Results for 'Machine learning'

998 found
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  1. 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 (...)
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  2.  27
    Clinical Applications of Machine Learning Algorithms: Beyond the Black Box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  3. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models (...)
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  4. Machines Learning Values.Steve Petersen - forthcoming - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, USA: Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: (...)
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  5. Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning.Gabbrielle Johnson - forthcoming - Journal Moral Philosophy.
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments (...)
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  6.  51
    The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to (...)
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  7. Machine Learning and Job Posting Classification: A Comparative Study.Ibrahim M. Nasser & Amjad H. Alzaanin - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):06-14.
    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 (...)
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  8. What Counts as “Clinical Data” in Machine Learning Healthcare Applications?Joshua August Skorburg - 2020 - American Journal of Bioethics 20 (11):27-30.
    Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .
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  9.  10
    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 (...)
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  10.  77
    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 (...)
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  11.  98
    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 (...)
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  12.  99
    Autonomy and Machine Learning as Risk Factors at the Interface of Nuclear Weapons, Computers and People.S. M. Amadae & Shahar Avin - 2019 - In Vincent Boulanin (ed.), The Impact of Artificial Intelligence on Strategic Stability and Nuclear Risk: Euro-Atlantic Perspectives. Stockholm, Sweden: pp. 105-118.
    This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and (...)
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  13.  95
    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 (...)
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  14. Consequences of Unexplainable Machine Learning for the Notions of a Trusted Doctor and Patient Autonomy.Michal Klincewicz & Lily Frank - 2020 - Proceedings of the 2nd EXplainable AI in Law Workshop (XAILA 2019) Co-Located with 32nd International Conference on Legal Knowledge and Information Systems (JURIX 2019).
    This paper provides an analysis of the way in which two foundational principles of medical ethics–the trusted doctor and patient autonomy–can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how to anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as (...)
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  15.  61
    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 (...)
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  16. Human-Aided Artificial Intelligence: Or, How to Run Large Computations in Human Brains? Towards a Media Sociology of Machine Learning.Rainer Mühlhoff - 2019 - New Media and Society 1.
    Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media (...)
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  17.  30
    Overhead Cross Section Sampling Machine Learning Based Cervical Cancer Risk Factors Prediction.A. Peter Soosai Anandaraj, - 2021 - Turkish Online Journal of Qualitative Inquiry (TOJQI) 12 (6): 7697-7715.
    Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk factor dataset from KAGGLE (...)
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  18.  32
    A Comprehensive Study on Machine Learning Approaches with Big Data.Satish Muppdi, B. V. N. K. S. S. D. Aditya & P. Sri Rama Krishna - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (1):23-27.
    Abstract: Big Data has altered the adjustments in the period of information stockpiling and its examination. Big Data Analytics is used to understand the information productivity that builds the extent of expectation and discoveries of hidden patterns. The age of information ought to be successfully figured out how to enhance the computational efficiencies of the frameworks. Machine Learning (ML) has turned into a remarkable computational tool for read, examine, giving bits of knowledge and choices. Information expectations, presumptions and (...)
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  19. 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 (...)
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  20. Big Data Optimization in Machine Learning.Xiaocheng Tang - 2015 - Disertation 1.
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  21.  25
    Prognostic System for Heart Disease Using Machine Learning: A Review.R. Senthilkumar - 2021 - Journal of Science Technology and Research (JSTAR) 2 (1):33-38.
    In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...)
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  22.  41
    A Hybrid Automated Intelligent COVID-19 Classification System Based on Neutrosophic Logic and Machine Learning Techniques Using Chest X-Ray Images.Ibrahim Yasser, Aya A. Abd El-Khalek, A. A. Salama, Abeer Twakol, Mohy-Eldin Abo-Elsoud & Fahmi Khalifa - forthcoming - In Advances in Data Science and Intelligent Data Communication Technologies for COVID-19 Pandemic (DSIDC-COVID-19) ,Studies in Systems, Decision and Control.
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  23.  74
    The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Device.Sara Gerke, Boris Babic, Theodoros Evgeniou & I. Glenn Cohen - 2020 - Nature Digital Medicine 53 (3):1-4.
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  24. Can Machines Read Our Minds?Christopher Burr & Nello Cristianini - 2019 - Minds and Machines 29 (3):461-494.
    We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in (...)
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  25.  61
    Machine Grading and Moral Learning.Joshua Schulz - 2014 - New Atlantis: A Journal of Technology and Society 41 (Winter):2014.
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  26. Banana Classification Using Deep Learning.Ahmed F. Al-Daour, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 3 (12):6-11.
    Abstract: Banana, fruit of the genus Musa, of the family Musaceae, one of the most important fruit crops of the world. The banana is grown in the tropics, and, though it is most widely consumed in those regions, it is valued worldwide for its flavour, nutritional value, and availability throughout the year. Cavendish, or dessert, bananas are most commonly eaten fresh, though they may be fried or mashed and chilled in pies or puddings. They may also be used to flavour (...)
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  27. Performance Vs. Competence in Human–Machine Comparisons.Chaz Firestone - 2020 - Proceedings of the National Academy of Sciences 41.
    Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. (...)
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  28. Analyzing Types of Cherry Using Deep Learning.Izzeddin A. Alshawwa, Hosni Qasim El-Mashharawi, Mohammed Elkahlout, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (1):1-5.
    A cherry is the fruit of many plants of the genus Prunus, and is a fleshy drupe (stone fruit), Michigan's Northwest Lower Peninsula is the largest producer of tart cherries in the United States. In fact, grow 75% of the country's variety of mighty Montmorency cherries. We use these Ruby Red Morsels of Joy in over 200 cherry products like Salsas, Chocolate Covered Cherries, Cherry Nut Mixes, and much more. Cherry fruits are rich in vitamins and minerals, and it is (...)
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  29. Classification of Apple Fruits by Deep Learning.Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):1-7.
    Abstract: Apple is a plant species that follows the apple genus, which is a fruit because it contains seeds of the pink family. It is one of the most fruit trees in terms of agriculture. The apple tree is small in length from 3 to 12 meters. Several recent studies have shown many health benefits of apples. It helps with the strengthening of the brain, heart, and stomach. It is used in the treatment of joint pain and limberness. It is (...)
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  30. Grape Type Classification Using Deep Learning.Hosni Qasim El-Mashharawi, Samy S. Abu-Naser, Izzeddin A. Alshawwa & Mohammed Elkahlout - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):41-45.
    Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...)
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  31. Peach Type Classification Using Deep Learning.Mohammed I. El-Kahlout & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):35-40.
    Abstract: Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees (...)
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  32. 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 (...)
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  33.  74
    Grape Type Classification Using Deep Learning.Hosni Qasim El-Mashharawi, Samy S. Abu-Naser, Izzeddin A. Alshawwa & Mohammed Elkahlout - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):41-45.
    Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...)
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  34. Learning to Discriminate: The Perfect Proxy Problem in Artificially Intelligent Criminal Sentencing.Benjamin Davies & Thomas Douglas - manuscript
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring (...)
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  35. Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation.Conor Mayo-Wilson, Kevin Zollman & David Danks - 2013 - International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (...)
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  36. Reconceptualizing the Organism: From Complex Machine to Flowing Stream.Daniel J. Nicholson - 2018 - In Daniel J. Nicholson & John A. Dupre (eds.), Everything Flows: Towards a Processual Philosophy of Biology.
    This chapter draws on insights from non-equilibrium thermodynamics to demonstrate the ontological inadequacy of the machine conception of the organism. The thermodynamic character of living systems underlies the importance of metabolism and calls for the adoption of a processual view, exemplified by the Heraclitean metaphor of the stream of life. This alternative conception is explored in its various historical formulations and the extent to which it captures the nature of living systems is examined. Following this, the chapter considers the (...)
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  37.  31
    Undersampling Aware Learning Based Fetal Health Prediction Using Cardiotocographic Data.M. Shyamala Devi - 2021 - Turkish Online Journal of Qualitative Inquiry (TOJQI) 12 (6):7730-7749.
    With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized (...)
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  38. Turing on the Integration of Human and Machine Intelligence.S. G. Sterrett - 2014
    Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There are hopes (...)
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  39.  38
    Shared Decision‐Making and Maternity Care in the Deep Learning Age: Acknowledging and Overcoming Inherited Defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, (...)
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  40. Rethinking Machine Ethics in the Era of Ubiquitous Technology.Jeffrey White (ed.) - 2015 - IGI.
    Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...)
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  41. Adaptive Intelligent Tutoring System for Learning Computer Theory.Mohammed A. Al-Nakhal & Samy S. Abu Naser - 2017 - EUROPEAN ACADEMIC RESEARCH 4 (10).
    In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according (...)
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  42. Deep Learning Classification of Peach Fruits.AlKahlout Mohammad - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):35-40.
    Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees may (...)
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  43. Information, Learning and Falsification.David Balduzzi - 2011
    There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical (...)
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  44. Machine Intelligence: A Chimera.Mihai Nadin - 2019 - AI and Society 34 (2):215-242.
    The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...)
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  45.  84
    Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts.Hasyim Muhammad - 2021 - International Journal of Advanced Computer Science and Applications 12 (3):186-191.
    The use of machine translation as artificial intelligence (AI) keeps increasing and the world’s most popular a translation tool is Google Translate (GT). This tool is not merely used for the benefits of learning and obtaining information from foreign languages through translation but has also been used as a medium of interaction and communication in hospitals, airports and shopping centres. This paper aims to explore machine translation accuracy in translating French-Indonesian culinary texts (recipes). The samples of culinary (...)
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  46.  19
    Using Deep Learning to Detect Facial Markers of Complex Decision Making.Gianluca Guglielmo, Irene Font Peradejordi & Michal Klincewicz - forthcoming - Lecture Notes in Computer Science (Advances in Computer Games 2021).
    In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game where players have to survive in a post-apocalyptic world filled with zombies. We used OpenFace software to extract action unit (AU) intensities of facial expressions characteristic of decision-making processes and then we implemented a simple convolution neural network (CNN) to see which AUs are predictive of decision-making. Our results provide evidence that the pre-decision variations in action units 17 (chin raiser), 23 (...)
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  47. Interprétabilité et explicabilité pour l’apprentissage machine : entre modèles descriptifs, modèles prédictifs et modèles causaux. Une nécessaire clarification épistémologique.Christophe Denis & Franck Varenne - 2019 - Actes de la Conférence Nationale En Intelligence Artificielle - CNIA 2019.
    Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathématique et causale d’un phénomène (...)
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  48. The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics.Roberto Cordeschi - 2002 - Kluwer Academic Publishers.
    Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building (...)
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  49. Deepfakes and the Epistemic Backstop.Regina Rini - 2020 - Philosophers' Imprint 20 (24):1-16.
    Deepfake technology uses machine learning to fabricate video and audio recordings that represent people doing and saying things they've never done. In coming years, malicious actors will likely use this technology in attempts to manipulate public discourse. This paper prepares for that danger by explicating the unappreciated way in which recordings have so far provided an epistemic backstop to our testimonial practices. Our reasonable trust in the testimony of others depends, to a surprising extent, on the regulative effects (...)
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  50. A Theory Explains Deep Learning.Kenneth Kijun Lee & Chase Kihwan Lee - manuscript
    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 (...)
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