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  1. Extending Environments To Measure Self-Reflection In Reinforcement Learning.Samuel Allen Alexander, Michael Castaneda, Kevin Compher & Oscar Martinez - manuscript
    We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus, an agent's self-reflection ability can be numerically estimated by running the agent through a battery (...)
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  2. A Statistical Learning Approach to a Problem of Induction.Kino Zhao - manuscript
    At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in (...)
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  3. Two Dimensions of Opacity and the Deep Learning Predicament.Florian J. Boge - forthcoming - Minds and Machines:1-33.
    Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...)
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  4. Autognorics Approach to the Problem of Defining Life and Artificial Intelligence.Joey Lawsin - forthcoming
    Many thinkers, past and present, have tried to solve the underlying mystery of Life. Yet, no one has ever categorically expressed its exact concrete essence, scope, or meaning until a new school of thought known as Originemology was conceptualized in 1988 by Joey Lawsin. Life and consciousness can not be explained properly because their theoretical and philosophical bases are wrong. When the bases are incorrect, the outcomes are incorrect. Like the words associated with life such as alive, aware, conscious, intelligent, (...)
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  5. Believing in Black Boxes: Must Machine Learning in Healthcare Be Explainable to Be Evidence-Based?Liam McCoy, Connor Brenna, Stacy Chen, Karina Vold & Sunit Das - forthcoming - Journal of Clinical Epidemiology.
    Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding explainability are (...)
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  6. Human Induction in Machine Learning: A Survey of the Nexus.Petr Spelda & Vit Stritecky - forthcoming - ACM Computing Surveys.
    As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...)
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  7. A Note on the Learning-Theoretic Characterizations of Randomness and Convergence.Tomasz Steifer - forthcoming - Review of Symbolic Logic:1-15.
    Recently, a connection has been established between two branches of computability theory, namely between algorithmic randomness and algorithmic learning theory. Learning-theoretical characterizations of several notions of randomness were discovered. We study such characterizations based on the asymptotic density of positive answers. In particular, this note provides a new learning-theoretic definition of weak 2-randomness, solving the problem posed by (Zaffora Blando, Rev. Symb. Log. 2019). The note also highlights the close connection between these characterizations and the problem of convergence on random (...)
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  8. Designing AI for Explainability and Verifiability: A Value Sensitive Design Approach to Avoid Artificial Stupidity in Autonomous Vehicles.Steven Umbrello & Roman Yampolskiy - forthcoming - International Journal of Social Robotics:1-15.
    One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper explores how decision matrix algorithms, via the belief-desire-intention model for (...)
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  9. Correlation Isn’T Good Enough: Causal Explanation and Big Data. [REVIEW]Frank Cabrera - 2021 - Metascience 30 (2):335-338.
    A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.
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  10. Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Joshua Dever - 2021 - New York, USA: Oxford University Press.
    Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. (...)
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  11. Fair Machine Learning Under Partial Compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? (...)
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  12. Exploring Machine Learning Techniques for Coronary Heart Disease Prediction.Hisham Khdair - 2021 - International Journal of Advanced Computer Science and Applications 12 (5):28-36.
    Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...)
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  13. Tecno-especies: la humanidad que se hace a sí misma y los desechables.Mateja Kovacic & María G. Navarro - 2021 - Bajo Palabra. Revista de Filosofía 27 (II Epoca):45-62.
    Popular culture continues fuelling public imagination with things, human and non-human, that we might beco-me or confront. Besides robots, other significant tropes in popular fiction that generated images include non-human humans and cyborgs, wired into his-torically varying sociocultural realities. Robots and artificial intelligence are re-defining the natural order and its hierar-chical structure. This is not surprising, as natural order is always in flux, shaped by new scientific discoveries, especially the reading of the genetic code, that reveal and redefine relationships between (...)
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  14. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  15. Healthcare and Anomaly Detection: Using Machine Learning to Predict Anomalies in Heart Rate Data.Edin Šabić, David Keeley, Bailey Henderson & Sara Nannemann - 2021 - AI and Society 36 (1):149-158.
    The application of machine learning algorithms to healthcare data can enhance patient care while also reducing healthcare worker cognitive load. These algorithms can be used to detect anomalous physiological readings, potentially leading to expedited emergency response or new knowledge about the development of a health condition. However, while there has been much research conducted in assessing the performance of anomaly detection algorithms on well-known public datasets, there is less conceptual comparison across unsupervised and supervised performance on physiological data. Moreover, while (...)
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  16. Predicting Me: The Route to Digital Immortality?Paul Smart - 2021 - In Robert W. Clowes, Klaus Gärtner & Inês Hipólito (eds.), The Mind-Technology Problem: Investigating Minds, Selves and 21st Century Artefacts. Cham, Switzerland: Springer. pp. 185–207.
    An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (...)
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  17. Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals Through Artificial Intelligence.Frank Ursin, Cristian Timmermann & Florian Steger - 2021 - Diagnostics 11 (3):440.
    Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward (...)
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  18. Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2021 - Philosophy and Technology 34 (2):265-288.
    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...)
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  19. Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2021 - Philosophy and Technology 34 (2):265-288.
    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...)
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  20. The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - 2020 - Journal of Artificial General Intelligence 11 (1):70-85.
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways (...)
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  21. On Uniform Definability of Types Over Finite Sets for NIP Formulas.Shlomo Eshel & Itay Kaplan - 2020 - Journal of Mathematical Logic 21 (3).
    Combining two results from machine learning theory we prove that a formula is NIP if and only if it satisfies uniform definability of types over finite sets. This settles a conjecture of La...
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  22. Humanistic Interpretation and Machine Learning.Juho Paakkonen & Petri Ylikoski - 2020 - Synthese 199 (1-2):1-37.
    This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the (...)
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  23. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  24. Machine learning, inductive reasoning, and reliability of generalisations.Petr Spelda - 2020 - AI and Society 35 (1):29-37.
    The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate (...)
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  25. The Future of Human-Artificial Intelligence Nexus and its Environmental Costs.Petr Spelda & Vit Stritecky - 2020 - Futures 117.
    The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...)
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  26. What Can Artificial Intelligence Do for Scientific Realism?Petr Spelda & Vit Stritecky - 2020 - Axiomathes 31 (1):85-104.
    The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for (...)
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  27. 类人猿或安卓会毁灭地球吗?*雷·库兹韦尔(2012年)关于如何创造心灵的评论 (Will Hominoids or Androids Destroy the Earth? —A Review of How to Create a Mind by Ray Kurzweil (2012)) (2019年修订版).Michael Richard Starks - 2020 - In 欢迎来到地球上的地狱: 婴儿,气候变化,比特币,卡特尔,中国,民主,多样性,养成基因,平等,黑客,人权,伊斯兰教,自由主义,繁荣,网络,混乱。饥饿,疾病,暴力,人工智能,战争. Las Vegas, NV USA: Reality Press. pp. 146-158.
    几年前,我通常可以从书名中分辨出什么,或者至少从章节标题中看出,会犯什么样的哲学错误,以及错误的频率。就名义上的科学著作而言,这些可能在很大程度上局限于某些章节,这些章节具有哲学意义或试图得出关于该作 品的意义或长期意义的一般性结论。然而,通常情况下,事实的科学问题慷慨地与哲学的胡言乱语,这些事实意味着什么。维特根斯坦在大约80年前描述的科学问题与各种语言游戏所描述的明确区别很少被考虑,因此人们交替 地被科学所震惊,并因它的不连贯而感到沮丧。分析。因此,这是与这个卷。 如果一个人要创造一个或多或少像我们一样的头脑,一个人需要有一个理性的逻辑结构,并理解两种思想体系(双过程理论)。如果一个人要对此进行哲学思考,就需要理解科学事实问题与语言如何在问题语境中工作,以及如何 避免还原主义和科学主义的陷阱的哲学问题之间的区别,但Kurzweil,如最学生的行为,基本上都是无知的。他被模型、理论和概念所陶醉,以及解释的冲动,而维特根斯坦向我们表明,我们只需要描述,理论、概念等 只是使用语言(语言游戏)的方式,只有它们有明确的价值测试(清晰的真理制造者,或约翰西尔(AI最著名的批评家)喜欢说,明确的满意条件(COS))。我试图在我最近的著作中对此作一个开端。 那些希望从现代两个系统的观点来看为人类行为建立一个全面的最新框架的人,可以查阅我的书《路德维希的哲学、心理学、Mind 和语言的逻辑结构》维特根斯坦和约翰·西尔的《第二部》(2019年)。那些对我更多的作品感兴趣的人可能会看到《会说话的猴子——一个末日星球上的哲学、心理学、科学、宗教和政治——文章和评论2006-201 9年第3次(2019年)和自杀乌托邦幻想21篇世纪4日 (2019) .
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  28. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  29. On the Possibility of Emotional Robots.Godwin Darmanin - 2019 - Revista de Filosofia Aurora 31 (54).
    In this article, I examine whether the possibility exists that in the foreseeable future, robot technology will permit the development of emotional robots. As the title suggests, the content is of a technological as well as of a philosophical nature. As a matter of fact, my aim in writing this paper was that of bridging two distinctive fields in a world where humanity has become accustomed to technological innovations while overlooking any consequential complications arising from such inventions. To this end, (...)
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  30. Psychopower and Ordinary Madness: Reticulated Dividuals in Cognitive Capitalism.Ekin Erkan - 2019 - Cosmos and History 15 (1):214-241.
    Despite the seemingly neutral vantage of using nature for widely-distributed computational purposes, neither post-biological nor post-humanist teleology simply concludes with the real "end of nature" as entailed in the loss of the specific ontological status embedded in the identifier "natural." As evinced by the ecological crises of the Anthropocene—of which the 2019 Brazil Amazon rainforest fires are only the most recent—our epoch has transfixed the “natural order" and imposed entropic artificial integration, producing living species that become “anoetic,” made to serve (...)
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  31. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    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 (...)
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  32. Changement de Méthode causé par la Numérisation.Jörn Lengsfeld - 2019
    La numérisation va de pair avec un changement fondamental des méthodes qui a le potentiel de changer la pensée, les décisions et les actions des gens. Sur la base de cette thèse, une structure est proposée pour l’analyse de le changement de méthode induit par la numérisation. L’article donne un bref aperçu des forces motrices, des formes et des effets de ce changement méthodologique.
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  33. Method Change Caused by Digitalization.Jörn Lengsfeld - 2019
    Digitalization goes hand in hand with a fundamental change in methods that has the potential to change people’s thinking, decisions and actions. Departing from this thesis, a structure is proposed for the analysis of the method change induced by digitalization. The article provides a brief outline of the driving forces, the forms and the effects of this method change.
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  34. Methodenwandel durch Digitalisierung.Jörn Lengsfeld - 2019
    Die Digitalisierung geht mit einem fundamentalen Methodenwandel einher, dem das Potential innewohnt, das Denken, Entscheiden und Handeln der Menschen nachhaltig zu verändern. Ausgehend von dieser These wird eine gliedernde Struktur zur näheren Betrachtung des durch die Digitalisierung induzierten Methodenwandels vorgeschlagen. Der Artikel bietet dazu einen kurzen Abriss über die Triebfedern, die Formen und die Auswirkungen dieses Methodenwandels.
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  35. Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    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 (...)
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  36. The Pharmacological Significance of Mechanical Intelligence and Artificial Stupidity.Adrian Mróz - 2019 - Kultura I Historia 36 (2):17-40.
    By drawing on the philosophy of Bernard Stiegler, the phenomena of mechanical (a.k.a. artificial, digital, or electronic) intelligence is explored in terms of its real significance as an ever-repeating threat of the reemergence of stupidity (as cowardice), which can be transformed into knowledge (pharmacological analysis of poisons and remedies) by practices of care, through the outlook of what researchers describe equivocally as “artificial stupidity”, which has been identified as a new direction in the future of computer science and machine problem (...)
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  37. Machine Learning Application to Predict The Quality of Watermelon Using JustNN.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):1-8.
    In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent accurate for (...)
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  38. Predicting Whether a Couple is Going to Get Divorced or Not Using Artificial Neural Networks.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):49-55.
    In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.
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  39. The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  40. The Pragmatic Turn in Explainable Artificial Intelligence.Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  41. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...)
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  42. Smart Walking System Based on Artificial Intelligence.Vanita Babanne, Simranjeet Kaur, Tejal Mehta, Divya Mulay & Rachana Nagarkar - 2018 - International Journal of Research in Engineering, Science and Management 1 (12).
    This paper shows the smart walking stick based on ultrasonic sensors and Arduino for outwardly debilitated individuals. There are roughly 37 million individuals over the globe who are visually impaired as indicated by the World Health Organization. Individuals with visual inabilities are regularly subjected to outer help which can be given by people, trained dogs, or electronic gadgets as supportive networks for basic assistance. Thus, this played as the motivation to develop a smart cane white stick to survive these restrictions (...)
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  43. Building Machines That Learn and Think About Morality.Christopher Burr & Geoff Keeling - 2018 - In Proceedings of the Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2018). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
    Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...)
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  44. Content and Misrepresentation in Hierarchical Generative Models.Alex Kiefer & Jakob Hohwy - 2018 - Synthese 195 (6):2387-2415.
    In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...)
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  45. Biomedical Ontology Alignment: An Approach Based on Representation Learning.Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith & Dimitris Kiritsis - 2018 - Journal of Biomedical Semantics 9 (21).
    While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...)
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  46. Intelligent Tutoring System for Teaching "Introduction to Computer Science" in Al-Azhar University, Gaza.Ahmad Marouf - 2018 - Dissertation, Al-Azhar University , Gaza
    ITS (Intelligent Tutoring System) is a computer software that supplies direct and adaptive training or response to students without, or with little human teacher interfering. The main target of ITS is smoothing the learning-teaching process using the ultimate technology in computer science. The proposed system will be implemented using the “ITSB” Authoring tool. The book "Introduction To Computer Science" is taught in Al-Azhar University in Gaza as a compulsory subject for students who study at humanities faculties. In this thesis, the (...)
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  47. The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI.Fernando Martínez-Plumed, Bao Sheng Loe, Peter Flach, Sean O. O. HEigeartaigh, Karina Vold & José Hernández-Orallo - 2018 - In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Evolution of the contours of AI. pp. 5180-5187.
    We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI (...)
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  48. Ethical Machines?Ariela Tubert - 2018 - Seattle University Law Review 41 (4).
    This Article explores the possibility of having ethical artificial intelligence. It argues that we face a dilemma in trying to develop artificial intelligence that is ethical: either we have to be able to codify ethics as a set of rules or we have to value a machine’s ability to make ethical mistakes so that it can learn ethics like children do. Neither path seems very promising, though perhaps by thinking about the difficulties with each we may come to a better (...)
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  49. Experiences in Mining Educational Data to Analyze Teacher's Performance: A Case Study with High Educational Teachers.Abdelbaset Almasri - 2017 - International Journal of Hybrid Information Technology 10 (12):1-12.
    Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness (...)
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  50. A Conceptual Framework Over Contextual Analysis of Concept Learning Within Human-Machine Interplays.Farshad Badie - 2017 - In Emerging Technologies for Education. Cham, Switzerland: pp. 65-74.
    This research provides a contextual description concerning an existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on the conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the proposed framework provides (...)
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