Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from (...) data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (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 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)

E-learning has taken it full fudged emergence with regards to Covid Scenario. Also, the lockdown of schools and playgrounds, the restriction of outdoor activities, physical and social isolation leads to the behavioural change among school children. Students are more attached to their schools, teachers and friends. But Covid 19 has changed the entire situation changed and they were held in their home itself. Students were not able to meet their friends and teachers, they especially miss their school and class (...) environment. It is to be noted that maintaining social isolation and following “Stay at Home” plays a very high impact among school students. Also, the lockdown created for families and children sit inside their homes. Which ,We cannot leave children’s education as it is. In order to continue their education, Indian Government has taken many efforts .Even schools are doing their best to bridge the gap between teachers and students by providing online education because of the absence of regular classroom education. Because of the introduction of online class by schools, on the one side students are not missing the education, on the other side students and parents faces a lot of problems (Physical and mental. It also puts them at the risk of unsupervised access to websites and other unwanted sites in internet. (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)

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)

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue (...) contains a commentary on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’. (shrink)

This paper describes an attempt to repurpose Kant’s a priori psychology as the architectural blueprint for a machine learning system. First, it describes the conditions that must be satisfied for the agent to achieve unity of experience: the intuitions must be connected, via binary relations, so as to satisfy various unity conditions. Second, it shows how the categories are derived within this model: the categories are pure unary predicates that are derived from the pure binary relations. Third, I describe (...) how Kant’s cognitive architecture has been implemented in a computer system (the Apperception Engine) and show in detail what it is like for the system to construct a unified experience from a sequence of raw sensory input. (shrink)

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) (...) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. (shrink)

This is an in-depth review of "The Embodied Mind: Cognitive Science and Human Experience" by Francisco Varela, Evan Thompson and Eleanor Rosch.

Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning (...) algorithm for synthesising missing anatomy in extremely sparse clinical MRI scans of thousands of stroke patients. By approaching the study from a media-theoretical perspective, I analyse how its authors dis-cursively negotiated the anatomical and operative plausibility of the unobserved anatomy that their black-boxed algorithm reconstructed from the existing sparse data. My analysis fore-grounds the processual, relational, context-dependent, and essentially unstable character of the thus established plausibility of the algorithmically synthesised neuroanatomy. (shrink)

Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has (...) emerged a new field called ‘explainable AI’ (XAI). When we examine the XAI literature, however, it becomes apparent that its protagonists have redefined the term ‘explanation’ to mean something else, namely: ‘interpretation’. Interpretations are indeed sometimes possible, but we show that they give at best only a subjective understanding of how a model works. We propose an alternative to XAI, namely certified AI (CAI), and describe how an AI can be specified, realized, and tested in order to become certified. The resulting approach combines ontologies and formal logic with statistical learning to obtain reliable AI systems which can be safely used in technical applications. (shrink)

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is (...) brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the (...) QE is akin to functional characteristics of a specific class of retinal ganglion cells (the so-called Y-cells) in the visual systems of the primate and the cat, the sensitivity of the QE surpasses the capacity limits of human visual detection. Here, the quantization error in the SOM is found to reliably signal changes in contrast or colour when contrast information is removed from or added to the image, but not when the amount and relative weight of contrast information is constant and only the local spatial position of contrast elements in the pattern changes. While the RGB Mean reflects coarser changes in colour or contrast well enough, the SOM-QE is shown to outperform the RGB Mean in the detection of single-pixel changes in images with up to five million pixels. This could have important implications in the context of unsupervised image learning and computational building block approaches to large sets of image data (big data). (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

What can we learn from cases of knowledge from falsehood? Critics of knowledge-first epistemology have argued that these cases provide us with good reason for rejecting the knowledge accounts of evidence, justification, and the norm of belief. I shall offer a limited defense of the knowledge-first approach to these matters. Knowledge from falsehood cases should undermine our confidence in like-from-like reasoning in epistemology. Just as we should be open to the idea that knowledge can come from non-knowledge, we should be (...) open to the idea that justified beliefs can come from unjustified beliefs. (shrink)

Perception provides us with access to the external world, but that access is shaped by our own experiential histories. Through perceptual learning, we can enhance our capacities for perceptual discrimination, categorization, and attention to salient properties. We can also encode harmful biases and stereotypes. This article reviews interdisciplinary research on perceptual learning, with an emphasis on the implications for our rational and normative theorizing. Perceptual learning raises the possibility that our inquiries into topics such as epistemic justification, (...) aesthetic criticism, and moral knowledge should include not only an examination of cognition but also of perception. (shrink)

I propose that metaphysical study is initially indifferent to the truth of Metaphysical Realism about Metaphysics (MRM) and Metaphysical Realism and does not presuppose them. Metaphysical Realism is a metaphysical doctrine the truth of which cannot be settled logically prior to metaphysical investigation. MRM presupposes Metaphysical Realism and therefore, one should not hold MRM uncritically. An epistemological consequence of this is that arguments against the possibility of cognition about metaphysically real entities (by e.g., Hume) are not arguments against the epistemic (...) legitimacy of metaphysics without further arguments for MRM. This paper is also a case study of the philosophical significance of having rich enough acquaintance with the history of philosophy, the recent history included. (shrink)

This study explores a liberatory epistemic virtue that is suitable for good learning as a form of liberating socially situated epistemic agents toward ideal virtuousness. First, I demonstrate that the weak neutralization of epistemically bad stereotypes is an end of good learning. Second, I argue that weak neutralization represents a liberatory epistemic virtue, the value of which derives from liberating us as socially situated learners from epistemic blindness to epistemic freedom. Third, I explicate two distinct forms of epistemic (...) transformation: constitutive and causal epistemic transformation. I argue that compared with the ideal conception of epistemic virtue, constitutive epistemic transformation that involves good learning has a transcendent value in light of agents constantly renewing their default epistemic status toward ideal virtuousness. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

What might Adam Smith have learned from Jane Austen and other novelists of his moment? This paper finds and examines a serious problem at the center of Adam Smith’s moral psychology, stemming from an unacknowledged tension between the effort of the spectator to sympathize with the feelings of the agent and that of the agent to moderate her feelings. The agent’s efforts will result in her opacity to spectators, blocking their attempts to read her emotions. I argue that we can (...) resolve this tension by looking to developments in eighteenth-century literature, as perfected in the hands of Jane Austen. I focus on two techniques, focalization and free indirect style, and I show that the problems for spectatorship diminish when we see that good Smithian spectators are savvy readers. (shrink)

Perceptual experiences are not immediately responsive to reasons. You see a stick submerged in a glass of water as bent no matter how much you know about light refraction. Due to this isolation from reasons, perception is traditionally considered outside the scope of epistemic evaluability as justified or unjustified. Is perception really as independent from reasons as visual illusions make it out to be? I argue no, drawing on psychological evidence from perceptual learning. The flexibility of perceptual learning (...) is a way of responding to new epistemic reasons. The resulting perceptual experiences are epistemically evaluable as justified or unjustified. (shrink)

Suppose you have recently gained a disposition for recognizing a high-level kind property, like the property of being a wren. Wrens might look different to you now. According to the Phenomenal Contrast Argument, such cases of perceptual learning show that the contents of perception can include high-level kind properties such as the property of being a wren. I detail an alternative explanation for the different look of the wren: a shift in one’s attentional pattern onto other low-level properties. Philosophers (...) have alluded to this alternative before, but I provide a comprehensive account of the view, show how my account significantly differs from past claims, and offer a novel argument for the view. Finally, I show that my account puts us in a position to provide a new objection to the Phenomenal Contrast Argument. (shrink)

In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are (...) in fact many promising general models for explaining how concepts are learned. (shrink)

Confirmational holism is at odds with Jeffrey conditioning --- the orthodox Bayesian policy for accommodating uncertain learning experiences. Two of the great insights of holist epistemology are that (i) the effects of experience ought to be mediated by one's background beliefs, and (ii) the support provided by one's learning experience can and often is undercut by subsequent learning. Jeffrey conditioning fails to vindicate either of these insights. My aim is to describe and defend a new updating policy (...) that does better. In addition to showing that this new policy is more holism-friendly than Jeffrey conditioning, I will also show that it has an accuracy-centered justification. (shrink)

Learner’s attitude towards modular distance learning catches uncertainties as a world crisis occurs up to this point. As self-learning modules (SLMs) become a supplemental means of learning in new normal education, this study investigated efficiency towards the learners’ attitude and performance. Specifically, the study described the learners’ profile and their attitude and performance towards SLMs. It also ascertained the relationship between the learner’s profile with their attitude and performance, as well as the relationship between attitude and performance (...) relevant to SLMs. A descriptive-correlational research design was employed in the study with 88 non-randomly chosen participants from secondary schools in West Philippines. The learners’ GWA (general weighted average) was used to determine their performance and a survey questionnaire for their profile and attitude. Frequency counts, percentage, and Spearman’s rank correlation coefficient were used to analyze the data gathered with the aid of Jamovi software. Results revealed a strong correlation between attitude and performance, but there was no link between learner’s attitude and performance as to the learner’s profile. It was affirmed that attitude is a compelling factor that is related with performance. Hence, this research has broader ramifications that can direct the Bureau of Learning Delivery to orient the module writers to further contextualize the learning materials that can compound learner’s disposition and academic performance. (shrink)

This study aims to describe the learning motivation of students using virtual media when they are learning mathematics in grade 5. The research design applied in this research is classroom action research. The research is conducted in two phases which involve planning, action and observation and reflection. The results of the study revealed that intrinsic motivation to learn is most prevalent in the form of fun to learn mathematics with virtual media. Other forms of intrinsic motivation include curiosity, (...) need and interests, and satisfaction. Extrinsic motivation in the form of awarded and clarity of learning objectives. The most enjoyable learning experience for students was when they feel like they're learning more than they actually are. This level of enthusiasm increases with the level of learning complexity. The students are more active and motivated to learn. This can be evidenced by the increase in motivation to learn after the first phase. Unfortunately, the lack of bandwidth and time allocated for conducting experiments are some of the obstacles in the way of learning through virtual media. This study revealed that the use of virtual media could improve student's motivation for learning. (shrink)

The cognitive penetrability of perceptual experiences has been a long-standing topic of disagreement among philosophers and psychologists. Although the notion of cognitive penetrability itself has also been under dispute, the debate has mainly focused on the cases in which cognitive states allegedly penetrate perceptual experiences. This paper concerns the plausibility of two prominent cases. The first one originates from Susanna Siegel’s claim that perceptual experiences can represent natural kind properties. If this is true, then the concepts we possess change the (...) way things appear to us. The second candidate for cognitive penetration is Fiona Macpherson’s claim that, in addition to concepts, our beliefs can penetrate perceptual experiences. It is argued that neither candidate is a case of cognitive penetration. In doing so, I provide an explanation to both that is based on perceptual learning, a non-cognitive phenomenon where relatively slow and long-lasting modifications to an organism’s perceptual system bring about changes in perception. This explanation is theoretically more plausible and remains closer to the empirical data than the explanations based on cognitive penetration. (shrink)

This article reconsiders the early hominid ‘‘lithic niche’’ by examining the social implications of stone artifact making. I reject the idea that making tools for use is an adequate explanation of the elaborate artifact forms of the Lower Palaeolithic, or a sufficient cause for long-term trends in hominid technology. I then advance an alternative mechanism founded on the claim that competency in making stone artifacts requires extended learning, and that excellence in artifact making is attained only by highly skilled (...) individuals who have been taught and practiced for extensive periods. Consequently both competency and expertise in knapping comes at a high learning cost for both the individual learner and the social group to which they belong. Those high intrinsic costs of learning created contexts in which groups selected cost-reducing forms of social learning and teaching, and in which specialization could develop. Artifacts and their manufacturing processes probably acquired functions as social signals—as honest signals of valuable capacities. The magnification of these signals, through competition between knappers and through inspiring later craftspeople, may account for a substantial amount of the accumulated elaboration visible in the archaeological record. Consequently lithic artifacts operated as material symbols from an early time in hominid evolution. (shrink)

Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept learning, (...) we show that it is easier to infer the meaning of complex concepts than that of simple concepts. (shrink)

Our perceptual systems are not stagnant but can learn from experience. Why is this so? That is, what is the function of perceptual learning? I consider two answers to this question: The Offloading View, which says that the function of perceptual learning is to offload tasks from cognition onto perception, thereby freeing up cognitive resources (Connolly, 2019) and the Perceptual View, which says that the function of perceptual learning is to improve the functioning of perception. I argue (...) that the Perceptual View better explains data from infants and animals, and better accounts for learned tasks that only perception could perform. (shrink)

This is an excerpt of a report that highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York in March, 2012. This portion of the report explores the question: What is perceptual learning?

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip on (...) how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organise (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the Internet during exams. (shrink)

Learning to love the reviewer -/- Issue: 43(4) November 2017. Viewpoint Page 83 -/- Quan Hoang Vuong Western University Hanoi, Centre for Interdisciplinary Social Research, Hanoi, Vietnam.

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

The research aims to identify the learning organizations and their role in achieving organizational excellence in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) workers from the Palestinian universities was selected and the recovery (...) rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The research found the following results: there is a fair degree of approval on "cognitive dimension", there is a high approval about the importance of "organizational dimension", there is moderately consent of the importance of "Community dimension", there is a large degree of consent about the importance of axis of the "leadership excellence", there is a large degree of consent about the importance of axis of the "service-excellence", there is a fair degree approval about the importance of the axis of "cognitive excellence", and there is a moderately consent of the importance of "Organizational Excellence". The research found a group of recommendations including: the need to develop appropriate strategies for the University, employ modern technology in information systems, in addition to provide appropriate environment that achieve learning organizations, develop technological infrastructure, adopt universities for knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments, create technological incubators in universities to adopt excellence academic research projects, and protected them, support them, and market them. Establishing of centers of excellence for scientific research in the university.The need for cooperation and coordination between local, Arab and international universities for the exchange of knowledge, information, and participation programs and training courses, as it provides an opportunity to develop the capabilities and skills of personnel and expertise. (shrink)

Online education has become an essential component of education. Thus, several factors, such as the student’s learning strategy and motivation, generally contribute to their academic success. This study investigates the relationship between learning strategies, motivation, and online learning environment among 150 first-year college students. Employing correlational design, the statistical findings of the study reveal that the r coefficient of 0.59 indicates a moderate positive correlation between the variables. The p-value of 0.00, which is less than 0.05, leads (...) to the decision to reject the null hypothesis. Hence, a significant relationship exists between learning strategies, motivation, and the online learning environment. (shrink)

In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical (...) promise’, which suggests how work done in the realm of critical phenomenology is complementary to and useful for exploring the social justice and emancipatory aims of networked learning researchers. Through this framework, I reflect upon multiple ways in which phenomenology has and might continue to fruitfully inform and shape networked learning research. In doing so, I emphasise that deploying phenomenology in the context of networked learning not only enriches our understanding of networked learning but that phenomenology itself is enriched through testing, finetuning, and expanding its methods, concepts, and understanding, as well as unveiling its own limitations and constraints. (shrink)

Underlying much current work in philosophy of imagination is the assumption that imagination is a skill. This assumption seems to entail not only that facility with imagining will vary from one person to another, but also that people can improve their own imaginative capacities and learn to be better imaginers. This paper takes up this issue. After showing why this is properly understood as a philosophical question, I discuss what it means to say that one imagining is better than another (...) and then discuss the kinds of imagination training and techniques that might be employed in an effort to get better at imagining. The discussion of these techniques draws insight from consideration of other skills-based activities, as well as from consideration of the creation of art and our engagement with literature and poetry. Over the course of this discussion, we also gain further insight into the nature of imagination. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Literature frequently describes how ineffective implementation of instructional policy frameworks can make distance learning a lonely and unrewarding academic pursuit, characterized by high student drop-out rates, high failure rates and academic exclusion. In trying to mitigate this catastrophe, academic departments in distance learning institutions utilize learning management systems (LMSs) to stimulate students’ learning experiences. In keeping with techno-progressivism, the researchers (and authors of this paper) turned to extant documentary policy and literature to review – qualitatively – (...) how the University of South Africa’s (Unisa’s) Open Distance Learning Policy (ODLP) promotes academic inclusion and learning agency as cornerstones of student success and social justice, especially in an unequal society like South Africa. The findings revealed the following: 1) Unisa’s ODLP position has the impetus to influence the deployment of the LMS to promote academic inclusion; 2) Practical means of promoting inclusion were mirrored in the provision of manual or electronic learning material, computer laboratories and free internet connectivity in regional centres across all nine provinces of South Africa, as well as in the provision of laptops for National Student Financial Aid Scheme (NSFAS)-funded students, 30 gigabytes of free monthly mobile data to all students, and assistive technologies for students with disabilities; 3) Practical utilization of an LMS to foster self-regulated learning occurred through problem-based individual activities supplemented by asynchronous demonstrative learning material (e.g. audio, video), while collaborative learning agency was enabled through e-tutoring, which afforded students the opportunity to interact with e-tutors and their peers about the learning content; 4) Drawbacks in the use of the LMS stemmed from the insufficiency of assistive learning technologies that are required for students with disabilities to participate fully in online learning, and a corpus of students’ irregular attendance at and participation in e-tutoring discussions and their projection of a negative attitude towards the e-tutors. -/- . (shrink)

The study assessed the conceptual understanding and attitude toward fractions of teacher education students in a socio-constructivist learning environment. Specifically, it determined the students’ level of conceptual understanding before and after instruction; verified the types of conceptual changes that occurred; and ascertained the attitude of students toward fractions before and after instruction and its relationship to their levels of understanding. Descriptive-correlational research method was used. Socio-constructivist context-based teaching method was employed to introduce the concept of fractions. Achievement tests and (...) interviews were administered to determine the students’ level of conceptual understanding. Conceptual analysis based on Jensen and Finley’s (1995) method with Tiberghien’s (1994) classification of changes was utilized to describe students’ conceptual understanding and conceptual changes. In order to determine their attitude on fractions, students were asked to answer the socio-constructivist attitude questionnaire. The level of conceptual understanding of teacher education students in fraction was functional misconception and partial understanding before and after instruction, respectively. The type of conceptual change that occurred among teacher education students was change for the better. Socio-constructivist learning more likely to improve students’ attitudes toward fractions; promoted prosocial behavior among students; and tend to increase students’ activeness in the classroom activities as evidenced. (shrink)

This is a response to the nine commentaries on our target article “Unlimited Associative Learning: A primer and some predictions”. Our responses are organized by theme rather than by author. We present a minimal functional architecture for Unlimited Associative Learning that aims to tie to together the list of capacities presented in the target article. We explain why we discount higher-order thought theories of consciousness. We respond to the criticism that we have overplayed the importance of learning (...) and underplayed the importance of spatial modelling. We decline the invitation to add a negative marker to our proposed positive marker so as to rule out consciousness in plants, but we nonetheless maintain that there is no positive evidence of consciousness in plants. We close by discussing how UAL relates to development and to its material substrate. (shrink)

The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a (...) knowing community. And another part of this thinking is centered around the new, and the newly empowered, learner, the member of the net generation, who is thinking and interacting in new ways. These trends combine to form what is sometimes called 'e-learning 2.0' - an approach to learning that is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather, as embedded in meaningful activities such as games or workflows. (shrink)

In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian (...) norms is sufficient to identify a normatively privileged updating procedure for this kind of learning. Along the way, we justify a privileged formalization of the notion of ‘epistemic conservativity’, offer a new analysis of the Judy Benjamin problem, and emphasize the distinction between interpreting the content of new evidence and updating one’s beliefs on the basis of that content. (shrink)

On an account of virtue as skill, virtues are acquired in the ways that skills are acquired. In this paper I focus on one implication of that account that is deserving of greater attention, which is that becoming more skillful requires learning from one’s failures, but that turns out to be especially challenging when dealing with moral failures. In skill acquisition, skills are improved by deliberate practice, where you strive to correct past mistakes and learn how to overcome your (...) current limitations. A similar story applies to virtue acquisition, as moral failures will be a part of anyone’s life, and we will all have to learn from these experiences. However, despite the importance of being able to learn from our mistakes, this is very difficult in practice, given that failure of any kind can be distressing, and especially so for moral failure. The distress created by a recognition of moral failure often prompts responses of anger, avoidance, or defensiveness; rather than attempts to make amends and when necessary to work on self-improvement. The most potentially distressing response to moral failure is shame, as it is often associated with defensiveness. It is here where emotion regulation will be important to manage that distress, and I focus on the skill of emotion differentiation. I argue that emotion differentiation is a promising strategy for distinguishing the emotions we may experience in the wake of failure, including shame, and to encourage those emotions that motivate self-improvement. Thus, emotion regulation is important for virtue acquisition. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...) enhance model generalizability. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections.