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VerWey, John. "The Other Artificial Intelligence Hardware Problem." Computer 55, no. 1 (January 2022): 34–42. http://dx.doi.org/10.1109/mc.2021.3113271.
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LAWRENCE, DAVID R., CÉSAR PALACIOS-GONZÁLEZ, and JOHN HARRIS. "Artificial Intelligence." Cambridge Quarterly of Healthcare Ethics 25, no. 2 (March 9, 2016): 250–61. http://dx.doi.org/10.1017/s0963180115000559.
Повний текст джерелаАнотація:
Abstract:It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries—namely, with regard to artificially (super)intelligent persons—but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.
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Iliescu, Dragos, Samuel Greiff, Matthias Ziegler, and Marjolein Fokkema. "Artificial Intelligence, Machine Learning, and Other Demons." European Journal of Psychological Assessment 38, no. 3 (May 2022): 163–64. http://dx.doi.org/10.1027/1015-5759/a000713.
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Forghani, Reza. "Machine Learning and Other Artificial Intelligence Applications." Neuroimaging Clinics of North America 30, no. 4 (November 2020): i. http://dx.doi.org/10.1016/s1052-5149(20)30067-8.
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Takama, Yasufumi. "Web Intelligence and Artificial Intelligence." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (January 20, 2017): 25–30. http://dx.doi.org/10.20965/jaciii.2017.p0025.
Повний текст джерелаАнотація:
This paper briefly summarizes the progress of artificial intelligence (AI) and web intelligence (WI) in the last two decades. The reason why we mention AI and WI together is because those have strong relationship with each other. This paper first summarizes the history of AI, and then gives brief description of supervised learning, which I think has played a major role in AI in the last two decades. As most history of WI is in the target decades, this paper first briefly describes major WI topics, and then gives more detailed description about information recommendation, which I think one of more successful and necessary technologies in practical use.
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Tasioulas, John. "Artificial Intelligence, Humanistic Ethics." Daedalus 151, no. 2 (2022): 232–43. http://dx.doi.org/10.1162/daed_a_01912.
Повний текст джерелаАнотація:
Abstract Ethics is concerned with what it is to live a flourishing life and what it is we morally owe to others. The optimizing mindset prevalent among computer scientists and economists, among other powerful actors, has led to an approach focused on maximizing the fulfilment of human preferences, an approach that has acquired considerable influence in the ethics of AI. But this preference-based utilitarianism is open to serious objections. This essay sketches an alternative, “humanistic” ethics for AI that is sensitive to aspects of human engagement with the ethical often missed by the dominant approach. Three elements of this humanistic approach are outlined: its commitment to a plurality of values, its stress on the importance of the procedures we adopt, not just the outcomes they yield, and the centrality it accords to individual and collective participation in our understanding of human well-being and morality. The essay concludes with thoughts on how the prospect of artificial general intelligence bears on this humanistic outlook.
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A, Kasianenko, and Fedotov V. "Manifestation of artificial intelligence in human life." Artificial Intelligence 27, jai2022.27(1) (June 20, 2022): 183–92. http://dx.doi.org/10.15407/jai2022.01.183.
Повний текст джерелаАнотація:
Today, in many areas of science and social life, machines, or so-called robots, are entrusted with tasks that previously could only be performed by humans, and this is what led to the creation of artificial intelligence and further stimulates its development and improvement. Automated machines, which are endowed with artificial intelligence, are thus able to relieve a person from routine activities, in particular. Thus, systems based on artificial intelligence are increasingly used in technology, for example, cars endowed with artificial intelligence, or, for example, robots involved in production. That is, the purpose of creating artificial intelligence is primarily to improve human life. However, any system has its shortcomings and problems that need to be explored for further improvement and effective development. It can be stated that scientists identify many problems in the field of artificial intelligence and this list is not exhaustive and with the development of society there will be other debatable issues, however, in my opinion the central problem is the lack of unambiguous opinion on scientific discourse. basic concepts, such as "thinking", "consciousness", "intelligence". And in view of the above, there is an urgent need for a common understanding of these concepts, so that in the future it is possible to qualitatively solve the already mentioned legal and moral problems in the field of artificial intelligence. A large number of domestic researchers are studying issues related to artificial intelligence and looking for ways to overcome problems or at least reduce the number of problems in this area. These include: Karchevsky MV Nikolskny, Yu. V., Pasichnyk VV, Shcherbyna Yu. M., Stefanchuk RO, Pozova DD ,. Radutny OE and others.
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Berrar, Daniel, Naoyuki Sato, and Alfons Schuster. "Quo Vadis, Artificial Intelligence?" Advances in Artificial Intelligence 2010 (February 24, 2010): 1–12. http://dx.doi.org/10.1155/2010/629869.
Повний текст джерелаАнотація:
Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles.
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Bátfai, Norbert. "A játékok és a mesterséges intelligencia mint a kultúra jövője – egy kísérlet a szubjektivitás elméletének kialakítására." Információs Társadalom 18, no. 2 (July 31, 2018): 28. http://dx.doi.org/10.22503/inftars.xviii.2018.2.2.
Повний текст джерелаАнотація:
A cikk célja a mesterséges intelligencia kutatásokat az emberi önmegismerés szolgálatába állítani. Ehhez egyrészt filozófiai hátteret biztosítani, másrészt a mesterséges intelligencia társadalmi elfogadottságát megalapozni. Tézisünk, hogy az emberi kultúra fenntartásához és fejlesztéséhez a játékokon és a mesterséges intelligencián keresztül vezet az út. E tézis alátámasztásnak támogatására kísérletet teszünk a szubjektivitás elméletének megalapozására.
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Games and artificial intelligence as the future of culture: an attempt to develop a theory of subjectivity
The goal of this paper is to use artificial intelligence research to acquire more extensive knowledge of ourselves. On the one hand, we provide a philosophical background to facilitate this, and on the other hand, we try to improve the social acceptance of artificial intelligence. We argue that the way to maintain and further develop human culture is through gaming and artificial intelligence. In support of this thesis we make an attempt to create a theory of subjectivity.
Keywords: artificial intelligence, complexity, entropy, meme, computer games, esport
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Beenish Zahra. "ARTIFICIAL INTELLIGENCE AND CYC." Lahore Garrison University Research Journal of Computer Science and Information Technology 1, no. 4 (December 29, 2017): 29–36. http://dx.doi.org/10.54692/lgurjcsit.2017.010412.
Повний текст джерелаАнотація:
Since 1984, it is enormous work going on for the accomplishing of the project Cyc (‗Saik‘). This work is based on knowledge of ―Artificial Intelligence‖ which is developed by the Cycorpcompany and by Douglas Lenat at MCC. It‘s a Microelectronics and Computer Technology Corporation (MCC) part for so long. The dominant aim of Cycorp to develop this system is to just clarify anything in semantical determination rather than syntactically determination of words commands by the machine in which Cyc is installed to do some job. The other objective was in the building of Cyc is to codify, in a form which is usable by the machine, where knowledge‘s millions piece that composes common sense of a normal human or the common sense made in the human brain. Cyc presents a proprietary schema of knowledge representation that utilized first-order relationships. The relationships that presents between first-order logic (FOL) and first-order theory (FOT). After a long time, in1986, Cyc’s developer (Douglas Lenat) estimate that the total effort required to complete Cyc project would be 250,000 rules and 350 man-years. In 1994, Cyc Project was the reason behind creating independency into Cycorp, in Austin, Texas. As it is a common phrase that "Every tree is a plant" and "Plants die eventually" so that why by the mean of this some knowledge representing pieces which are in the database are like trees and plants like structures. The engine is known as an inference engine, able to draw the obvious results and answer the questions correctly on asking that whether trees die. The Knowledge Base (KB) system, which is included in Cyc, contains more than one million humans like assertions, rules or commonsense ideas. These ideas, rules, and human-defined assertions are describing or formatted in the language known as CycL. They are based on the predication of calculus and many otherhuman-based sciences, which has syntax similar to that of the language ―LISP‖. Though some extend the work on the Cyc project continues as a ―Knowledge Engineering‖, which represents some facts about the world, and implementing effective mechanisms which are derived after reaching the basic level conclusion on that knowledge. As Cyc include the firstorder logic and first order theory, which exist in some relationship; so it definitely uses and handle some other branches for human-interaction like mathematics, philosophy, and linguistics. However, increasingly, the other aim of Cycorp while developing Cyc is involvingan ability, which is given to the Cyc system that it can communicate with end users, by use of CycL, processing of natural language, and can assist with the knowledge formation process through the machine learning.
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Emin Taleh Mammadov, Emin, Hasan Panach Imanli, and Elcin Nizami Huseyn. "ARTIFICIAL INTELLIGENCE PRACTICES IN THE HEALTH SECTOR." NATURE AND SCIENCE 04, no. 05 (December 28, 2020): 21–29. http://dx.doi.org/10.36719/2707-1146/05/21-29.
Повний текст джерелаАнотація:
Artificial intelligence is a technology developed to make machines think like humans. Aristotle's historical artificial intelligence entered the health sector in the 1970s. The first application for artificial intelligence in the internist-1 field in health care, the Casnet expert system, and MYCIN. This technology was later introduced to many areas of health care. The main purpose of this application is for the benefit of doctors and patients. In general, applications, medical decisions, early diagnosis and treatment, drug development, and medical imaging issues deserve attention. Another important issue is the concern about the benefits of artificial intelligence, as well as the possibility of replacing the physicians in charge when medical decisions are left entirely to artificial intelligence. This study aims to provide readers with general information about the areas where artificial intelligence is used in health care. In this study, 14 applications were examined and outstanding results were observed. When doctors say that a comatose patient cannot wake up, artificial intelligence says that a comatose patient can wake up and the patient can wake up. Other advanced artificial intelligence has shown that it can detect cancer more accurately than 58 skin specialists. One study concluded that artificial intelligence with a predictive treatment approach lowered health care costs by 5 to 9% and could lead to an increase in life expectancy of 0.2 to 1.3 years. Keywords: Artificial Intelligence, Health Practices, Health Management
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Lee, Jae Moon, In Hwan Jung, and Kitae Hwang. "Classification of Beef by Using Artificial Intelligence." Webology 19, no. 1 (January 20, 2022): 4639–47. http://dx.doi.org/10.14704/web/v19i1/web19308.
Повний текст джерелаАнотація:
This paper aims to develop an application that classifies the quality of beef via Artificial Intelligence technology, which has experienced rapid technological growth in recent years. The application will allow users to obtain information including, but not limited to, cuts of beef, freshness, and marbling of the beef they are about to purchase. Deep learning image classification was used to classify the cuts of beef, and OpenCV technology was used to determine the freshness and marbling of the beef. The application was developed in a client-server system for real-time action. The mobile phone of the user (the client) will take a photo of the beef and send it to the server, and the server will analyze the received image to identify and determine the cuts of beef, freshness, and marbling of the beef. The results will then be sent back to the client from the server. Artificial Intelligence technology is used to develop applications with these functions. Image classification technology is used for the classification function of beef parts, and OpenCV's clustering technology is used to determine the freshness and marbling grade of beef. Also, Flask web server is used to apply the client-server structure. The developed system worked well for tenderloin, sirloin, and ribs. It provided high confidence over 75% for these cuts. However, it worked poor for other beef cuts. This is simply a learning problem for image classifiers.
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Jeste, Dilip V., Sarah A. Graham, Tanya T. Nguyen, Colin A. Depp, Ellen E. Lee, and Ho-Cheol Kim. "Beyond artificial intelligence: exploring artificial wisdom." International Psychogeriatrics 32, no. 8 (June 25, 2020): 993–1001. http://dx.doi.org/10.1017/s1041610220000927.
Повний текст джерелаАнотація:
ABSTRACTBackground:The ultimate goal of artificial intelligence (AI) is to develop technologies that are best able to serve humanity. This will require advancements that go beyond the basic components of general intelligence. The term “intelligence” does not best represent the technological needs of advancing society, because it is “wisdom”, rather than intelligence, that is associated with greater well-being, happiness, health, and perhaps even longevity of the individual and the society. Thus, the future need in technology is for artificial wisdom (AW).Methods:We examine the constructs of human intelligence and human wisdom in terms of their basic components, neurobiology, and relationship to aging, based on published empirical literature. We review the development of AI as inspired and driven by the model of human intelligence, and consider possible governing principles for AW that would enable humans to develop computers which can operationally utilize wise principles and result in wise acts. We review relevant examples of current efforts to develop such wise technologies.Results:AW systems will be based on developmental models of the neurobiology of human wisdom. These AW systems need to be able to a) learn from experience and self-correct; b) exhibit compassionate, unbiased, and ethical behaviors; and c) discern human emotions and help the human users to regulate their emotions and make wise decisions.Conclusions:A close collaboration among computer scientists, neuroscientists, mental health experts, and ethicists is necessary for developing AW technologies, which will emulate the qualities of wise humans and thus serve the greatest benefit to humanity. Just as human intelligence and AI have helped further the understanding and usefulness of each other, human wisdom and AW can aid in promoting each other’s growth
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Reddy, Sandeep. "Artificial intelligence and healthcare—why they need each other?" Journal of Hospital Management and Health Policy 5 (March 2020): 9. http://dx.doi.org/10.21037/jhmhp-2020-ai-03.
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Balmer, Roberto E., Stanford L. Levin, and Stephen Schmidt. "Artificial Intelligence Applications in Telecommunications and other network industries." Telecommunications Policy 44, no. 6 (July 2020): 101977. http://dx.doi.org/10.1016/j.telpol.2020.101977.
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Rehm, M. Casey. "Other Experts: Disciplinary and Aesthetic Impacts of Artificial Intelligence." Architectural Design 90, no. 5 (September 2020): 14–21. http://dx.doi.org/10.1002/ad.2606.
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Wang, Pei. "On Defining Artificial Intelligence." Journal of Artificial General Intelligence 10, no. 2 (January 1, 2019): 1–37. http://dx.doi.org/10.2478/jagi-2019-0002.
Повний текст джерелаАнотація:
Abstract This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.
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Devedzic, Vladan. "Is this artificial intelligence?" Facta universitatis - series: Electronics and Energetics 33, no. 4 (2020): 499–529. http://dx.doi.org/10.2298/fuee2004499d.
Повний текст джерелаАнотація:
Artificial Intelligence (AI) has become one of the most frequently used terms in the technical jargon (and often in not-so-technical jargon). Recent advancements in the field of AI have certainly contributed to the AI hype, and so have numerous applications and results of using AI technology in practice. Still, just like with any other hype, the AI hype has its controversies. This paper critically examines developments in the field of AI from multiple perspectives - research, technological, social and pragmatic. Part of the controversies of the AI hype stem from the fact that people use the term AI differently, often without a deep understanding of the wider context in which AI as a field has been developing since its inception in Mid 1950s.
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Faber, Jorge, Carolina Faber, and Pedro Faber. "Artificial intelligence in orthodontics." APOS Trends in Orthodontics 9 (December 31, 2019): 201–5. http://dx.doi.org/10.25259/apos_123_2019.
Повний текст джерелаАнотація:
This article aims to discuss how AI with its powerful pattern finding and prediction algorithms are helping orthodontics. Much remains to be done to help patients and clinicians make better treatment decisions. AI is an excellent tool to help orthodontists to choose the best way to move teeth with aligners to preset positions. On the other hand, AI today completely ignores the existence of oral diseases, does not fully integrate facial analysis in its algorithms, and is unable to consider the impact of functional problems in treatments. AI do increase sensitivity and specificity in imaging diagnosis in several conditions, from syndrome diagnosis to caries detection. AI with its set of tools for problem-solving is starting to assist orthodontists with extra powerful applied resources to provide better standards of care.
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STEFANOV, S. Z. "DAILY ARTIFICIAL DISPATCHER – THE OTHER HEAD." New Mathematics and Natural Computation 06, no. 03 (November 2010): 275–83. http://dx.doi.org/10.1142/s179300571000175x.
Повний текст джерелаАнотація:
The paper suggests Daily Artificial Dispatcher (DAD), as an ambient intelligence that solves the problem for automatic predictive analysis of electric power system (EPS) security and efficiency, using EPS and weather summarized data. DAD is constructed as heading EPS, implementing common sense thinking a day ahead for EPS. DAD is verified, as it is shown that it is an intentional agent, a robot, and an intelligent system. DAD is validated, as it is shown that it is an EPS adequate security and efficiency computation.
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Mihret, Estifanos Tilahun. "Robotics and Artificial Intelligence." International Journal of Artificial Intelligence and Machine Learning 10, no. 2 (July 2020): 57–78. http://dx.doi.org/10.4018/ijaiml.2020070104.
Повний текст джерелаАнотація:
Artificial intelligence and robotics are very recent technologies and risks for our world. They are developing their capacity dramatically and shifting their origins of developing intention to other dimensions. When humans see the past histories of AI and robotics, human beings can examine and understand the objectives and intentions of them which to make life easy and assist human beings within different circumstances and situations. However, currently and in the near future, due to changing the attitude of robotic and AI inventors and experts as well as based on the AI nature that their capacity of environmental acquisition and adaptation, they may become predators and put creatures at risk. They may also inherit the full nature of creatures. Thus, finally they will create their new universe or the destiny of our universe will be in danger.
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Akintol, Sarah A. "Optimization of Drilling Cost Using Artificial Intelligence." Petroleum & Petrochemical Engineering Journal 5, no. 4 (2021): 1–8. http://dx.doi.org/10.23880/ppej-16000285.
Повний текст джерелаАнотація:
Drilling operations in the oil and gas industry takes most of the well cost and how fast the drilling bit penetrate and bore the formation is termed the Rate of penetration (ROP). Since most of the cost incurred during drilling is related to the drilling operations, there is need not only to drill carefully, but also to optimize the drilling process. A lot of parameters are related to the rate of penetration which are actually interdependent on each other. This makes it difficult to predict the influence of every single parameter Drilling optimization techniques have been used recently to reduce drilling operation costs. There are different approaches to optimizing the cost of drilling oil and gas wells, some of which include static and /or real time optimization of drilling parameters. A potential area for optimization of drilling cost is through bit run in the well but this is particularly difficult due to its significance in both drilling time and bit cost. In this sense, as a particular bit gets used, it gets dull as its footage increases, resulting from the reduction in the bit penetration rate. The reduction in penetration rate increases total drill time. In order to optimize bit cost, it is desirable to find a trade-off between the two by a bit change policy This study is aimed at minimizing drilling time by use of artificial intelligent for the bit program. Data obtained from a well in the Niger delta region of Nigeria was used in this study and the cost optimization modelled as a Markov decision process where the intelligent agent was to learn the optimal timings for bit change by reinforcement policy Iteration learning. This study was able to achieve its objectives as the reinforcement learning optimization process performed very well with time as the computer agent was able to figure out how to improve drilling cost over time. Better results could be obtained with a better hardware and increased training time.
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Kabanda, Gabriel. "Performance of Machine Learning and other Artificial Intelligence paradigms in Cybersecurity." Oriental journal of computer science and technology 13, no. 1 (May 29, 2020): 1–21. http://dx.doi.org/10.13005/ojcst13.01.01.
Повний текст джерелаАнотація:
Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.
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Padmanabhan, Sandosh, Tran Quoc Bao Tran, and Anna F. Dominiczak. "Artificial Intelligence in Hypertension." Circulation Research 128, no. 7 (April 2, 2021): 1100–1118. http://dx.doi.org/10.1161/circresaha.121.318106.
Повний текст джерелаАнотація:
Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.
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Karmaza, Oleksandra O., Sergii O. Koroied, Vitalii M. Makhinchuk, Valentyna Yu Strilko, and Solomiia T. Iosypenko. "Artificial intelligence in justice." Linguistics and Culture Review 5, S4 (November 24, 2021): 1413–25. http://dx.doi.org/10.21744/lingcure.v5ns4.1764.
Повний текст джерелаАнотація:
The relevance of this study is condition upon the necessity of an in-depth investigation of the phenomenon of artificial intelligence, including its use in the judicial system of various legal states and its impact on the entire judicial system of the state. In this regard, the present paper aims to cover the main definitions of the concept of artificial intelligence, its origins, characteristics, grounds for application, as well as direct interaction and influence on the implementation of the main tasks of justice through the use and development of artificial intelligence in the judicial procedure. The leading method of this study is dialectical, although the authors also employ a combination of other different methods of scientific cognition. The dialectical method, which underlies the theoretical work and is directly listed as fundamental, allowed thoroughly analysing the nature of the concept of artificial intelligence, its key advantages and disadvantages, by analysing its use in the legal systems of the world's leading states. This paper investigates the emergence and transformation of artificial intelligence in modern technological and information relations, its gradual introduction in various spheres of life, namely the ways of implementation and the possibility of application in justice.
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Miranda, Eduardo R., and Duncan Williams. "Artificial Intelligence inOrganised Sound." Organised Sound 20, no. 1 (March 5, 2015): 76–81. http://dx.doi.org/10.1017/s1355771814000454.
Повний текст джерелаАнотація:
Artificial Intelligence is a rich and still-developing field with a number of musical applications. This paper surveys the use of Artificial Intelligence in music in the pages ofOrganised Sound, from the first issue to the latest, at the time of writing. Traditionally, Artificial Intelligence systems for music have been designed with note-based composition in mind, but the research we present here finds that Artificial Intelligence has also had a significant impact in electroacoustic music, with contributions in the fields of sound analysis, real-time sonic interaction and interactive performance-driven composition, to cite but three. Two distinct categories emerged in theOrganised Soundpapers: on the one hand, philosophically and/or psychologically inspired, symbolic approaches and, on the other hand, biologically inspired approaches, also referred to as Artificial Life approaches. The two approaches are not mutually exclusive in their use, and in some cases are combined to achieve ‘best of both’ solutions. That said, asOrganised Soundis uniquely positioned in the electroacoustic music community, it is somewhat surprising that work addressing important compositional issues such as musical form and structure, which Artificial Intelligence can be readily applied to, is not more present in these pages.
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Guthro, Clem. "Artificial Unintelligence: How Computers Misunderstand the World by Meridith Broussard." Journal of Intellectual Freedom & Privacy 3, no. 2-3 (January 15, 2019): 13. http://dx.doi.org/10.5860/jifp.v3i2-3.6776.
Повний текст джерелаАнотація:
Broussard, an assistant professor at New York University’s Arthur L Carter Journalism Institute, has written an accessible book on Artificial Intelligence’s (AI) grip on people’s imagination. In twelve short chapters, she lays out a cautionary narrative on the limits of AI and technology in general. Her book joins several other recent volumes that attempt to show the limits of AI and the ethical implications of wholesale and blind adoption of AI to solve the world’s problems. These include M. Tegmark. Life 3.0: Being Human in the Age of Artificial Intelligence, 2018; J. Aoun. Robot-Proof: Higher Education in the Age of Artificial Intelligence, 2017; M. Boden. Artificial Intelligence: A Very Short Introduction, 2018; and H. Collins. Artifictional Intelligence: Against Humanity’s Surrender to Computers, 2018.
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Sharma, Anupam, and Jasleen Kaur. "Artificial Intelligence Based System." Information Resources Management Journal 34, no. 2 (April 2021): 80–90. http://dx.doi.org/10.4018/irmj.2021040105.
Повний текст джерелаАнотація:
The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use of AI in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. The authors provide a general overview of AI and how it can be used to improve global health outcomes in resource-poor settings. They also describe some of the current ethical debates around patient safety and privacy. The research paper specifically highlights the challenges related to women menstrual hygiene and suggests AI technology for improving the menstrual hygiene and healthcare services in resource-poor settings for women. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate the realization of the full potential of AI for improving global health in resource-poor contexts.
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Hann, Alexander, and Alexander Meining. "Artificial Intelligence in Endoscopy." Visceral Medicine 37, no. 6 (2021): 471–75. http://dx.doi.org/10.1159/000519407.
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<b><i>Background:</i></b> Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. <b><i>Summary:</i></b> In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. <b><i>Key Messages:</i></b> The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.
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Ekler, Péter, and Dániel Pásztor. "Alkalmazott mesterséges intelligencia felhasználási területei és biztonsági kérdései – Mesterséges intelligencia a gyakorlatban." Scientia et Securitas 1, no. 1 (December 17, 2020): 35–42. http://dx.doi.org/10.1556/112.2020.00006.
Повний текст джерелаАнотація:
Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.
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Schneider, Howard. "Navigation Map-Based Artificial Intelligence." AI 3, no. 2 (May 12, 2022): 434–64. http://dx.doi.org/10.3390/ai3020026.
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A biologically inspired cognitive architecture is described which uses navigation maps (i.e., spatial locations of objects) as its main data elements. The navigation maps are also used to represent higher-level concepts as well as to direct operations to perform on other navigation maps. Incoming sensory information is mapped to local sensory navigation maps which then are in turn matched with the closest multisensory maps, and then mapped onto a best-matched multisensory navigation map. Enhancements of the biologically inspired feedback pathways allow the intermediate results of operations performed on the best-matched multisensory navigation map to be fed back, temporarily stored, and re-processed in the next cognitive cycle. This allows the exploration and generation of cause-and-effect behavior. In the re-processing of these intermediate results, navigation maps can, by core analogical mechanisms, lead to other navigation maps which offer an improved solution to many routine problems the architecture is exposed to. Given that the architecture is brain-inspired, analogical processing may also form a key mechanism in the human brain, consistent with psychological evidence. Similarly, for conventional artificial intelligence systems, analogical processing as a core mechanism may possibly allow enhanced performance.
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Makhamatov, T. M. "Philosophy of Artificial Intelligence." Humanities and Social Sciences. Bulletin of the Financial University 9, no. 4 (December 4, 2019): 52–56. http://dx.doi.org/10.26794/2226-7867-2019-9-4-52-56.
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In the article, the author substantiates the thesis that the development of artificial intelligence technology is closely related not only to discoveries in the field of natural science, anthropology and medicine, but also achievements in the field of philosophy of knowledge and cognitive sciences. The author conducted a philosophico-epistemological analysis of the problems of improving the neural network as the core of modern artificial intelligence led to the conclusion that the principles of functioning of the neural network corresponding to such principles of the cognitive process discovered and studied in the philosophical concepts of New Time, such as J. Locke’s apriorism, I. Kant’s apriorism other. The results of the comparative study allowed the author to come to the following conclusion: the improvement of the abilities of the neural network studied by S. Haikin, S. Russell, P. Norvig (“evidence of the answer”, “classification of images” and “reliability of the decision”) is possible when relying on the epistemological ideas of J. Locke, using Kant’s principles (“synthetic unity of apperception”, “I think”) and searching for the algorithm of neural network formation of the ability to create antinomies in artificial intelligence. Further development of artificial intelligence based on the neural network can also be based on the theory of cognition of T. Hobbes, R. Descartes, B. Spinoza, G. V. F. Hegel and the results of modern cognitive sciences.
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Dzyaloshinsky, I. M. "Artificial Intelligence: A Humanitarian Perspective." Vestnik NSU. Series: History and Philology 21, no. 6 (June 17, 2022): 20–29. http://dx.doi.org/10.25205/1818-7919-2022-21-6-20-29.
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The article is devoted to the study of the features of human intelligence and the intelligence of complex computer systems, usually referred to as artificial intelligence (AI). As a hypothesis, a statement was formulated about a significant difference between human and artificial intelligence. Human intelligence is a product of a multi-thousand-year history of the development and interaction of three interrelated processes: 1) the formation and development of the human personality; 2) the formation of complex network relationships between members of the social community; 3) collective activity as the basis for the existence and development of communities and individuals. AI is a complex of technological solutions that imitate human cognitive processes. Because of this, with all the options for technical development (acceleration of processes for collecting and processing data and finding solutions, using computer vision, speech recognition and synthesis, etc.). AI will always be associated with human activity. In other words, only people (not machines) are the ultimate source and determinant of values on which any artificial intelligence depends. No mind (human or machine) will ever be truly autonomous: everything we do depends on the social context created by other people who determine the meaning of what we want to achieve. This means that people are responsible for everything that AI does.
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Patel, Kunal Girdhar, and Mittal Sanjay Patil. "Artificial Intelligence in Agriculture." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 624–27. http://dx.doi.org/10.22214/ijraset.2022.40308.
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Abstract: Agriculture Sector plays important role in economic sector. The artificial intelligence is main concern and the emerging subject all across world. And population increasing day by day and with the increasing demand employment and food is also increasing. Our traditional method which was used by the farmer were not sufficient enough to fulfill the requirements. Consequently, synthetic intelligence technique is added. This method supplied meals requirement and employment possibilities to many people. Artificial Intelligence in agriculture has added associate agriculture revolution. This generation has covered the crop yield from different factors like weather adjustments, populace increase, employment problems, and meals protection issues. This era includes crop yields caused by various factors such as climate change, population surge, employment issues, and food security issues. The main difficulty of the document is to verify the many artificial intelligence applications in agriculture, including irrigation, weeding and spraying integrated with sensors and other tools used in robots and drones. These technologies can save extra water, the use of pesticides and herbicides, maintain soil fertility, and also help to effectively use manpower, increase productivity, and improve service quality. Implementation of automation in agriculture, the weeding structures thru robots and drones. The diverse soil water sensing techniques are mentioned together with computerized weeding techniques. The implementation of drones is mentioned, the diverse techniques utilized by drones for spraying and crop-tracking is likewise mentioned on this paper Keywords: Image recognition, cognitive science, deep learning, sematic analysis, Neural network
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Tudorache, Paul. "Applicability of Artificial Intelligence in Decision-Making for Land Forces." Vojenské rozhledy 30, no. 2 (June 8, 2021): 039–54. http://dx.doi.org/10.3849/2336-2995.30.2021.02.039-054.
Повний текст джерелаАнотація:
Similar to other fields, also in the military one, the Artificial Intelligence has become recently an evident solution for optimizing specific processes and activities. Therefore, this research paper aims to highlight the potential uses of Artificial Intelligence in the military operations carried out by the Land Forces. In this regard, analysing the framework of the operations process and applying suitable research methodology, the main findings are related to AI’s contributions in optimizing commander’s decisions during the progress of planning and execution. On the other hand, picturing the AI upgrated combat power of the Land Forces is another significant result of this study.
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Liu, Ziheng. "The Evolution of Artificial Intelligence and its Collaboration with Brain Science." Highlights in Science, Engineering and Technology 1 (June 14, 2022): 31–39. http://dx.doi.org/10.54097/hset.v1i.424.
Повний текст джерелаАнотація:
The emergence of artificial intelligence is the product of the development of science and technology. At the same time, the development of science and technology has greatly improved the ability of the human brain and artificial intelligence. For example, scientists choose to train artificial intelligence with video games and improve the flexibility of the human brain. One example of artificial intelligence’s success and its rapid development is AlphaGo. Go has long been recognized as one of the most difficult games in the world, and it seems surprising that artificial intelligence could easily win a match against a human master. But, it wasn't easy, and the reason for AlphaGo's victory has a lot to do with machine learning. By mimicking humans and their own evolution to give themselves an edge in the game and win. At the same time, many people think that artificial intelligence is very dangerous, and they think that artificial intelligence will replace or eliminate human beings, but the fact is the same as people's imagination? The benevolent see benevolence and the wise see wisdom. After decades of development, artificial intelligence can reach the level of cooperation with humans. Basic cooperation can no longer meet people's needs, and more advanced cooperation projects need to be developed. Human beings and artificial intelligence help each other, especially in brain science and the application of CNN in image processing. In these studies, human beings provide inspiration for the research and development of artificial intelligence, and artificial intelligence provides convenience for human life. Therefore, we can know that artificial intelligence plays a very important role in the evolution of human beings.
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Ahmad, S. Rehan. "Artificial Intelligence: Use in Clinical and Genomic Diagnostics." Emerging Trends in Nutraceuticals 1, no. 2 (August 28, 2022): 42–50. http://dx.doi.org/10.18782/2583-4606.111.
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The development of computer systems that are capable of carrying out tasks that typically require human intelligence is known as artificial intelligence (AI). Recent and quickly rising interest in medical AI applications is a result of AI software and technology improvements, especially deep learning algorithms and the graphics processing units (GPUs) that enable their training. While other AI subtypes have started to show similar promise in different diagnostic modalities, AI-based computer vision methods are poised to change image-based diagnostics in clinical diagnostics. Large and complicated genomic datasets are processed using a particular form of AI algorithm known as deep learning in various fields, such as clinical genomics. In this review, we first provide an overview of the primary categories of issues that AI systems are best adapted to address, followed by a description of the clinical diagnostic tasks that are aided by these solutions. Then, we concentrate on recently developed techniques for certain clinical genomics applications, such as variant calling, genome annotation and variant categorization, and phenotype-to-genotype correlation. We conclude by talking about the future potential of AI in individualized medicine applications, particularly for risk prediction in common complex diseases, as well as the issues, constraints, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those using data from genomics and human genetics.
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Rab, Suzanne. "Artificial intelligence, algorithms and antitrust." Competition Law Journal 18, no. 4 (January 29, 2020): 141–50. http://dx.doi.org/10.4337/clj.2019.04.02.
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The field of artificial intelligence or ‘AI’ has been reshaping virtually every industry built on the idea that machines could be used to simulate human intelligence through so-called ‘machine learning’. Antitrust interest in this topic has been generated among regulators, policy-makers, academics and business in the EU and internationally. This article explores the extent to which AI may raise competition or other concerns for consumer welfare and whether existing legal and policy instruments are appropriate to deal with the emerging opportunities and challenges.
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Kim, Jae-Woong. "Artificial intelligence, conversation with a parasitic other self -The survival method of stem (artificial intelligence) in the movie〈Upgrade〉-." Cartoon and Animation Studies 66 (March 31, 2022): 333–64. http://dx.doi.org/10.7230/koscas.2022.66.333.
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Attanasio, Simona, Sara Maria Forte, Giuliana Restante, Michela Gabelloni, Giuseppe Guglielmi, and Emanuele Neri. "Artificial intelligence, radiomics and other horizons in body composition assessment." Quantitative Imaging in Medicine and Surgery 10, no. 8 (August 2020): 1650–60. http://dx.doi.org/10.21037/qims.2020.03.10.
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Prylypko, Daryna. "Artificial intelligence and copyright." Theory and Practice of Intellectual Property, no. 2 (July 6, 2021): 15–22. http://dx.doi.org/10.33731/22021.236526.
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Key words: copyright, work, artificial intelligence, computer program
In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.
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ACYPRESTE, RAFAEL DE, and EDEMILSON PARANÁ. "Artificial Intelligence and employment: a systematic review." Brazilian Journal of Political Economy 42, no. 4 (December 2022): 1014–32. http://dx.doi.org/10.1590/0101-31572022-3320.
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ABSTRACT This paper presents a systematic literature review, grounded on bibliometric procedures, of the (political economy) works, produced from 2008 to 2020, on the relations between Artificial Intelligence and employment. It detects a growing tendency of published papers in this field, especially from 2019, and identifies four main groups of concerns on this topic. Within these groups, a prevalence of more optimistic over skeptical accounts and, especially, of economic orthodox over heterodox approaches on the issue can be noted. Overall, it is possible to understand that both the reviewed works and their metrics are quite dispersed and varied in scope. Among other reasons, this is due to the lack of a common basic definition, within the field, of AI in the first place.
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Panchariya, Dev Arastu. "The Theory of Natural-Artificial Intelligence." European Journal of Artificial Intelligence and Machine Learning 1, no. 1 (February 15, 2022): 1–3. http://dx.doi.org/10.24018/ejai.2022.1.1.2.
Повний текст джерелаАнотація:
In recent times, mankind is seeking for certain peculiar solutions to multiple facets containing an identically very fundamental philosophy i.e., certainly intend to have indeterminism as a primordial prerequisite; however, that indeterminism is itself like a void filled with determinism as analogous to the quantum computing as qubits and the corresponding complexity. In the meantime, there are algorithms and mathematical frameworks and those in general; yield the required distinctions in the underlying theories constructed upon principles which then give rise to respective objectifications. But, when it comes to the Artificial Intelligence and Machine Learning, then there find some mathematical gaps in order to connect other regimes in relation of one and the other. The proposed discovery in this paper is about quilting some of those gaps as like the whole structure of Artificial Intelligence is yet to be developed in the realm concerning with responsive analysis in betwixt to humans and machines or beyond to such analogy. Hence, the entire introduction & incitement of this theory is to mathematically determine the deep rationality as responsive manifestation of human brain with a designed computing and both with the highest potential degree of attributions or overlaps and both the conditions will be shown mathematically herewith as identifications that make each other separate and clear to persuade.
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Santy, R. D., M. I. Habibillah, Y. R. Dimyati, V. S. S. Nofia, S. Luckyardi, T. V. L. Gaol, and D. Oktafiani. "Artificial Intelligence as Human Behavior Detection for Auto Personalization Function in Social Media Marketing." International Journal of Research and Applied Technology 1, no. 1 (June 25, 2021): 25–34. http://dx.doi.org/10.34010/injuratech.v1i1.5456.
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This study aims to determine how artificial intelligence's role in increasing marketing activities' effectiveness in automatic personalization. Some of the factors that can influence humans using social media are what they like, where they comment, and their type in the search fields on their social media. The method used in this research is a comparative descriptive method, namely describing or explaining and validating a phenomenon under study, then comparing it with other social media that has a different system. Some of the variables or objects studied in this paper are the user's habits in using social media and the artificial intelligence found on several social media that reacts with their users' habits. This study shows how artificial intelligence can increase the effectiveness of marketing activities on social media and produce comparable data between artificial intelligence technologies found in several social media, which shows that artificial intelligence is proven to increase marketing activities' effectiveness. It is done on social media.
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Orsi Koch Delgado, Heloísa, Aline De Azevedo Fay, Maria José Sebastiany, and Asafe Davi Cortina Silva. "Artificial intelligence adaptive learning tools." BELT - Brazilian English Language Teaching Journal 11, no. 2 (December 31, 2020): e38749. http://dx.doi.org/10.15448/2178-3640.2020.2.38749.
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This paper explores the field of Artificial Intelligence applied to Education, focusing on the English Language Teaching. It outlines concepts and uses of Artificial Intelligence, and appraises the functionalities of adaptive tools, bringing evaluative feedback on their use by American school teachers, and highlighting the importance of additional research on the matter. It was observed that the tools are valid media options to complement teaching, especially concerning adaptive learning. They offer students more inclusive opportunities: they maximize learning by tailoring instruction to address students ‘needs, and helping students become more responsible for their own schooling. As for teachers, their testimonials highlight the benefits of dedicating more class time to the students’ most pressing weaker areas. Drawbacks might include the need to provide teachers with autonomy to override recommendations so as to help them find other ways to teach a skill that seems to be more effective for a specific student.
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Romm, Eden L., and Igor F. Tsigelny. "Artificial Intelligence in Drug Treatment." Annual Review of Pharmacology and Toxicology 60, no. 1 (January 6, 2020): 353–69. http://dx.doi.org/10.1146/annurev-pharmtox-010919-023746.
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The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.
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Chen, Li-Shan, Yen-Ming Tseng, and Xiao-Na Lin. "Artificial intelligence in safety system." MATEC Web of Conferences 185 (2018): 00009. http://dx.doi.org/10.1051/matecconf/201818500009.
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This research aims to study learning environment, and let the learning environment become smart. Swarm intelligence, cloud computing, and active Ultra-High Frequency RFID were used on it. We built friendly human-computer-interface software for users to use as pad phone. The Extensible Markup Language (XML) and C sharp language were used in this research. If the users begin to search, the kernel safety learning system automatically communicates with other RFID readers by agents, and the agents can search the closer camera for users. This study’s result has successfully implemented to Chin-Huo educational organization, and it would be helpful for the paterfamilias to hold all situations about their children at Chin-Huo educational organization. Paterfamilias can understand their children’s learning, going to Chin-Huo and leaving Chin-Huo through personal computers, or notebooks simultaneously or asynchronously by the computer-mediated communication. That will be great help in the grip of whole after-school remedial education, teaching and learning situation.
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Krittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, Scott Kaplin, Eric Venner, Mullai Murugan, Zhen Wang, et al. "Artificial Intelligence and Cardiovascular Genetics." Life 12, no. 2 (February 14, 2022): 279. http://dx.doi.org/10.3390/life12020279.
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Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
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Yildirim, Yetkin, Emin Alp Arslan, Kamil Yildirim, and Ibrahim Bisen. "Reimagining Education with Artificial Intelligence." Eurasian Journal of Higher Education 2, no. 4 (September 24, 2021): 32–46. http://dx.doi.org/10.31039/ejohe.2021.4.52.
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Artificial intelligence (AI) technologies have been implemented successfully in many industries, from virtual hospital assistants to algorithm-based warehouse processing. And now that Covid-19 has forced students and teachers to transition to online or hybrid learning, these technologies could offer new and exciting possibilities for education as well. By incorporating AI and machine learning tools into online classrooms, educators can address many of the challenges that have emerged with the recent loss of face-to-face instruction, including the struggle for students to self-regulate their learning, the burden of curriculum planning and administrative work for teachers, and the loss of personalized interaction between students and teachers. This chapter will explore some of the AI technologies that have been used in educational contexts and describe applications of AI in other industries that could be adapted to create more personalized, flexible, inclusive, and engaging learning experiences. If the future of education is going to include online learning as a substantial component, then AI could be the key to maintaining high levels of motivation and engagement from students and teachers alike.
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Paliński, Andrzej. "Prognozowanie zapotrzebowania na gaz metodami sztucznej inteligencji." Nafta-Gaz 75, no. 2 (February 2019): 111–17. http://dx.doi.org/10.18668/ng.2019.02.07.
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The paper presents contemporary trends in artificial intelligence and machine learning methods, which include, among others, artificial neural networks, decision trees, fuzzy logic systems and others. Computational intelligence methods are part of the field of research on artificial intelligence. Selected methods of computational intelligence were used to build medium-term monthly forecasts of natural gas demand for Poland. The accuracy of forecasts obtained using the artificial neural network and the decision tree with classical linear regression was compared based on historical data from a ten-year period. The explanatory variables were: gas consumption in other EU countries, average monthly temperature, industrial production, wages in the economy and the price of natural gas. Forecasting was carried out in five stages differing in the selection of the learning and testing sample, the use of data preprocessing and the elimination of some variables. For raw data and a random training set, the highest accuracy was achieved by linear regression. For the preprocessed data and the random learning set, the decision tree was the most accurate. The forecast obtained on the basis of the first eight years and tested on the last two was most accurately created by regression, but only slightly better than with the decision tree or neural network, regardless of data normalization and elimination of collinear variables. Machine learning methods showed good accuracy of monthly gas consumption forecasts, but nevertheless slightly gave way to classical linear regression, due to too narrow set of explanatory variables. Machine learning methods will be able to show higher effectiveness as the number of data increases and the set of potential explanatory variables is expanded. In the sea of data, machine learning methods are able to create prognostic models more effectively, without the analyst’s laborious involvement in data preparation and multi-stage analysis. They will also allow for the frequent updating of the form of prognostic models even after each addition of new data into the database.