium, appreciating writing for pre-publication as a process of open-ended discovery rather than product delivery, then the (...) prospect of deinstitutionalizing e-learning could become an exciting new reality. The chapter’s methodology is a combination of normative-theoretical analysis and small sample (n=2) case study based on the author’s personal and professional experiences. While the generalizability (or external validity) of the conclusions is limited, the author hopes to motivate further inquiry into the connection between on-line pre-publication and e-learning. (shrink)

The Relationship Between Language, Mathematics, Information, Knowledge, and New Discoveries in Understanding the Nature of Consciousness -/- Introduction -/- The nature of consciousness has long been a subject of philosophical and scientific inquiry. As human intelligence evolved, the invention of language, mathematics, and information systems became fundamental tools in shaping how we perceive and understand reality. These intellectual advancements not only reflect consciousness but also influence its development. By examining the interplay between language, mathematics, information, knowledge, and new discoveries, we (...) can gain deeper insight into how consciousness emerges, evolves, and expands. -/- Language: The Foundation of Conscious Thought -/- Language is the primary medium through which consciousness expresses itself. It allows individuals to articulate thoughts, share experiences, and construct complex ideas. Without language, higher-order thinking and self-reflection would be severely limited. -/- 1. Language and Self-Awareness – Internal dialogue, or the “voice in our head,” is a critical component of consciousness. It enables introspection, planning, and the ability to reflect on past experiences. -/- 2. Language and Social Consciousness – Through shared language, individuals contribute to collective intelligence. Cultural knowledge, traditions, and ideologies are preserved and transmitted across generations. -/- 3. Language and Abstract Thinking – Words enable humans to discuss non-physical concepts such as justice, morality, and the nature of existence, expanding the boundaries of conscious thought. -/- As language evolves, so does consciousness, leading to new ways of perceiving and interpreting reality. -/- Mathematics: The Language of Patterns and Conscious Awareness -/- Mathematics is often described as a universal language that reveals the underlying order of the universe. It provides structure to human cognition and enhances our ability to predict, measure, and manipulate reality. -/- 1. Mathematics and Logical Thought – Mathematical reasoning mirrors the logical structures of conscious thought. It allows for problem-solving, abstraction, and deductive reasoning. -/- 2. Mathematics and Scientific Discovery – From physics to neuroscience, mathematical models describe the fundamental principles governing existence, furthering our understanding of consciousness itself. -/- 3. Mathematics and Artificial Intelligence – The development of AI and machine learning relies on mathematical principles, raising questions about whether artificial systems can achieve a form of consciousness. -/- If consciousness operates under universal laws, mathematics may be the key to deciphering its principles. -/- Information and Knowledge: The Evolution of Consciousness -/- Information is the raw material from which knowledge is constructed. The accumulation, processing, and application of information drive the expansion of consciousness. -/- 1. Memory and Learning – The ability to store and recall information enhances cognitive abilities, shaping an individual’s awareness of the world. -/- 2. Collective Intelligence – The sharing of information across societies leads to cultural evolution, technological advancements, and the refinement of knowledge. -/- 3. External Storage of Consciousness – Books, digital databases, and AI systems act as extensions of human consciousness, preserving and expanding knowledge beyond biological limitations. -/- The more information consciousness can process, the more it expands its awareness and potential for discovery. -/- New Discoveries: The Feedback Loop of Expanding Consciousness -/- Scientific and philosophical discoveries continuously reshape how consciousness understands itself and the universe. Each breakthrough: -/- 1. Expands Perception – Tools like microscopes and telescopes extend human awareness beyond natural sensory limitations. -/- 2. Challenges Established Beliefs – Paradigm shifts force consciousness to adapt to new realities, redefining truth and understanding. -/- 3. Integrates Technology and Consciousness – Advancements in neuroscience, quantum mechanics, and artificial intelligence suggest that consciousness may be an emergent phenomenon shaped by computational and environmental factors. -/- The cycle of discovery feeds back into language, mathematics, and knowledge, leading to new levels of understanding and further expanding consciousness. -/- Conclusion: Consciousness as a Self-Perpetuating System -/- The relationship between language, mathematics, information, knowledge, and discovery forms a self-reinforcing loop that drives the evolution of consciousness. Each component influences and strengthens the others, creating a dynamic system of intellectual and cognitive growth. -/- Language provides structure for thought and communication. -/- Mathematics reveals underlying patterns of reality and enhances logical reasoning. -/- Information and knowledge serve as the building blocks of learning and awareness. -/- New discoveries expand the limits of consciousness, redefining human potential. -/- If consciousness operates under natural laws, this interconnected system may represent the fundamental process by which awareness evolves. The more humanity advances in these areas, the more profound our understanding of consciousness—and reality itself—becomes. -/- . (shrink)

Aim: This study attempted to assess the organizational resiliency in the implementation of Basic Education School Learning Continuity Plan and to design a resilient basic education school learning continuity plan. -/- Methodology: The study used mixed method which is quantitative and qualitative. The data for the quantitative part of the study were gathered through a survey with the 19 school heads and 345 teacher- respondents while the qualitative method was conducted through focus group discussion interview with 19 key (...) informants who were composed of the teachers, school heads and stakeholders. The research instrument that was used for the perceived organizational resiliency was a survey questionnaire that was validated by the experts and which the reliability was tested through Cronbach’s alpha with excellent level of internal consistency. The statistical treatment for quantitative results used mean and standard deviation to determine the perception of the respondents with regards to the organizational resiliency on the implementation of the BE-SLCP and Pearson Product Moment Correlation Coefficient, testing its significance at the 0.05 level was used in finding out the relationship between the evaluation of the basic education school learning continuity plan and school performance indicators and organizational resiliency. While content analysis was employed for the qualitative results. -/- Results: The salient findings of the study were as follows: The result showed that the rate of enrollment during pandemic is low while the drop-out rate fostered a positive rate. The school heads and teacher respondents perceived the schools’ organizational resiliency as high. Based on the feedback of the key informants the lessons learned from the implementation of Basic Education School Learning Continuity Plan are strong collaboration, consistent communication, and enhanced ICT Skills. The best practices that were observed are sustained partnership and continuous implementation of programs, projects, and activities. The challenges encountered include authenticity of assessment and low capability of learning facilitators at home. The respondent assessed the developed learning continuity plan as very highly satisfactory. -/- Conclusion: The organizational resiliency of schools is high in its performance of continuing education during pandemic. The study suggests that the schools may developed creative strategies on increasing enrolment; the school may strengthen its school-based management level to ensure that the plans and goals for continuity of education will be achieved; the school may also consider strengthening the stakeholders’ involvement and the school may consider the strategy of close coordination and constant monitoring and follow-ups with parents/guardians on the implementation of learning delivery. (shrink)

In this paper, we focus on the most efficacious AI applications for life extension and anti-aging at three expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the existing research and commercial work performed by a select number of startups and academic projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension are geroprotector-combination discovery, detection of aging biomarkers, and personalized anti-aging therapy. These advances could help currently (...) living people reach longevity escape velocity and survive until more advanced AI appears. When AI comes close to human level, the main contribution to life extension will come from AI integration with humans through brain-computer interfaces, integrated AI assistants capable of autonomously diagnosing and treating health issues, and cyber systems embedded into human bodies. Lastly, we speculate about the more remote future, when AI reaches the level of superintelligence and such life-extension methods as uploading human minds and creating nanotechnological bodies may become possible, thus lowering the probability of human death close to zero. We suggest that medical AI based superintelligence could be safer than, say, military AI, as it may help humans to evolve into part of the future superintelligence via brain augmentation, uploading, and a network of self-improving humans. Medical AI’s value system is focused on human benefit. (shrink)

Deep learning, a transformative force in computational biology, has reshaped biological data analysis and interpretation terrain. This review delves into the multifaceted role of deep knowledge in this field, exploring its historical roots, inherent advantages, and persistent challenges. It investigates explicitly its application in two pivotal domains: DNA sequence classification, where it has been used to identify disease-causing mutations, and protein structure prediction from sequence data, where it has enabled the accurate determination of protein tertiary structures. Moreover, it offers (...) a glimpse into the future trajectory of this dynamic field, sparking intrigue and excitement about the potential of deep learning. Deep learning, a powerful tool in computational biology, can be traced back to the inception of 'threshold logic,' a fusion of algorithmic principles and mathematical frameworks devised to mimic cognitive processes. This seminal breakthrough, which introduced the concept of a threshold function that could be used to model complex decision-making processes, paved the way for deep learning to decipher intricate patterns embedded within vast biological datasets. Deep learning presents many benefits, including precise disease diagnosis, novel drug discovery, and personalized medicine facilitation. Its prowess lies in handling vast, intricate datasets while enhancing generalization capabilities, a testament to its evolution and development that we can appreciate. (shrink)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it (...) assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)

This paper comprehensively examines Antoine Henri Becquerel’s discovery of radioactivity in 1896, a groundbreaking scientific advancement often viewed through serendipity. This case study explores the typologies of serendipity and investigates the conditions that foster its occurrence. A detailed study of Becquerel’s investigations reveals that his discovery aligns with a Walpolian type of serendipity, characterized by true serendipity heavily influenced by unforeseen experimental results. This paper emphasizes the role of bisociation, a cognitive process associating previously disconnected concepts, in Becquerel’s (...) discovery, challenging the view that his discovery was merely a chance event. Instead, it argues that Becquerel’s discovery represents an intricate interplay of logic and chance, exemplifying the Walpolian type of serendipity. Moreover, by examining Becquerel’s experimental design, results, and innovative approach, this paper illustrates that his discovery adheres to the fundamental aspects of the scientific method, albeit executed in a non-linear and iterative manner. The process and context of Becquerel’s discovery provide valuable insights into scientific knowledge inception, progression, and definition, underscoring the intertwined roles of serendipity and scientific inquiry in advancing science. (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is (...) a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning (...) models (DLMs) is epistemically problematic in the context of justification, though not in the context of discovery. -/- In this paper, I aim to show that a similar epistemological concern is echoed in the epistemology of imagination literature. It is traditionally held that, given its black box features, reliance on the imagination is epistemically problematic in the context of justification, though not in the context of discovery. The constraints-based approach to the imagination answers the epistemological concern by providing an account of how we can rely on the imagination in the context of justification by way of constraints. I argue by analogy that a similar approach can be applied to the opaque DLM case. Ultimately, my goal is to explore just how far this analogy extends, and whether a constraints-based approach to opaque DLMs can answer the epistemological concern surrounding their black box features in the context of justification. -/- (Note that this paper is IN PROGRESS and UNPUBLISHED). (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor”, that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led (...) to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments. (shrink)

The central thesis of this book is that we need to reform philosophy and join it to science to recreate a modern version of natural philosophy; we need to do this in the interests of rigour, intellectual honesty, and so that science may serve the best interests of humanity. Modern science began as natural philosophy. In the time of Newton, what we call science and philosophy today – the disparate endeavours – formed one mutually interacting, integrated endeavour of natural philosophy: (...) to improve our knowledge and understanding of the universe, and to improve our understanding of ourselves as a part of it. Profound discoveries were made, indeed one should say unprecedented discoveries. It was a time of quite astonishing intellectual excitement and achievement. And then natural philosophy died. It split into science on the one hand, and philosophy on the other. This happened during the 18th and 19th centuries, and the split is now built into our intellectual landscape. But the two fragments, science and philosophy, are defective shadows of the glorious unified endeavour of natural philosophy. Rigour, sheer intellectual good sense and decisive argument demand that we put the two together again, and rediscover the immense merits of the integrated enterprise of natural philosophy. This requires an intellectual revolution, with dramatic implications for how we understand our world, how we understand and do science, and how we understand and do philosophy. There are dramatic implications, too, for education. And it does not stop there. For, as I show in the final chapter, resurrected natural philosophy has dramatic, indeed revolutionary methodological implications for social science and the humanities, indeed for the whole academic enterprise. It means academic inquiry needs to be reorganized so that it comes to take, as its basic task, to seek and promote wisdom by rational means, wisdom being the capacity to realize what is of value in life, for oneself and others, thus including knowledge, technological know-how and understanding, but much else besides. The outcome is institutions of learning rationally designed and devoted to helping us tackle our immense global problems in increasingly cooperatively rational ways, thus helping us make progress towards a good world – or at least as good a world as possible. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet (...) it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

We need urgently to bring about a revolution in universities around the world, wherever possible, so that they take their fundamental task to be, not to acquire and apply knowledge, but rather to help humanity learn how to resolve conflicts and problems of living in increasingly cooperatively rational ways, so that we may make progress towards a good, genuinely civilized, wise world. The pursuit of knowledge would be a vital but subsidiary task. I have argued for the urgent need for (...) such an academic revolution for nearly 50 years, ever since the publication of two books: What’s Wrong With Science? Towards a People’s Rational Science of Delight and Compassion, 1976, and From Knowledge to Wisdom: A Revolution for Science and the Humanities, 1984, both available free online. This article, written in preparation for a talk given to Pittsburgh University on the 15th September 2023, gives an outline of my baffled struggles with problems of philosophy, science, literature and life that led up to the discovery of the urgent need for an intellectual and institutional revolution in universities around the world, needed in order to galvanize the social revolution required to enable us to make progress to a world in which peace, justice, democracy, individual freedom, sustainable prosperity, the possibility of a good life, are available to all, insofar as that is attainable. (shrink)

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we (...) need a procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) a shift from categorical “responding or not” assessments to degrees of evidence; (2) the use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) the use of standardized probability yardsticks to convey those assessments as clearly as possible. (shrink)

Two fundamentally distinct approaches to the teaching of philosophy are contrasted: On the one hand, there is the “information-oriented” approach which has dominated classrooms and which emphasizes the understanding of historically important philosophical works. On the other hand, there is the “cognitive skills” approach. The two approaches may be distinguished under the headings of ‘knowing that’ as opposed to ‘knowing how’. This paper describes and discusses four perspectives relating to the teaching of cognitive skills: (i) the discovery-oriented approach, (ii) (...) Piagetian learning cycles, (iii) protocol analysis, and (iv) conceptual therapy. The latter approach reflects the author’s interest in helping students to develop “therapeutic” skills that enable them to identify and eliminate concepts which they employ in their thinking and which are incompatible with their own presuppositional bases and are therefore self-refuting. ●●●●● -/- 2022 UPDATE: The approach of this paper has been updated and developed further in Appendix II, "Epistemological Intelligence," of the author’s 2021 book _Critique of Impure Reason: Horizons of Possibility and Meaning_. The book is available both in a printed edition (under ISBN 978-0-578-88646-6 from Barnes & Noble, Amazon, and other booksellers) and an Open Access eBook edition (available through Philpapers under the book’s title and other philosophy online archives). (shrink)

In his recent article "Advent of Google means we must rethink our approach to education", Sugata Mitra argues that our education system needs to change. He suggests that the existence of modern technologies such as Google make the skills of the past obsolete. For Mitra, the only reason we continue to teach skills such as longhand multiplication is because we have some sort of romantic attachment to the past. In this short article I respond to his central claim that we (...) should replace the Intellectual Traditionalist form of education with a new system. I argue that Mitra description of the Intellectual Traditionalist system is a strawman. Further, I argue that it would be a mistake to replace Intellectual Traditionalism with a constructivist pedagogy. (shrink)

As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static (...) Code Analysis. The framework provides a systematic approach to identifying, tracking, and managing data dependencies across the entire BI ecosystem using advanced static code analysis techniques. Detailed methodologies, algorithms, and implementation considerations for static code analysis are discussed. This framework offers a robust solution for organizations seeking to enhance data quality, system reliability, and operational efficiency in their BI systems. Future research will expand on dynamic runtime analysis and machine learning- based methods to further enhance the framework. (shrink)

Humankind is currently confronted with a critical challenge that determines its very existence, not only on an individual, racial, or national level but as a whole species: the fight against climate change and environmental degradation. To win this battle, humanity needs innovations and non-linear thinking. Nature has long been a substantial information source for unthinkable discoveries that save human lives. The paper suggests that by understanding the nature, emergence, and mechanism of serendipity, the survival skill of humans, humanity can capitalize (...) on it to learn from nature and generate nature-based innovations that can address the climate and environmental degradation crises. In the era of Artificial Intelligence (AI) proliferation, AI will be a vital tool providing navigational and useful information to leverage the power of serendipity in climate and sustainability science. However, an eco-surplus culture that incorporates core values pertaining to nature and sees environmental sustainability as the conscience of the times needs to be built to direct and control the immense power of AI-leveraged serendipity. Insights applied in this paper are drawn from the book titled “A New Theory of Serendipity: Nature, Emergence and Mechanism”. (shrink)

This paper reports on an ongoing ARC Discovery Project that is conducting design research into learning in collaborative virtual worlds (CVW).The paper will describe three design components of the project: (a) pedagogical design, (b)technical and graphics design, and (c) learning research design. The perspectives of each design team will be discussed and how the three teams worked together to produce the CVW. The development of productive failure learning activities for the CVW will be discussed and there (...) will be an interactive demonstration of the project's CVW. (shrink)

Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and (...) biological insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. -/- Results: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. -/- Conclusions: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field. (shrink)

This three-part article reinforces crosscurrents between cybernetician Gordon Pask’s work towards creating responsive machines applied to theater and education, and Vygotsky’s theory, to advance sociohistorical approaches into the internet age. We first outline Pask’s discovery of possibilities of a neoclassical cybernetic framework for human–human, human–machine, and machine–machine conversations. Second, we outline conversation theory as an elaboration of the reconstruction of mental models/concepts by observers through reliance on sociocultural psychological approaches, and apply concepts like the zone of proximal development and (...) perezhivanie to Paskian aesthetic technologies. Third, we interpret Pask’s teaching/learning devices as zones of proximal development, and outline how Paskian algorithms in digital devices like THOUGHTSTICKER have been generalized on today’s internet, supplemented by corporate interests. We conclude that Paskian theory may offer understandings of the roles of internet technologies in transforming human thinking, and suggest (re)designing tools incorporating algorithms that contextually advance conceptual understanding that deviates from current indexing approaches. (shrink)

Difficulties in learning Ohm’s Law suggest a need to refocus it from the law for a part of the circuit to the law for the whole circuit. Such a revision may improve understanding of Ohm’s Law and its practical applications. This suggestion comes from analysis of the history of the law’s discovery and its teaching. The historical materials this paper provides can also help teacher to improve students’ insights into the nature of science.

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, (...) since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

Hermann Hesse's works often explore deep philosophical themes and the human quest for self-understanding and enlightenment. His writing draws heavily from Eastern philosophy, Jungian psychology, and Western existentialism, creating a rich tapestry of ideas that challenge and inspire readers. Hermann Hesse's philosophical exploration in his works offers profound insights into the human condition, emphasizing the importance of personal experience, the integration of dualities, and the interconnectedness of all life. His writings encourage readers to embark on their own journeys of self- (...) class='Hi'>discovery, recognizing that enlightenment is a continuous, evolving process. Here, we'll examine some of the key philosophical elements present in his most famous works. -/- The Search for Self-Identity At the heart of Siddhartha is the quest for self-identity. Siddhartha's journey represents the human struggle to understand one's true self and purpose in life. This theme resonates with existentialist philosophy, which emphasizes the individual's responsibility to create meaning in an inherently meaningless world. Siddhartha's refusal to accept predefined paths and doctrines underscores the existentialist belief in the necessity of personal experience and authenticity in the search for self-identity. -/- 1. Siddhartha: The Quest for Authenticity In Siddhartha, the protagonist's journey is a vivid representation of the search for self-identity. From the outset, Siddhartha is dissatisfied with the conventional teachings and practices of his Brahmin upbringing. His quest for authenticity drives him to leave his home and explore various paths, including asceticism, sensual indulgence, and finally, a contemplative life by the river. Siddhartha's journey reflects existentialist themes, particularly the idea that true self-identity cannot be handed down through tradition or external teachings. Instead, it must be discovered through personal experience and introspection. This aligns with the existentialist notion that individuals must create their own essence through their actions and choices, rather than conforming to predefined roles or expectations. -/- 2. Steppenwolf: The Duality of the Self In Steppenwolf, Hesse delves into the complexities of self-identity through the character of Harry Haller. Harry is torn between his human side, which longs for connection and meaning, and his "wolf" side, which is driven by instinct and isolation. This inner conflict illustrates the duality inherent in human nature and the struggle to reconcile these opposing aspects. Hesse's exploration of Harry's psyche resonates with the Jungian concept of the individuation process, where the integration of the conscious and unconscious parts of the self leads to wholeness. Harry's journey is a quest for self-identity that involves confronting and accepting the darker, shadowy parts of his personality. This process of self-discovery is both painful and liberating, underscoring the idea that true self-identity emerges from embracing all facets of one's being. -/- 3. Demian: The Role of the Unconscious Demian is another key work in which Hesse explores the search for self-identity, focusing on the protagonist Emil Sinclair's journey toward self-realization. Guided by the enigmatic Max Demian, Sinclair learns to listen to his inner voice and question societal norms. The novel emphasizes the importance of the unconscious mind in the search for self-identity, drawing on Jungian psychology. Sinclair's experiences highlight the tension between conforming to societal expectations and following one's inner truth. His journey reflects the existentialist idea that individuals must break free from external constraints to discover their true selves. Hesse suggests that the path to self-identity involves a deep engagement with the unconscious mind and an acceptance of one's inner conflicts and desires. -/- ………………………………….. The Concept of Impermanence -/- One of the central philosophical concepts in Siddhartha is impermanence, a fundamental tenet of Buddhist philosophy. The novel illustrates the transient nature of all things, emphasizing that nothing in life is permanent or unchanging. Siddhartha's experiences with love, wealth, and asceticism teach him that attachment to transient phenomena leads to suffering. This aligns with the Buddhist teaching of Anicca, which posits that recognizing and accepting impermanence is essential for achieving enlightenment. -/- Siddhartha: The Transient Nature of Life In Siddhartha, Hesse’s exploration of impermanence is central to the protagonist’s spiritual journey. Siddhartha’s experiences reflect the Buddhist teaching of Anicca, which posits that all things are in a constant state of flux and that clinging to anything impermanent leads to suffering. -/- Throughout the novel, Siddhartha undergoes various transformations—from a Brahmin’s son to an ascetic, a lover, a merchant, and finally, a ferryman. Each phase of his life teaches him about the transient nature of existence. His realization that neither extreme asceticism nor indulgence provides lasting fulfillment underscores the futility of seeking permanence in an impermanent world. -/- The river, a recurring symbol in the novel, epitomizes the flow of life and the constant change inherent in all things. Siddhartha’s enlightenment comes when he embraces the river’s wisdom, understanding that true peace lies in accepting the impermanence of all experiences. -/- Steppenwolf: The Ephemeral Nature of Identity Steppenwolf delves into the theme of impermanence through the internal conflict of its protagonist, Harry Haller. Harry’s struggle with his dual nature—the civilized man and the untamed wolf—reflects the fluidity and transient nature of identity. Hesse portrays identity not as a fixed entity but as a dynamic interplay of opposing forces. -/- The novel’s surreal episodes, particularly in the Magic Theater, emphasize the impermanent nature of reality and the self. Harry’s experiences in the theater reveal the illusory and ever-changing aspects of his perceptions and identity. Hesse suggests that true self-understanding requires embracing the impermanence and multiplicity of one’s nature. -/- The Glass Bead Game: The Impermanence of Human Endeavors -/- In The Glass Bead Game, Hesse explores the impermanence of human knowledge and cultural achievements. The game itself, a complex intellectual synthesis of arts and sciences, represents the pinnacle of human endeavor. However, the novel’s setting in a distant future where the game’s origins and significance have been largely forgotten underscores the transient nature of all human creations. -/- The protagonist, Joseph Knecht, ultimately realizes that the pursuit of intellectual perfection through the game is insufficient for a meaningful life. His decision to leave the Order and experience the impermanent, unpredictable world outside reflects his acceptance of life’s impermanence. Knecht’s journey illustrates that true wisdom involves embracing the fleeting nature of human existence and engaging fully with the present moment. -/- Hesse’s exploration of impermanence invites readers to reflect on their own lives and the ways in which the recognition of life’s fleeting nature can lead to greater wisdom and fulfillment. By embracing the impermanence of all things, Hesse suggests, individuals can attain a deeper appreciation of the present moment and the interconnectedness of all experiences. (shrink)

Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is a basis of our ability to manipulate, move, and transform objects of the physical world around us, to recognize them on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip (...) force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a cognitive task where the planning and strategic execution of hand movements is essential. Sensorial and cognitive processes underlying manual skills and/or hand-specific (dominant versus non-dominant hand) behaviors can be studied in a variety of contexts by probing selected measurement loci in the fingers and palm of the human hand. Thousands of sensor data recorded from multiple spatial locations can be approached statistically to breathe functional sense into the forces measured under specific task constraints. Grip force patterns in individual performance profiling may reveal the evolution of grip force control as a direct result of cognitive changes during task learning. Grip forces can be functionally mapped to from-global-to-local coding principles in brain networks governing somatosensory processes for motor control in cognitive tasks leading to a specific task expertise or skill. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic and task relevant hand movements in complex real-world precision task, are pointed out. (shrink)

In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense strategies, (...) the effectiveness of each defense methods, and finally we did comparison on effectiveness and potency of different adversarial attack and defense methods. We came to conclusion that adversarial attack will mainly be blackbox for the foreseeable future since attacker has limited or no knowledge of gradient use for NN model, we also concluded that as dataset becomes more complex, so will be increase in demand for scalable adversarial defense strategy to mitigate or combat attack, and we strongly recommended that any neural network model with or without defense strategy should regularly be revisited, with the source code continuously updated at regular interval to check for any vulnerability against newer attack. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate (...) how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by the modeler without much justification -- and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents -- roughly that agents are systems that would adapt their policy if their actions influenced the world in a different way. From this (...) we derive the first causal discovery algorithm for discovering agents from empirical data, and give algorithms for translating between causal models and game-theoretic influence diagrams. We demonstrate our approach by resolving some previous confusions caused by incorrect causal modelling of agents. (shrink)

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the (...) classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

If you start taking courses in contemporary cognitive science, you will soon encounter a particular picture of the human mind. This picture says that the mind is a lot like a computer. Specifically, the mind is made up of certain states and certain processes. These states and processes interact, in accordance with certain general rules, to generate specific behaviors. If you want to know how those states and processes got there in the first place, the only answer is that they (...) arose through the interaction of other states and processes, which arose from others... until, ultimately, the chain goes back to factors in our genes and our environment. Hence, one can explain human behavior just by positing a collection of mental states and psychological processes and discussing the ways in which these states and processes interact. This picture of the mind sometimes leaves people feeling deeply uncomfortable. They find themselves thinking something like: 'If the mind actually does work like that, it seems like we could never truly be morally responsible for anything we did. After all, we would never be free to choose any behavior other than the one we actually performed. Our behaviors would just follow inevitably from certain facts about the configuration of the states and processes within us.' Many philosophers think that this sort of discomfort is fundamentally confused or wrongheaded. They think that the confusion here can be cleared up just by saying something like: 'Wait! It doesn't make any sense to say that the interaction of these states and processes is preventing you from controlling your own life. The thing you are forgetting is that the interaction of these states and processes – this whole complex system described by cognitive science – is simply you. So when you learn that these states and processes control your behavior, all you are learning is that you are controlling your behavior. There is no reason at all to see these discoveries as a threat to your freedom or responsibility.'2 Philosophers may regard this argument as a powerful one, perhaps even irrefutable.. (shrink)

Modern scientific communication traditionally uses visual narratives, such as comics, for education, presentation of scientific achievements to a mass audience, and as an object of research. The article offers a three-level characterization of the interaction of comic culture and science in a diachronic aspect. Attention is focused not only on the chronological stages of these intersections, the expression of the specifics of the interaction is offered against the background of scientific and public discussions that accompany the comics–science dialogue to this (...) day. Within the framework of the first stage (the appearance and distribution of popular science and educational comics), the characteristics of comics content necessary for the different genesis are highlighted: documentary storytelling, educational practices of learning through drawing, active cooperation with well-known companies and institutions, informativeness and empathic involvement of the young reader in a heroico-romantic narrative of scientific discoveries and mastering nature. With the intensification of interdisciplinary approaches (the second stage), comics are increasingly involved in presenting scientific results within the most diverse fields. Comics-based research is becoming an interdisciplinary method and a widespread practical area with the corresponding formation of scientific tools (applied comics, data comics), forms of interdisciplinary interaction (graphic medicine, ethnography, narrative geography, urban comics, comics journalism, etc.), and scientific publications (“The Comics Grid: Journal of Comics Scholarship”, “Sequentials”). The national format of comics-based research is presented on the example of Ukrainian comics projects (historical, feminist comics). In the genesis of development, Comics Studies have gone from a field of research to disciplinary definitions. In the creation of the metadiscourse of the scientific direction (the third stage), the authors focused on scientific discussions, the formation of academic directions and approaches, and markers of disciplinary self-identification. Emphasis is placed on the unique phenomenon of the simultaneous concordance of various stages of the dialogue between comics and science, on the prolonged replication of successful inventions into modern experience, and the active testing of known narratives at new levels of a scientific presentation. (shrink)

The change in the organization of science education over the past fifty years is quickly recalled. Being its cultural bound the lack of a conception of the foundation of science, the multiple innovations have resulted as temporary improvements without a clear direction, apart from the technocratic goal of an automation of learning processes. The discovery of two dichotomies as the foundations of science suggests a pluralist conception of science, and hence the need to entirely renew science education, in (...) particular by introducing the notions of incommensurability and radical variations in meaning of the basic notions. This change was already anticipated by several proposals within each subject-matter. (shrink)

The paper identifies defining characteristics of the principal models of problem-solving behavior which are useful in developing a general theory of problem-solving. An attempt is made both to make explicit those disagreements between theorists of different persuasions which have served as obstacles to an integrated approach, and to show that these disagreements have arisen from a number of conceptual confusions: The conflict between information processors and behavioral analysts has resulted from a common failure to understand theoretical sufficiency, and hence these (...) theorists have been at a loss to understand one another. Two directions of research in problem-solving, mechanical algorithmic problem-solving and the psychology of invention, have been thought to be divergent, but in fact complement one another once it is clear that problem-solving involves neither discovery nor invention, but rather is a matter of perceptual encoding and processing. Successful problem-solving behavior can be described as adaptive, learning behavior in which organization skills are effectively associated with situationally determined perceptual encoding processes of the individual mechanical or biological problem-solver. (shrink)

In recent years, the advent of Artificial Intelligence (AI) has profoundly reshaped the way we access and utilize humanity's vast reservoir of knowledge. Equipped with this powerful tool, I have undertaken a thorough exploration of the Human Mind, motivated by a growing concern: the evident decline in students' enthusiasm for learning. More specifically, as a professor and researcher, my need for understanding has driven me to explore in depth what defines us as Human beings and what sets us apart (...) from other species. This investigative journey prompted me to reevaluate the evolutionary process, revisit Darwin's theories, and contemplate fundamental questions regarding the nature of time and reality. These reflections ultimately culminated in the formulation of a concept we call the ``Total Universe", a realm in which all possible events and interactions coexist. Building on this concept, we formulated a Theory of Consciousness rooted in the exploration of this Universe through our minds. This culminated in the emergence of the ``World of the Mind", which represents Human reality within the Total Universe, the fusion of all existing Human realities. Importantly, this theory not only integrates various theories of consciousness but also provides a clear framework for understanding what it might mean for Artificial Intelligence to attain consciousness. Hence, I invite you to embark on this journey of reflection, guided by this collection of written codes that encapsulate the discoveries made within the Total Universe through my mind, my reality. My perspective may resonate with yours, or it may not, and both are entirely valid. After all, each individual inhabits their own unique reality. (shrink)

The main aim of Samuel Hartlib was to provide an advancement of learning finalized to the amelioration of the material conditions of men and the pursuit of a religious peace, i.e., the unification of the Protestants. To this aim, inspired by Comenius, he devoted his efforts or gathering knowledge by the creation of a society or office of learned men (in technical fields, philosophy, and theology), and by the establishment of a network of correspondents (the Hartlib Circle). The method (...) of discovery underlying his program of advancement of learning was inspired by Bacon’s Novum Organum and by Jacopo Aconcio’s method of analysis, while the categorization and transmission of knowledge had to be based on commonplace books and artificial languages. His plan of economic improvement, to be fulfilled mainly through the amelioration of husbandry, was motivated by the Puritan Millenarianism to which he adhered. (shrink)

Visual analytics (VA) combines the strengths of human and machine intelligence to enable the discovery of interesting patterns in challenging datasets. Historically, most attention has been given to developing the machine component—for example, machine learning or the human-computer interface. However, it is also essential to develop the abilities of the analysts themselves, especially at the beginning of their careers. -/- For the past several years, we at the University of British Columbia (UBC)—with the support of The Boeing Company—have (...) experimented with various ways of preparing undergraduate students for VA. Although inspired by the need to prepare students to become visual analysts, the result turned out to be fairly general in scope, applicable to other analytical approaches, as well as more general research. In hindsight, this makes considerable sense. Although the visual component of VA is necessary, it is insufficient; many analytical activities at the human end involve nonvisual skills, such as effective decision-making and the ability to quickly focus on the relevant parts of a problem. -/- The result of this experimentation is a third-year undergraduate course titled Cognitive Systems 303 (COGS 303) that focuses on “VA unplugged”—that is, on developing investigative abilities prior to training on the VA systems themselves. It was felt that if students focused on developing basic analytical habits of thought prior to learning VA systems, these habits would be reinforced by subsequent practice on “live” systems. (shrink)

As is clear from the multiple references to knowledge in the proemium of fragment B1, Parmenides presented himself to his audiences as one who had achieved a profound insight into the nature of ‘what-is’. In support of this claim he conducted an elenchos or ‘testing’ of the ways of inquiry available for thinking, in the process revealing a set of sêmata or ‘signs’ indicating that what-is an eternal, indivisible, and unchanging plenum. In each of these respects, Parmenides was speaking the (...) language of discovery heard elsewhere in early Greek poetry. Similarly, his claim that we can neither learn nor know about what-is-not (hence must not say or think ‘it is not’) was justified by the ordinary meaning of the ancient Greek verbs for learning and knowing. Strikingly, Parmenides’ revisionary metaphysics rested in large measure on a widely shared view of what can be learned, known, and made known to others. -/- . (shrink)

Well elucidated and defined education policy is the most essential criteria for comprehensive progress of all human beings. From the time immemorial it is known that progress can be ushered only through education. A futuristic education policy both at the school and university level is extremely imperative. Countries at the global level have been adopting effective education policies to meet the changing needs of education and society at large. There is a need to shift educational approach from rote learning (...) to understanding, from exhaustive learning techniques to excitement of discovery and towards the joyful exploration. True education must encompass a synchronized working of body, mind and spirit. The ultimate aim of education must be to help the growing soul draw out the best and make it perfect for a noble use. The aim of education is not only to prepare an individual to succeed in life and society, but to reach the optimum. With this background, the paper is attempting at the theoretical level, to study the Integral philosophy, in the light of NEP 2020. It also tries to inquire into the modalities of Integral thinking, propose the application of the same in education and pedagogies to characterize its practice and goals in Integral Education. (shrink)