Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely (...) related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute political (...) agency? Or should this be seen, on the contrary, as an invitation for new voices and demands to enter into democratic assemblages? How might engagement with the more-than-human disrupt or extend theories of radical democracy? Hans Asenbaum and Amanda Machin discuss the human democratic subject both through radical democratic and new materialist lenses. They suggest that a new materialist decentred subject does not lose agency but further gains political responsibility in radical democracy. Jean-Paul Gagnon highlights the possibility of becoming through loss. In losing our anthropocentric arrogance—our understanding of being other and better than animal—we become more connected and discover our place in human-nonhuman assemblages. Melissa Orlie agrees that something can be gained through renouncing dominant human fantasies and presents a ‘radical democratic naturalism’. By renouncing land violence and acknowledging nature’s subjectivity, we can further nonexploitative radical democratic politics. This is where the notion of ‘tidalectic’ processes introduced by James L. Smith is helpful. Focusing on water and the Indigenous knowledges around rivers, he considers how democracy and community can be reimagined as plural, cyclical and attentive to the nonhuman with which the human is inevitably entangled. Diana Leong offers a different approach. Considering the nonhuman from the perspective of Black Studies demands an awareness to how blackness has long been constituted as non- or partially human. (shrink)

In conceptual modeling (CM) as a subdiscipline of software engineering, current proposed ontologies (categorical analysis of entities) are typically established through whole adoption of philosophical theories (e.g. Bunge’s). In this paper, we pursue an interdisciplinary research approach to develop a diagrammatic-based ontological foundation for CM using philosophical ontology as a secondary source. It is an endeavor to escape an offshore procurement of ontology from philosophy and implant it in CM. In such an effort, the CM diagrammatic language plays an important (...) role in contrast to dogmatic philosophical languages’ obsession with abstract entities. Specifically, this paper is about developing a descriptive (in contrast to formal) ontology that a modeler accepts as a supplementary account of reality when using thinging machines (TMs; i.e. a reality that uncovers the ontology of things that TM modeling discusses or “talks about,” akin to the ontology of natural language). Although existence is a well-established notion, we defend subsistence (Stoic term) as a supplementary mode of reality (e.g. reflection of event). The aim here is aligned toward developing CM notions and processes that are firm enough. Classical analysis of being per se (e.g. identity, substance, classes, objects) is de-emphasized in this work; nevertheless, philosophical concepts form an acknowledged authority to compare to. As a case study, such a methodology is applied to the notion of information that provides a common-sense understanding of the world. This application would enhance understanding of the TM methodology and clarify some of the issues that shed light on the question of the nature of information as an important concept in software engineering. Information is defined as about events; that is, it is about existing things. It is viewed as having a subsisting nature that exists only through being “carried on” by other things. The results seem to indicate a promising approach to define information and understand its nature. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction (...) are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

This article addresses the question of how ‘Country of Origin Information’ reports—that is, research developed and used to support decision-making in the asylum process—can be published in an ethical manner. The article focuses on the risk that published COI reports could be misused and thereby harm the subjects of the reports and/or those involved in their development. It supports a situational approach to assessing data ethics when publishing COI reports, whereby COI service providers must weigh up the benefits and (...) harms of publication based, inter alia, on the foreseeability and probability of harm due to potential misuse of the research, the public good nature of the research, and the need to balance the rights and duties of the various actors in the asylum process, including asylum seekers themselves. Although this article focuses on the specific question of ‘how to publish COI reports in an ethical manner’, it also intends to promote further research on data ethics in the asylum process, particularly in relation to refugees, where more foundational issues should be considered. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at (...) this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared with (...) others. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Synthetic biology aims at reconstructing life to put to the test the limits of our understanding. It is based on premises similar to those which permitted invention of computers, where a machine, which reproduces over time, runs a program, which replicates. The underlying heuristics explored here is that an authentic category of reality, information, must be coupled with the standard categories, matter, energy, space and time to account for what life is. The use of this still elusive category permits (...) us to interact with reality via construction of self-consistent models producing predictions which can be instantiated into experiments. While the present theory of information has much to say about the program, with the creative properties of recursivity at its heart, we almost entirely lack a theory of the information supporting the machine. We suggest that the program of life codes for processes meant to trap information which comes from the context provided by the environment of the machine. (shrink)

One of the most important ongoing debates in the philosophy of mind is the debate over the reality of the first-person character of consciousness.[1] Philosophers on one side of this debate hold that some features of experience are accessible only from a first-person standpoint. Some members of this camp, notably Frank Jackson, have maintained that epiphenomenal properties play roles in consciousness [2]; others, notably John R. Searle, have rejected dualism and regarded mental phenomena as entirely biological.[3] In the opposite camp (...) are philosophers who hold that all mental capacities are in some sense computational - or, more broadly, explainable in terms of features of information processing systems.[4] Consistent with this explanatory agenda, members of this camp normally deny that any aspect of mind is accessible solely from a first-person standpoint. This denial sometimes goes very far - even as far as Dennett's claim that the phenomenology of conscious experience does not really exist. (shrink)

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. By drawing an analogy from Kant’s incongruent counterparts, it posits two identical deterministic worlds -- one comprising a human agent and the other comprising a machine agent. These agents exhibit different types of information processing mechanisms despite their apparent sameness in a causal sense. By postulating the distinctiveness of human over machine intelligence, this paper resolves what it refers to as “the vantage point problem” – (...) namely, how to legitimize a determinist’s assertion of determinism by placing the determinist within the universe. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for (...) assessing the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. Specifically, it posits two identical deterministic worlds -- one comprising a human agent and the other comprising a machine agent. These agents exhibit different types of information processing mechanisms despite their apparent sameness in a causal sense. By postulating the distinctiveness of human over machine intelligence, this paper resolves what it refers to as “the vantage point problem” – namely, how to legitimize a determinist’s assertion (...) of determinism by placing the determinist within the universe. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial, natural sciences, and philosophy. The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as (...) info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents (...) some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the roles (...) of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

For any given society, its particular technology of communication has far-reaching consequences, not merely as regards social organization, but on the epistemic level as well. Plato's name-theory of meaning represents the transition from the age of primary orality to that of literacy; Wittgenstein's use-theory of meaning stands for the transition from the age of literacy to that of a second orality (audiovisual communication, electronic information processing). On the basis of a use-theory of meaning the problem of machine consciousness, (...) to which the later Wittgenstein again and again returned, is capable of a non-essentialist solution: appropriate changes in our form of life might well entail a radically different psychological language-game. (shrink)

Management faces replacement by automated processes. Workflow automation in the information processing sectors of the economy is changing the way information and knowledge workers do their jobs. I consider the changing nature of the information supply chain from the creation of knowledge in firms to the supply of information to consumers. The changing nature of data and the development of data science and machine learning methods that enable the analysis of unstructured data have meant that (...) what was once viewed as tacit knowledge is now just a problem in information processing. The rise of workflow automation in the IT industries and the emergence of the reproducible research movement in the sciences is leading to increased automation of the production of information in both industry and academia and this has profoundly changed the nature of the information supply chain. (shrink)

This paper proposes a new metaphysical framework for distinguishing between human and machine intelligence by drawing on Kant’s incongruent counterparts as an analogy. Specifically, the paper posits two deterministic worlds that are superficially identical but ultimately different. Using ideas from Wittgenstein, Gödel, and Cantor, the paper defines “deterministic knowledge” and investigates how this knowledge is processed differently in those two worlds. The paper considers computationalism and causal determinism for the new framework. Then, the paper introduces new concepts to illustrate why (...) human and machine agents display different causal characteristics in processing verbal information. Overall, the paper’s framework provides a theoretical basis for the uniqueness of the human mind. (shrink)

Throughout what is now the more than 50-year history of the computer many theories have been advanced regarding the contribution this machine would make to changes both in the structure of society and in ways of thinking. Like other theories regarding the future, these should also be taken with a pinch of salt. The history of the development of computer technology contains many predictions which have failed to come true and many applications that have not been foreseen. While we must (...) reserve judgment as to the question of the impact on the structure of society and human thought, there is no reason to wait for history when it comes to the question: what are the properties that could give the computer such far-reaching importance? The present book is intended as an answer to this question. The fact that this is a theoretical analysis is due to the nature of the subject. No other possibilities are available because such a description of the properties of the computer must be valid for any kind of application. An additional demand is that the description should be capable of providing an account of the properties which permit and limit these possible applications, just as it must make it possible to characterize a computer as distinct from a) other machines whether clocks, steam engines, thermostats, or mechanical and automatic calculating machines, b) other symbolic media whether printed, mechanical, or electronic and c) other symbolic languages whether ordinary languages, spoken or written or formal languages. This triple limitation, however, (with regard to other machines, symbolic media and symbolic languages) raises a theoretical question as it implies a meeting between concepts of mechanical-deterministic systems, which stem from mathematical physics, and concepts of symbolic systems which stem from the description of symbolic activities common to the humanities. The relationship between science and the humanities has traditionally been seen from a dualistic perspective, as a relationship between two clearly separate subject areas, each studied on its own set of premises and using its own methods. In the present case, however, this perspective cannot be maintained since there is both a common subject area and a new - and specific - kind of interaction between physical and symbolic processes. (shrink)

Life can be defined as combining two entities that rest on completely different physico-chemical properties and on a particular way of handling information. The cell, first, is a « machine », that combines elements which are quite similar (although in a fairly fuzzy way) to those involved in a man-made factory. The machine combines two processes. First, it requires explicit compartmentalisation, including scaffolding structures similar to that of the châssis of engineered machines. In addition, cells define clearly an (...) inside, the cytoplasm, and an outside. The cell envelope is more or less complicated in bacteria, and it is much more complicated in organisms made of cells with a nucleus (eukaryotes). Second, the machine also requires dynamic chemical processes, collectively named metabolism, that can be split into intermediary metabolism, managing chemical transformations and transport of small building blocks and management of energy (often with a rotating nanomachine, ATP synthase), and the macromolecule synthesis, salvage and turnover machinery which uses a variety of nanomachines, the ribosome being the most prominent one. The second entity which needs to be associated to life is the genetic program, in the form of the genome, composed of one or several chromosomes made of DNA. This is the entity which associates most clearly to information. -/- Natural selection uses information in a particular way: it is a process that uses energy to prevent degradation of any functional entity. This process keeps building up novel information within organisms. (shrink)

“There’s Plenty of Room at the Bottom”, said the title of Richard Feynman’s 1959 seminal conference at the California Institute of Technology. Fifty years on, nanotechnologies have led computer scientists to pay close attention to the links between physical reality and information processing. Not all the physical requirements of optimal computation are captured by traditional models—one still largely missing is reversibility. The dynamic laws of physics are reversible at microphysical level, distinct initial states of a system leading to (...) distinct final states. On the other hand, as von Neumann already conjectured, irreversible information processing is expensive: to erase a single bit of information costs ~3 × 10−21 joules at room temperature. Information entropy is a thermodynamic cost, to be paid in non-computational energy dissipation. This paper addresses the problem drawing on Edward Fredkin’s Finite Nature hypothesis: the ultimate nature of the universe is discrete and finite, satisfying the axioms of classical, atomistic mereology. The chosen model is a cellular automaton with reversible dynamics, capable of retaining memory of the information present at the beginning of the universe. Such a CA can implement the Boolean logical operations and the other building bricks of computation: it can develop and host all-purpose computers. The model is a candidate for the realization of computational systems, capable of exploiting the resources of the physical world in an efficient way, for they can host logical circuits with negligible internal energy dissipation. (shrink)

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological (...) computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted. (shrink)

Since March of 2020, the world has been brought to its knees by unscientific and unethical mandates. These mandates have destroyed the world economy and the lives of countless innocent individuals. The “cure” that has been offered by medical bureaucrats and politicians has been more deadly than the disease (COVID-19). The imposition of ludicrous lockdowns, mask-wearing, coerced vaccination, and vaccine passports have not only proved to be ineffective, but also much more harmful than SARS-CoV-2 and all its variants. COVID-19 has (...) a recovery rate of close to 99% for most of the world’s population, however, despite this, institutions and power-hungry individuals have trampled upon our civil liberties and ignored our inalienable human rights. It is precisely as Thomas Paine famously stated: “The greatest tyrannies are always perpetrated in the name of the noblest causes.” -/- Many lives have been gratuitously lost because of the administration of deadly medical treatments, and the rejection of effective treatments that follow genuine science. In the process, informed consent has been disparaged and desecrated. For over two years, we were instructed to “trust the science,” but the “science” advocated by medical bureaucrats and greedy politicians was the reason why the world was turned upside-down. In the face of these crimes against humanity, justice will only be brought by real courts and real judges, but this will require the awakening of a critical mass. This book serves as an instrument for this awakening. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

Categorization behavior can be fruitfully analyzed in terms of the trade‐off between as high as possible faithfulness in the transmission of information about samples of the classes to be categorized, and as low as possible transmission costs for that same information. The kinds of categorization behaviors we associate with conceptual atoms, prototypes, and exemplars emerge naturally as a result of this trade‐off, in the presence of certain natural constraints on the probabilistic distribution of samples, and the ways in (...) which we measure faithfulness. Beyond the general structure of categorization in these circumstances, the same information‐centered perspective can shed light on other, more concrete properties of human categorization performance, such as the results of certain prominent experiments on supervised categorization. (shrink)

Psychopathy is increasingly in the public eye. However, it is yet to be fully and effectively understood. Within the context of the DSM-IV, for example, it is best regarded as a complex family of disorders. The upside is that this family can be tightly related along common dimensions. Characteristic marks of psychopaths include a lack of guilt and remorse for paradigm case immoral actions, leading to the common conception of psychopathy rooted in affective dysfunctions. An adequate portrait of psychopathy is (...) much more complicated, however. Though some neural regions and corresponding functions are commonly indicated, they range across those responsible for action planning and learning, as well as emotional processes. Accordingly, a complete fine-grained map of all neural mechanisms responsible for psychopathy has not been realized, and even if it were, such a map would have limited utility outside of the context of surgical or chemical intervention. The utility of a neural-level understanding of psychopathy is further limited by the fact that it is only applicable in the clinical identification of individual subjects, and the neuro-chemical/biological correction of those subjects after they are positively identified as psychopaths. On the other hand, an information processing model of moral cognition provides for wider-ranging applications. The theoretical and practical implications for such a feasible working model of psychopathic personalities are assessed. Finally, this chapter raises the possibility of directed modification of social-environmental factors discouraging the development of psychopathic personalities in the first place, modifications which are also open to simulation and testing in terms of the same model of moral cognition. (shrink)

This paper exploits the fact that the variability in the inter-spike intervals, in the spike train issuing from a neuron, carries substantial information regarding the input to the neuron. A framework for neuronal information processing is proposed which utilizes the above fact to distinguish phenomenal from non-phenomenal mental representation. In the process, an explanation is offered as to what is it, in the nature of conscious mental states, that imparts them a subjective feeling – there is something (...) it is like to be in those mental states. To give empirical support, it is shown how the proposed framework can neatly explain, the delay in eliciting conscious awareness as observed by Libet and the related backwards referral in time. (shrink)

Over centuries, philosophers have theorised about what constitutes ‘the good’ regarding behavioural choice. Characteristically, these attempts have tried to decipher the nature and substantive values that link the apparent trichotomous nature of the human psyche, variously articulated in terms of human reasoning, feeling, and desiring. Of the three, most emphasis has focused on the unique human characteristic of reasoned behavioural choice in terms of its relationship to the emotions. This article determines the principle dynamics behind 'ethical' behaviour: In the nervous (...) system, efferent nerves, otherwise known as motor neurones, carry nerve impulses away from the central nervous system to effectors such as muscles. A great deal of neural activity underpins‘efferent information processing’. What follows is a categorisation of the structure of 'efferent information processing' in a manner, that enhances our understanding of the attempts of philosophers, from Plato to Russell, to explain ‘what it is to be behave well’. (shrink)

The key assumption behind evolutionary epistemology is that animals are active learners or ‘knowers’. In the present study, I updated the concept of natural learning, developed by Henry Plotkin and John Odling-Smee, by expanding it from the animal-only territory to the biosphere-as-a-whole territory. In the new interpretation of natural learning the concept of biological information, guided by Peter Corning’s concept of “control information”, becomes the ‘glue’ holding the organism–environment interactions together. The control information guides biological systems, from (...) bacteria to ecosystems, in the process of natural learning executed by the universal algorithm. This algorithm, summarized by the acronym IGPT (information-gain-process-translate) incorporates natural cognitive methods including sensing/perception, memory, communication, and decision-making. Finally, the biosphere becomes the distributed network of communicative interactions between biological systems termed the interactome. The concept of interactome is based on Gregory Bateson’s natural epistemology known as the “ecology of mind”. Mimicking Bateson’s approach, the interactome may also be designated “physiology of mind”—the principle behind regulating the biosphere homeostasis. (shrink)

In this article, I discuss what motivated reasoning research tells us about the prospects for deliberative democracy. In section I, I introduce the results of several political psychology studies examining the problematic affective and cognitive processing of political information by individuals in nondeliberative, experimental environments. This is useful because these studies are often neglected in political philosophy literature. Section II has three stages. First, I sketch how the study results from section I question the practical viability of deliberative (...) democracy. Second, I briefly present the results of three empirical studies of political deliberation that can be interpreted to counter the findings of the studies in section I. Third, I show why this is a misinterpretation and that the study results from section I mean that it is implausible that sites of political deliberation would naturally emerge from the wide public sphere and coalesce into institutionalized forms of the practice such that deliberative democracy can satisfy its raison d’être. Finally, in section III, I conclude that viable conceptions of deliberative democracy should be limited to narrower aims. (shrink)

Learning from contemporary natural, formal, and social sciences, especially from current biology, as well as from humanities, particularly contemporary philosophy of nature, requires updates of our old definitions of cognition and intelligence. The result of current insights into basal cognition of single cells and evolution of multicellular cognitive systems within the framework of extended evolutionary synthesis (EES) helps us better to understand mechanisms of cognition and intelligence as they appear in nature. New understanding of information and processes of physical (...) (morphological) computation contribute to novel possibilities that can be used to inspire the development of abiotic cognitive systems (cognitive robotics), cognitive computing and artificial intelligence. (shrink)

هل فوجئت يومًا بأن نظام التشغيل لهاتفك أو حاسوبك لا يقبل التحديث لأن الشركة المُنتجة قد أوقفت دعم إصدارٍ ما زال يعمل، لكي تحث عملائها على شراء إصدار جديد أكثر كفاءة وأشد جذبًا وإشباعًا لحاجات المستخدمين؟ وهل تساءلت يومًا عن طبيعة العلاقة التي تربط بينك وبين هاتفك أو حاسوبك أو أي جهازٍ إلكتروني آخر تمتلكه، هل هي علاقة عاطفية قوامها المظهر والتباهي أم علاقة عقلانية قوامها الحاجة ونمط الاستخدام؟ وهل فكرت يومًا في كيفية عمل هاتفك أو حاسوبك، وما الذي يحدث (...) حين تقوم بالضغط على أيقونة أحد البرامج، أو ما هي التفاصيل الفنية الدقيقة التي تتوارى خلف تلك الواجهات الرسومية التي تتعامل معها؟ هذا ما يُناقشه كاتب الخيال الأمريكي «نيل ستيفنسون» (من مواليد سنة 1959) في مقالٍ له تحت عنوان «في البدء كان سطر الأوامر»، نًشر أولاً على أحد المواقع الإلكترونية سنة 1999، ثم أتيح ككتاب في السنة ذاتها، وفيه يُقدم عرضًا تاريخيًا لمجموعة من أنظمة التشغيل، لاسيما تلك التي أنتجتها الشركتان المهيمنتان: «مايكروسوفت» و«آبل»، بالإضافة إلى البدائل المعروفة مثل «لينكس» و«بي أو إس». كما يسعى في المقال ذاته إلى تفسير سبب عدم احتمال بقاء أنظمة التشغيل الاحتكارية مُربحة في المستقبل في معية المنافسة الشرسة، ويُحلل الثقافة الجمعية للشركات الكبرى العاملة في مجال البرمجيات الحرة. (shrink)

Imagine a 5-stone weakling whose brain has been loaded with all the knowledge of a champion tennis player. He goes to serve in his first match – Wham! – His arm falls off. The 5-stone weakling just doesn’t have the bone structure or muscular development to serve that hard. There are, clearly, different types of knowledge/ability/skill, only some of which are a matter of what can be transferred simply by passing signals down a wire from one brain (or computer) to (...) another. Sometimes it is the body (the hardware) which knows. (shrink)

Conventional methods of genetic engineering and more recent genome editing techniques focus on identifying genetic target sequences for manipulation. This is a result of historical concept of the gene which was also the main assumption of the ENCODE project designed to identify all functional elements in the human genome sequence. However, the theoretical core concept changed dramatically. The old concept of genetic sequences which can be assembled and manipulated like molecular bricks has problems in explaining the natural genome-editing competences of (...) viruses and RNA consortia that are able to insert or delete, combine and recombine genetic sequences more precisely than random-like into cellular host organisms according to adaptational needs or even generate sequences de novo. Increasing knowledge about natural genome editing questions the traditional narrative of mutations (error replications) as essential for generating genetic diversity and genetic content arrangements in biological systems. This may have far-reaching consequences for our understanding of artificial genome editing. (shrink)

This paper links the conjecture that the physical world is a virtual reality to the findings of modern physics. What is usually the subject of science fiction is here proposed as a scientific theory open to empirical evaluation. We know from physics how the world behaves, and from computing how information behaves, so whether the physical world arises from ongoing information processing is a question science can evaluate. A prima facie case for the virtual reality conjecture is (...) presented. If a photon is a pixel on a multi-dimensional grid that gives rise to space, the speed of light could reflect its refresh rate. If mass, charge and energy all arise from processing, the many conservation laws of physics could reduce to a single law of dynamic information conservation. If the universe is a virtual reality, then its big bang creation could be simply when the system was booted up. Deriving core physics from information processing could reconcile relativity and quantum theory, with the former how processing creates the space-time operating system and the latter how it creates energy and matter applications. (shrink)

After employing the mindsponge mechanism and 3D information process of creativity to explain the serendipity process in previous chapters, we realize that it may be helpful to delve into the relations between serendipity and the formulation of new values and information connections through non-linear processes. Thus, this chapter summarizes some preliminary attempts to use non-linear information processes to explain serendipity. We also briefly mention the benefits of information exchange among members of social groups and explain this (...) approach. (shrink)

Emotional Intelligence (EI) has been conceptualized in the literature either as a dispositional tendency, in line with a personality trait (trait EI; Petrides and Furnham, 2001), or as an ability, moderately correlated with general intelligence (ability EI; Mayer and Salovey, 1997). Surprisingly, there have been few empirical attempts conceptualizing how the different EI approaches should be related to each other. However, understanding how the different approaches of EI may be interwoven and/or complementary is of primary importance for clarifying the conceptualization (...) of EI and organizing the literature around it. We introduce a theoretical framework explaining how trait EI, ability EI, and emotion information processing – a novel component related to EI recently introduced in the literature (e.g., Fiori and Vesely Maillefer, 2018) – may contribute to effective emotion-related performance and provide initial evidence supporting its usefulness in predicting EI-related outcomes. More specifically, we show that performance in a task in which participants had to infer the mental and emotional states of others, namely a Theory of Mind task, was predicted jointly (e.g., interaction effects) by trait EI, ability EI, and emotion information processing, after controlling for personality and IQ (N = 323). Our results argue for the importance of investigating the joint contribution of different aspects of EI in explaining variability in emotionally laden outcomes. (shrink)

When a prominent attribute looms larger in one response procedure than in another, a violation of procedure invariance occurs. A hypothesis based on compatibility between the structure of the input information and the required output was tested as an explanation of this phenomenon. It was also compared with other existing hypotheses in the field. The study had two aims: (1) to illustrate the prominence effect in a selection of preference tasks (choice, acceptance decisions, and preference ratings); (2) to demonstrate (...) the processing differences in a matching procedure versus the selected preference tasks. Hence, verbal protocols were collected in both a matching task and in subsequent preference tasks. Silent control conditions were also employed. The structure compatibility hypothesis was confirmed in that a prominence effect obtained in the preference tasks was accompanied by a lower degree of attention to the attribute levels in these tasks. Furthermore, as predicted from the structure compatibility hypothesis, it was found that fewer comparisons between attribute levels were performed in the preference tasks than in the matching task. It was therefore concluded that both these processing differences may explain the occurrence of the prominence effects. (shrink)

Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. (...) Impor- tantly, modern proponents of Intelligent Design, the latest version of creationism, have exploited biologists’ use of the language of information and blueprints to make their spurious case, based on pseudoscientific concepts such as ‘‘irreducible complexity’’ and on flawed analogies between living cells and mechanical factories. However, the living organ- ism = machine analogy was criticized already by David Hume in his Dialogues Concerning Natural Religion. In line with Hume’s criticism, over the past several years a more nuanced and accurate understanding of what genes are and how they operate has emerged, ironically in part from the work of computational scientists who take biology, and in particular developmental biology, more seriously than some biologists seem to do. In this article we connect Hume’s original criticism of the living organism = machine analogy with the modern ID movement, and illustrate how the use of misleading and outdated metaphors in science can play into the hands of pseudoscientists. Thus, we argue that dropping the blueprint and similar metaphors will improve both the science of biology and its understanding by the general public. (shrink)

In this article the linguistic processes of consciousness are discussed at the informational and semantic levels. The key question is devoted to the distinction between the information, meaning and sense in the physical, logico-semantic and historic levels of brain and consciousness. The principal point runs that the human linguistic process of sense producing takes the variety and indistinctness in the cultural presupposition. The modern theories of philosophy of mind relying on the theories of Soviet psychological school propose some new (...) solutions in the pragmatic questions of the semantic noncomputability. In this review we will try to justify the dualistic correlation between the cultural base and the communicative semantic process. (shrink)

An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G theory (...) consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label’s learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maxmization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic. (shrink)

This is our journal for developing Deduction Theory and studying Deep Learning and Artificial intelligence. Deduction Theory is a Theory of Deducing World’s Relativity by Information Coupling and Asymmetry. We focus on information processing, see intelligence as an information structure that relatively close object-oriented, probability-oriented, unsupervised learning, relativity information processing and massive automated information processing. We see deep learning and machine learning as an attempt to make all types of information (...) class='Hi'>processing relatively close to probability information processing. We will discuss about how to understand Deep Learning and Artificial intelligence and why Deep Learning is shown better performance than the other methods by metaphysical logic. (shrink)

This book constitutes refereed proceedings of the COST 2102 International Training School on Cognitive Behavioural Systems held in Dresden, Germany, in February 2011. The 39 revised full papers presented were carefully reviewed and selected from various submissions. The volume presents new and original research results in the field of human-machine interaction inspired by cognitive behavioural human-human interaction features. The themes covered are on cognitive and computational social information processing, emotional and social believable Human-Computer Interaction (HCI) systems, behavioural and (...) contextual analysis of interaction, embodiment, perception, linguistics, semantics and sentiment analysis in dialogues and interactions, algorithmic and computational issues for the automatic recognition and synthesis of emotional states. (shrink)

Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, (...) Searle's Chinese room (1980) and arguments from Gödel's Incompleteness theorems (e.g., Lucas, 1961; Penrose, 1989), both of which fall short of addressing the complete TM model. Both UTM-computers and formal systems provide an unsound basis for debate. In particular, their special natures easily foster the misconception that computation entails intrinsically meaningless symbol manipulation. This common view is incorrect with respect to full-fledged TM's, which can process inputs non-formally, i.e., in a subjective and dynamically evolving fashion. To avoid a distorted understanding of the theory of computation, philosophical judgments and discussions should be grounded firmly upon the complete Turing machine model, the proper model for real computers. (shrink)

The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation (...) assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

The comprehension of anaphoric relations may be guided not only by discourse, but also syntactic information. In the literature on online processing, however, the focus has been on audible pronouns and descriptions whose reference is resolved mainly on the former. This paper examines one relation that both lacks overt exponence, and relies almost exclusively on syntax for its resolution: adjunct control, or the dependency between the null subject of a non-finite adjunct and its antecedent in sentences such as (...) Mickey talked to Minnie before ___ eating. Using visual world eyetracking, we compare the time course of interpreting this null subject and overt pronouns (Mickey talked to Minnie before he ate). We show that when control structures are highly frequent, listeners are just as quick to resolve reference in either case. When control structures are less frequent, reference resolution based on structural information still occurs upon hearing the non-finite verb, but more slowly, especially when unaided by structural and referential predictions. This may be due to increased difficulty in recognizing that a referential dependency is necessary. These results indicate that in at least some contexts, referential expressions whose resolution depends on very different sources of information can be resolved approximately equally rapidly, and that the speed of interpretation is largely independent of whether or not the dependency is cued by an overt referring expression. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe (...) consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

We try to show that there is no difference in principle between communicating a piece of information to a human and to a machine. The argumentation depends on the following theses: Communicating is transfer of information; information has propositional form; propositional form can be modelled as categorization; categorisation can be modelled in a machine; a suitably equipped machine can grasp propositional content designed for human communication. What I suggest is that the discussion should focus on the truth (...) and precise meaning of these statements. However, in case these statements are true it follows that: For any act of communication that successfully transfers a piece of information to a human, that act could also transfer that piece of information to a machine. (shrink)