Zeitschriftenartikel: „AI-computer“

1

Nareyek, Alexander. „AI in Computer Games“. Queue 1, Nr. 10 (Februar 2004): 58–65. http://dx.doi.org/10.1145/971564.971593.

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2

Jaimes, Alex. „Computer Vision Startups Tackle AI“. IEEE MultiMedia 23, Nr. 4 (Oktober 2016): 94–96. http://dx.doi.org/10.1109/mmul.2016.62.

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3

Camstra, Bert. „AI in computer-based training“. British Journal of Educational Technology 39, Nr. 2 (März 2008): 297–303. http://dx.doi.org/10.1111/j.1467-8535.2008.00820.x.

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4

Cass, S. „Mind games [computer game AI]“. IEEE Spectrum 39, Nr. 12 (Dezember 2002): 40–44. http://dx.doi.org/10.1109/mspec.2002.1088444.

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5

Kersting, Kristian. „Rethinking Computer Science Through AI“. KI - Künstliche Intelligenz 34, Nr. 4 (Dezember 2020): 435–37. http://dx.doi.org/10.1007/s13218-020-00692-5.

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6

Bouzy, Bruno, und Tristan Cazenave. „Computer Go: An AI oriented survey“. Artificial Intelligence 132, Nr. 1 (Oktober 2001): 39–103. http://dx.doi.org/10.1016/s0004-3702(01)00127-8.

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7

Chauvette, Peter. „Formalism in AI and computer science“. Information Processing & Management 28, Nr. 2 (Januar 1992): 284–85. http://dx.doi.org/10.1016/0306-4573(92)90055-5.

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8

Malcolm, Grant. „Formalism in AI and computer science“. Science of Computer Programming 17, Nr. 1-3 (Dezember 1991): 267–69. http://dx.doi.org/10.1016/0167-6423(91)90052-y.

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9

Frey, Peter W. „Memory Based Expertise: Computer Chess vs. AI“. ICGA Journal 14, Nr. 4 (01.12.1991): 192–97. http://dx.doi.org/10.3233/icg-1991-14403.

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10

Khoo, A., und R. Zubek. „Applying inexpensive AI techniques to computer games“. IEEE Intelligent Systems 17, Nr. 4 (Juli 2002): 48–53. http://dx.doi.org/10.1109/mis.2002.1024752.

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11

Fujita, Hiroshi. „AI-based computer-aided diagnosis (AI-CAD): the latest review to read first“. Radiological Physics and Technology 13, Nr. 1 (02.01.2020): 6–19. http://dx.doi.org/10.1007/s12194-019-00552-4.

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12

Lee, Youngseok, und Jungwon Cho. „Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors“. JOIV : International Journal on Informatics Visualization 5, Nr. 2 (20.06.2021): 113. http://dx.doi.org/10.30630/joiv.5.2.552.

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In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.

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Kusumawati, Ririen. „KECERDASAN BUATAN MANUSIA (ARTIFICIAL INTELLIGENCE); TEKNOLOGI IMPIAN MASA DEPAN“. ULUL ALBAB Jurnal Studi Islam 9, Nr. 2 (26.12.2018): 257–74. http://dx.doi.org/10.18860/ua.v9i2.6218.

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The computer technology has incredibly increased. Computer software and hardware compete to meet the customer's needs. The research intends to spread the knowledge of information technology, specifically, on the artificial intelligence. The concept of artificial intelligence is adopting and imitating human form, character, and habit which to be implemented on the computer. Using natural approach, the research aims to investigate whether artificial intelligence (AI) will produce the duplication of God's creation. Another important reason of other reseaches on AI is to create a computer which is smart and able to understand human brain working system. Hence, AI has been designed into more practical with faster CPU, cheaper mass memory, and sophisticated software tool. The concept of integrating AI science or collaborative art among sub-fields of technology will stimulate and lead to further AI researches, and it will be an interesting topic for AI researchers for developing AI technology in the future.

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Cao, Zehong. „A review of artificial intelligence for EEG‐based brain−computer interfaces and applications“. Brain Science Advances 6, Nr. 3 (September 2020): 162–70. http://dx.doi.org/10.26599/bsa.2020.9050017.

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The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.

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15

Ziegel, Eric R., und D. J. Hand. „AI and Computer Power: The Impact of Statistics“. Technometrics 37, Nr. 2 (Mai 1995): 241. http://dx.doi.org/10.2307/1269649.

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16

Laird, John E. „Research in human-level AI using computer games“. Communications of the ACM 45, Nr. 1 (Januar 2002): 32–35. http://dx.doi.org/10.1145/502269.502290.

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17

Wah, B., und G. J. Li. „Survey on special purpose computer architectures for AI“. ACM SIGART Bulletin, Nr. 96 (April 1986): 28–46. http://dx.doi.org/10.1145/15715.15718.

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18

Salomon, Gavriel. „AI in Reverse: Computer Tools That Turn Cognitive“. Journal of Educational Computing Research 4, Nr. 2 (Mai 1988): 123–39. http://dx.doi.org/10.2190/4lu7-vw23-egb1-aw5g.

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This article posits artificial intelligence (AI), as applied in instruction, is a more efficient means to old ends and that newer ends can be thought of. In particular, it is argued on the basis of some past research and on the basis of a Vygotskian view, that intelligent computer tools can not only simulate human cognition but, given specific conditions, humans can simulate computer's intelligence. That is, learners can internalize computers' intelligent tools and use them as cognitive ones. Internalization is discussed in terms of cognitive reconstruction of cultural artifacts of particular characteristics — tool-like nature, relative novelty of function, compatibility with learners' schemata, and most importantly — explicitness of operation. The article also discusses learning conditions that might facilitate the process of tool internalization, particularly “high road” learning whereby learners are intentionally mindful of a tool's mode of operation and logic. A preliminary study is briefly described showing that children are capable of internalizing the metacognitive guidance provided by a semi-intelligent Reading Aid and that they show evidence of internalizing the tool's “intelligence” thereby manifesting improved reading comprehension as well as improved essay writing. Four fundamental questions are raised for future research as well as questions of ethics.

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Chen, Lizhong, Drew Penney und Daniel Jiménez. „AI for Computer Architecture: Principles, Practice, and Prospects“. Synthesis Lectures on Computer Architecture 15, Nr. 5 (06.11.2020): 1–142. http://dx.doi.org/10.2200/s01052ed1v01y202009cac055.

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20

Laird, J. E. „Using a computer game to develop advanced AI“. Computer 34, Nr. 7 (Juli 2001): 70–75. http://dx.doi.org/10.1109/2.933506.

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21

Buarque, Bernardo S., Ronald B. Davies, Ryan M. Hynes und Dieter F. Kogler. „OK Computer: the creation and integration of AI in Europe“. Cambridge Journal of Regions, Economy and Society 13, Nr. 1 (01.02.2020): 175–92. http://dx.doi.org/10.1093/cjres/rsz023.

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Abstract This article investigates the creation and integration of artificial intelligence (AI) patents in Europe. We create a panel of AI patents over time, mapping them into regions at the NUTS2 level. We then proceed by examining how AI is integrated into the knowledge space of each region. In particular, we find that those regions where AI is most embedded into the innovation landscape are also those where the number of AI patents is largest. This suggests that, to increase AI innovation, it may be necessary to integrate it with industrial development, a feature central to many recent AI-promoting policies.

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Smith, Adam M., und Daniel Shapiro. „Teaching Game AI as an Undergraduate Course in Computational Media“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 09 (03.04.2020): 13404–11. http://dx.doi.org/10.1609/aaai.v34i09.7064.

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We need to teach AI to students in and outside of traditional computer science degree programs, including those designer-engineer hybrid students who will design and implement games or engage in technical games research later. The need to rethink AI curriculum is pressing in a design education context because AI powers many emerging practical techniques such as drama management, procedural content generation, player modeling, and machine playtesting. In this paper, we describe a 5-year experimental effort to teach a Game AI course structured around a broad and expanding set of roles AI can play in game design (e.g., Adversary and Actor, as well as Design Assistant and Storyteller). This course sets up computer science and computer game design students to transform practices in the game industry as well as create new forms of media that were previously unreachable. Our students gained mastery over the relevant techniques and further demonstrated (via novel prototype systems) many new roles for AI along the way.

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Lee, Linda W., Amir Dabirian, Ian P. McCarthy und Jan Kietzmann. „Making sense of text: artificial intelligence-enabled content analysis“. European Journal of Marketing 54, Nr. 3 (24.02.2020): 615–44. http://dx.doi.org/10.1108/ejm-02-2019-0219.

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Purpose The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

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Bahl, Manisha. „Artificial Intelligence: A Primer for Breast Imaging Radiologists“. Journal of Breast Imaging 2, Nr. 4 (19.06.2020): 304–14. http://dx.doi.org/10.1093/jbi/wbaa033.

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Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

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Goel, Ashok K., und David A. Joyner. „Using AI to Teach AI: Lessons from an Online AI Class“. AI Magazine 38, Nr. 2 (01.07.2017): 48–59. http://dx.doi.org/10.1609/aimag.v38i2.2732.

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In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.

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Pudner, A. „DLM — a powerful ai computer for embedded expert systems“. Future Generation Computer Systems 3, Nr. 4 (Dezember 1987): 299–306. http://dx.doi.org/10.1016/0167-739x(87)90034-3.

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Fotta, Michael E. „Applications of Artificial Intelligence to Improving the Intention Stage of the Human-Computer Interaction“. Proceedings of the Human Factors Society Annual Meeting 30, Nr. 2 (September 1986): 175–78. http://dx.doi.org/10.1177/154193128603000216.

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The intention stage of the human-computer interaction is a promising area for the application of artificial intelligence (AI) techniques. At this stage, the computer needs to have information on the intended tasks and actions of the user. At least three AI techniques – scripts, student models, and expert system dialogues – have the potential to help systems gather this information.

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McDougall, Rosalind J. „Computer knows best? The need for value-flexibility in medical AI“. Journal of Medical Ethics 45, Nr. 3 (22.11.2018): 156–60. http://dx.doi.org/10.1136/medethics-2018-105118.

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Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.

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Moussally, C., H. Fron-Chabouis, A. Charrière, L. Maladry und E. Dursun. „Full-mouth Rehabilitation of Hypocalcified-type Amelogenesis Imperfecta With Chairside Computer-aided Design and Computer-aided Manufacturing: A Case Report“. Operative Dentistry 44, Nr. 3 (01.05.2019): E145—E158. http://dx.doi.org/10.2341/17-241-t.

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SUMMARY Background: This case report describes the complete full-mouth treatment of hypocalcified amelogenesis imperfecta (AI) by chairside computer-aided design and computer-aided manufacturing (CAD/CAM). Case summary: After several years of interrupted dental care, a 17-year-old female patient presented with pain and also esthetic and functional discomfort. With loss of enamel and dyschromia affecting all teeth, the diagnosis was hypocalcified AI. Affected tissues were eliminated, gingivectomy with laser was performed, an indented jig was used to record the centric relationship during optical impressions, and 28 full ceramic crowns were created by chairside CAD/CAM in four sessions. The patient reported rapid pain relief and an overall improvement of well-being. Conclusion: AI sequelae can be treated promptly and conservatively with chairside CAD/CAM, obtaining esthetic and functional results.

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Mozgovoy, Maxim, Mike Preuss und Rafael Bidarra. „Team Sports for Game AI Benchmarking Revisited“. International Journal of Computer Games Technology 2021 (30.05.2021): 1–9. http://dx.doi.org/10.1155/2021/5521877.

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Sport games are among the oldest and best established genres of computer games. Sport-inspired environments, such as RoboCup, have been used for AI benchmarking for years. We argue that, in spite of the rise of increasingly more sophisticated game genres, team sport games will remain an important testbed for AI benchmarking due to two primary factors. First, there are several genre-specific challenges for AI systems that are neither present nor emphasized in other types of games, such as team AI and frequent replanning. Second, there are unmistakable nonskill-related goals of AI systems, contributing to player enjoyment, that are most easily observed and addressed within a context of a team sport, such as showing creative and emotional traits. We analyze these factors in detail and outline promising directions for future research for game AI benchmarking, within a team sport context.

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Chanda Halim und Hendri Prasetyo. „Penerapan Artificial Intelligence dalam Computer Aided Instructure(CAI)“. Jurnal Sistem Cerdas 1, Nr. 1 (25.07.2018): 50–57. http://dx.doi.org/10.37396/jsc.v1i1.6.

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Dengan perkembangan teknologi komputer dan informasi, Artificial Intelligence (AI) digunakan dalam teknik pendidikan, dan telah menjadi kecenderungan pengembangan pendidikan komputer. Makalah ini memperkenalkan sistem instruksi dibantu komputer dan aktualitasnya, dan membahas konsepsi, objek penelitian dan ruang lingkup AI, kemudian berurusan dengan instruksi dibantu komputer cerdas berdasarkan kecerdasan buatan, akhirnya mengusulkan beberapa jenis Computer Aided Instructure(CAI).

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Anderson, Toni, William C. Torreggiani, Peter L. Munk und Paul I. Mallinson. „The impact of the introduction of artificial intelligence in radiology and its potential legal implications in the UK and Ireland“. BJR|Open 2, Nr. 1 (November 2020): 20200030. http://dx.doi.org/10.1259/bjro.20200030.

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Artificial intelligence (AI) has been defined as a branch of computer science dealing with the capability and simulation of a machine to imitate intelligent human behaviour. Diagnostic radiology, being a computer-based service, is unsurprisingly at the forefront of the discussion of the use of AI in medicine. There are however differing schools of thought regarding its use; namely, will AI eventually replace the radiologist? Or indeed will it ever be fully capable of replacing radiology as a speciality, but rather be used as an aid to the profession whereby a human’s input will always be required? Furthermore, what will the legal implications of AI in radiology mean to the profession? Who will be liable for missed diagnoses? Is it possible that the introduction of AI to radiology will in fact make the profession busier?

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Shultz, David. „Abrama's End Game“. After Dinner Conversation 2, Nr. 5 (2021): 98–117. http://dx.doi.org/10.5840/adc20212545.

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What does it mean to be alive? Can a computer program be sentient? What would it need to do to prove it? In this work of philosophical short story fiction, Abrama is summoned to the Grand Temple by Sir Gödel. Gödel informs Abrama that he is living in a simulated world (a computer game) created by her people as a place to play in their free time. She also informs Abrama that the game is not as popular as it once was and is scheduled to be permanently turned off. It turns out Gödel is an AI researcher that was given permission to test out her AI by implanting characters like Abrama into the game. Over 100’s of versions, the AI continued to improve, and now the researcher feels an ethical obligation to tell her creations their world is coming to an end. Abrama, using this new information, organizes the AI characters in the game and starts trading virtual goods for real-life services from computer hackers that play the game. The computer hackers create computer code and sell it to Abrama. If triggered, or if the game is turned off, the code would expose top secret information to the general public. A bargain is struck, the game will continue on a closed world for the AI characters and, in exchange, the sensitive information will never be made public.

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Van Teijlingen, Alexander, Tell Tuttle, Hamid Bouchachia, Brijesh Sathian und Edwin Van Teijlingen. „Artificial Intelligence and Health in Nepal“. Nepal Journal of Epidemiology 10, Nr. 3 (30.09.2020): 915–18. http://dx.doi.org/10.3126/nje.v10i3.31649.

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The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data. This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal’s health and education systems to track such developments and apply them locally. Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions. This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI.

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Cohen, Paul. „Harold Cohen and AARON“. AI Magazine 37, Nr. 4 (17.01.2017): 63–66. http://dx.doi.org/10.1609/aimag.v37i4.2695.

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Harold Cohen (1928–2016) was a computer art pioneer and the developer of AARON, one of the most creative AI programs in daily use. This memoriam, written by his son (and AI scientist) Paul, describes Harold Cohen's work and contributions to the AI and art communities.

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Winter, Stephan, Timothy Baldwin, Martin Tomko, Jochen Renz, Werner Kuhn und Maria Vasardani. „Spatial concepts in the conversation with a computer“. Communications of the ACM 64, Nr. 7 (Juli 2021): 82–88. http://dx.doi.org/10.1145/3440752.

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Hasegawa, Ken, Narutoshi Tanaka, Ryuji Emoto, Yusuke Sugihara, Ardta Ngonphachanh, Junko Ichino und Tomonori Hashiyama. „Action Selection for Game Play Agents Using Genetic Algorithms in Platform Game Computational Intelligence Competitions“. Journal of Advanced Computational Intelligence and Intelligent Informatics 17, Nr. 2 (20.03.2013): 201–7. http://dx.doi.org/10.20965/jaciii.2013.p0201.

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The application of computational intelligence (CI) and artificial intelligence (AI) to games has been attempted as a typical implementation of intelligent processing on computers. Intelligence in this sense is understood as the ability to search for the best solution efficiently among multiple options, specifically in AI playing board games such as chess. As the processing ability of computers increases, CI/AI systems are outperforming humans in finding potential solutions from a tremendous number of options within a short timeframe. These days, computer games are widely prevalent. CI/AI applications in computer games are focused on animating non-player characters (NPCs), whereas CI/AI applications in the scientific fields are focused on modeling intelligent human activities. The field of computer games faces many issues, such as dealing with dynamic environments that change quickly and processing images at higher resolutions and complexity. The use of computer games as a benchmark for CI/AI technologies has been attempted, and competitions involving various kinds of games have been held to encourage innovation in the field. In this paper, we describe a learning agent that participated in a platform game CI competition held in conjunction with Fuzzy System Symposium (FSS 2012). The approach adopted in this paper is a basic method based on conventional methods. The authors hope that this presentation of our development processes would encourage many researchers to participate in competitions and that it would contribute to progress in the field.

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Boden, Margaret A. „Computer Models of Creativity“. AI Magazine 30, Nr. 3 (07.07.2009): 23. http://dx.doi.org/10.1609/aimag.v30i3.2254.

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Creativity isn’t magical. It’s an aspect of normal human intelligence, not a special faculty granted to a tiny elite. There are three forms: combinational, exploratory, and transformational. All three can be modeled by AI—in some cases, with impressive results. AI techniques underlie various types of computer art. Whether computers could “really” be creative isn’t a scientific question but a philosophical one, to which there’s no clear answer. But we do have the beginnings of a scientific understanding of creativity.

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KUNZ, JOHN. „From PUFF to integrated concurrent engineering: A personal evolution“. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, Nr. 1 (Januar 2007): 19–22. http://dx.doi.org/10.1017/s0890060407070096.

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Artificial intelligence (AI) emerged from the 1956 Dartmouth Conference. Twenty-one years later, my colleagues and I started daily operational use of what we think became the first application of AI to be used in practice: the PUFF pulmonary function system. We later described the design and initial performance of that system (Aikins et al., 1983; Snow et al., 1998). Today, easily recognizable descendants of that first “expert system” run on commercial products found in medical offices around the world (http://www.medgraphics.com/datasheet_pconsult.html), as do many other AI applications. My research now focuses on integrated concurrent engineering (ICE), a computer and AI-enabled multiparticipant engineering design method that is extremely rapid and effective (Garcia et al., 2004). This brief note compares the early PUFF, the current ICE work, and the modern AI view of neurobiological systems. This comparison shows the dramatic and surprising changes in AI methods in the past few decades and suggests research opportunities for the future. The comparison identifies the continuing crucial role of symbolic representation and reasoning and the dramatic generalization of the context in which those classical AI methods work. It suggests surprising parallels between animal neuroprocesses and the multihuman and multicomputer agent collaborative ICE environment. Finally, it identifies some of the findings and lessons of the intervening years, fundamentally the move to model-based multidiscipline, multimethod, multiagent systems in which AI methods are tightly integrated with theoretically founded engineering models and analytical methods implemented as multiagent human and computer systems that include databases, numeric algorithms, graphics, human–computer interaction, and networking.

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Khemasuwan, Danai, Jeffrey S. Sorensen und Henri G. Colt. „Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19“. European Respiratory Review 29, Nr. 157 (30.09.2020): 200181. http://dx.doi.org/10.1183/16000617.0181-2020.

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Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

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Hancock, Jeffrey T., Mor Naaman und Karen Levy. „AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations“. Journal of Computer-Mediated Communication 25, Nr. 1 (Januar 2020): 89–100. http://dx.doi.org/10.1093/jcmc/zmz022.

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Abstract We define Artificial Intelligence-Mediated Communication (AI-MC) as interpersonal communication in which an intelligent agent operates on behalf of a communicator by modifying, augmenting, or generating messages to accomplish communication goals. The recent advent of AI-MC raises new questions about how technology may shape human communication and requires re-evaluation – and potentially expansion – of many of Computer-Mediated Communication’s (CMC) key theories, frameworks, and findings. A research agenda around AI-MC should consider the design of these technologies and the psychological, linguistic, relational, policy and ethical implications of introducing AI into human–human communication. This article aims to articulate such an agenda.

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Oohashi, Yukinori. „Special Issue: Computer Applications. AI Technology Development and Daido's Approach.“ DENKI-SEIKO[ELECTRIC FURNACE STEEL] 65, Nr. 3 (1994): 267–72. http://dx.doi.org/10.4262/denkiseiko.65.267.

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Seo, Jinseok. „HSM(Hierarchical State Machine) based LOD AI for Computer GamesS“. Journal of Digital Contents Society 14, Nr. 2 (30.06.2013): 143–49. http://dx.doi.org/10.9728/dcs.2013.14.2.143.

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How, Meng-Leong, Sin-Mei Cheah, Aik Cheow Khor und Yong Jiet Chan. „Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach“. Big Data and Cognitive Computing 4, Nr. 2 (27.04.2020): 8. http://dx.doi.org/10.3390/bdcc4020008.

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According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict their intentions toward the financial products being offered. This approach contributes to the literature by illustrating how AI for social good can also be accessible for people who are not well-versed in computer science. A rudimentary AI-based predictive modeling approach that does not require programming skills will be illustrated in this paper. In these AI-generated multi-criteria optimizations, analysts in FSPs can simulate scenarios to better understand their prospective customers. In conjunction with the usage of AI, this paper also suggests how AI-Thinking could be utilized as a cognitive scaffold for educing (drawing out) actionable insights to advance financial inclusion.

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HOW, Meng-Leong, und Wei Loong David HUNG. „Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations“. Education Sciences 9, Nr. 2 (20.05.2019): 110. http://dx.doi.org/10.3390/educsci9020110.

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Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.

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Lin, Pei-Chun. „Character model identification and interactive AI simulation application“. Impact 2020, Nr. 4 (13.10.2020): 70–72. http://dx.doi.org/10.21820/23987073.2020.4.70.

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AI is the theory and development of computer systems that can operate and perform functions which would normally require human input, such as visual perception, speech recognition, and translating one language into another. Dr Pei-Chun Lin is a researcher based in the Department of Information Engineering and Computer Science at Feng Chia University in Taiwan. She is collaborating with other academics around the world to develop a robot that incorporates AI and will act in a way that suggests it is a thinking and feeling robot which humans can relate to. The character model identification is especially important for creating an AI robot. The team applied data analysis tools to confirm the model and incorporate fuzzy logic, which holds significant promise for the field of AI application. Lin believes that there is a great deal of potential for AI robots to improve the lives of people around the world.

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Lin, Pei-Chun. „Character model identification and interactive AI simulation application“. Impact 2021, Nr. 1 (05.02.2021): 18–20. http://dx.doi.org/10.21820/23987073.2021.1.18.

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AI is the theory and development of computer systems that can operate and perform functions which would normally require human input, such as visual perception, speech recognition, and translating one language into another. Dr Pei-Chun Lin is a researcher based in the Department of Information Engineering and Computer Science at Feng Chia University in Taiwan. She is collaborating with other academics around the world to develop a robot that incorporates AI and will act in a way that suggests it is a thinking and feeling robot which humans can relate to. The character model identification is especially important for creating an AI robot. The team applied data analysis tools to confirm the model and incorporate fuzzy logic, which holds significant promise for the field of AI application. Lin believes that there is a great deal of potential for AI robots to improve the lives of people around the world.

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Ao, Chunyan, Shunshan Jin, Hui Ding, Quan Zou und Liang Yu. „Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis“. Current Pharmaceutical Design 26, Nr. 26 (11.08.2020): 3069–75. http://dx.doi.org/10.2174/1381612826666200331091156.

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With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.

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Vorm, E. S., und Prithviraj Dasgupta. „Computer-Centered Humans: Why Human-AI Interaction Research Will Be Critical to Successful AI Integration in the DoD“. IEEE Intelligent Systems 35, Nr. 4 (01.07.2020): 112–16. http://dx.doi.org/10.1109/mis.2020.3013133.

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Galiautdinov, Rinat. „Microservice-Oriented Architecture in Distributed Artificial Intelligence Systems and the Language of AI in Bio-Neural Systems“. International Journal of Applied Research in Bioinformatics 10, Nr. 2 (Juli 2020): 18–27. http://dx.doi.org/10.4018/ijarb.2020070103.

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This article describes the views on the architecture of distributed AI systems based on the simulated bio-neurons representing the basis for the bio-neural circuits, which represent distributed AI subsystems and serve as microservices for the AI client-side systems. The article also describes the interface and the demands to the protocol of communication with the distributed subsystems of the AI, the ways of tuning the synaptic contacts in the brand new neural circuits, which represent the distributed AI systems, and finally, the new approach to communication with such the systems based on the new computer language, which will be used in construction and tuning of such the AI systems.

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