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Journal articles on the topic 'Artificial Intelligence in Physics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Jalilova, S., and G. Musayeva. "ARTIFICIAL INTELLIGENCE IN PHYSICS TEACHING." Sciences of Europe, no. 157 (January 27, 2025): 46–49. https://doi.org/10.5281/zenodo.14744941.

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This paper presents a comprehensive literature review on the application of artificial intelligence in physics teaching. The purpose of the study is to explore the fundamental concepts of AI, its various applications in physics teaching, and the benefits and challenges associated with its implementation. Through a systematic search of academic databases, a collection of relevant research articles, journals, conference proceedings and books on the use of AI in physics education was assembled. The selected studies were analyzed and synthesized to develop a coherent framework for understanding th

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2

Pattnaik, P. C., G. Fletcher, and J. L. Fry. "Artificial intelligence programming in physics." European Journal of Physics 7, no. 1 (1986): 25–28. http://dx.doi.org/10.1088/0143-0807/7/1/005.

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Chougule, Arun, and Gourav Kumar Jain. "Artificial Intelligence in Medical Physics." Journal of Medical Physics 49, no. 3 (2024): 489–91. http://dx.doi.org/10.4103/jmp.jmp_102_24.

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4

Trout, Joseph J., and Lauren Winterbottom. "Artificial intelligence and undergraduate physics education." Physics Education 60, no. 1 (2024): 015024. https://doi.org/10.1088/1361-6552/ad98de.

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Abstract The latest advances in science and technology have resulted in great advances in artificial intelligence (AI), including the creation of chatbots. Chatbots simulate human conversation and allow humans to ask questions and receive answers based on a large volume of electronically stored information. Faculties of universities around the world are trying to come to grips with the availability of AI tools, such as chatbots, and are debating the ethical and moral questions surrounding the use of AI in education. This paper presents the results of a study which intended to answer three rese

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Sperling, Alissa, and James Lincoln. "Artificial intelligence and high school physics." Physics Teacher 62, no. 4 (2024): 314–15. http://dx.doi.org/10.1119/5.0202994.

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6

Jing, Yumei, and Fangping Ouyang. "The Role of Integrating Artificial Intelligence and Virtual Simulation Technologies in Physics Teaching." Advances in Education, Humanities and Social Science Research 6, no. 1 (2023): 572. http://dx.doi.org/10.56028/aehssr.6.1.572.2023.

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With the continuous progress of information technology, the integration of artificial intelligence and virtual simulation technologies into the teaching system is of great significance to promote the informatization reform of physics teaching and the cultivation of innovative talents. This paper first introduces the characteristics of the combination of artificial intelligence and virtual simulation technologies, then comprehensively analyzes the positive effects of the integration of artificial intelligence and virtual simulation technologies on physics teaching, and finally puts forward some

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Commissariat, Tushna. "Artificial intelligence." Physics World 34, no. 5 (2021): 17. http://dx.doi.org/10.1088/2058-7058/34/05/24.

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8

Oh, Sang Hoon. "Nobel Prize and Artificial Intelligence." Liberal Arts Innovation Center 17 (March 31, 2025): 1061–81. https://doi.org/10.54698/kl.2025.17.1061.

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The Nobel Prize in science has traditionally been awarded to researchers who have made groundbreaking discoveries or inventions that significantly contribute to human progress in the field of pure science. However, in 2024, the Nobel Prize in Physics was awarded not to a researcher in pure physics but to a researcher in the field of artificial intelligence. Similarly, the Nobel Prize in Chemistry was awarded not to a pure chemistry researcher but to a scientist studying protein structures using artificial intelligence or computational techniques. This paper examines the research achievements o

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Allen, Machel M. A. "Physics-Based Transfer Learning and Artificial Intelligence." International Journal of Advanced Engineering Research and Science 7, no. 8 (2020): 266–74. http://dx.doi.org/10.22161/ijaers.78.28.

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Zanca, F., M. Avanzo, N. Colgan, et al. "Focus issue: Artificial intelligence in medical physics." Physica Medica 83 (March 2021): 287–91. http://dx.doi.org/10.1016/j.ejmp.2021.05.008.

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11

Cho, Adrian. "Physics award honors pioneers in artificial intelligence." Science 386, no. 6718 (2024): 135. http://dx.doi.org/10.1126/science.adt7205.

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12

Anbu Principal, K. "Enhancing Physics Education through Artificial Intelligence Tools." Scientiarum: A multidisciplinary journal 1, no. 3 (2025): 25–32. https://doi.org/10.54646/sapars.2025.13.

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The integration of Artificial Intelligence (AI) in education has opened new avenues for enhancing the teaching-learning process, particularly in subjects like physics, which often involve complex concepts and abstract reasoning. This research explores the application of AI tools in the domain of physics education and evaluates their effectiveness in improving student engagement, conceptual understanding, and performance outcomes. As traditional teaching methods frequently struggle to meet the diverse needs of 21st-century learners, AI offers promising alternatives through adaptive learning pla

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13

HEMKER, ANDREAS, and KARL-HEINZ BECKS. "EVOLUTION ENGINES AND ARTIFICIAL INTELLIGENCE." International Journal of Modern Physics C 05, no. 01 (1994): 15–36. http://dx.doi.org/10.1142/s0129183194000039.

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In the last years artificial intelligence has achieved great successes, mainly in the field of expert systems and neural networks. Nevertheless the road to truly intelligent systems is still obscured. Artificial intelligence systems with a broad range of cognitive abilities are not within sight. The limited competence of such systems (brittleness) is identified as a consequence of the top-down design process. The evolution principle of nature on the other hand shows an alternative and elegant way to build intelligent systems. We propose to take an evolution engine as the driving force for the

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14

Dong, Changming, Guangjun Xu, Guoqing Han, Brandon J. Bethel, Wenhong Xie, and Shuyi Zhou. "Recent Developments in Artificial Intelligence in Oceanography." Ocean-Land-Atmosphere Research 2022 (November 3, 2022): 1–26. http://dx.doi.org/10.34133/2022/9870950.

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With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surge

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15

Mahligawati, F., E. Allanas, M. H. Butarbutar, and N. A. N. Nordin. "Artificial intelligence in Physics Education: a comprehensive literature review." Journal of Physics: Conference Series 2596, no. 1 (2023): 012080. http://dx.doi.org/10.1088/1742-6596/2596/1/012080.

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Abstract This paper presents a comprehensive literature review on the application of artificial intelligence (AI) in physics education. The study aims to explore the fundamental concepts of AI, its diverse applications in physics learning, and the benefits and challenges associated with its implementation. Through a systematic search of academic databases, a collection of relevant research articles, journals, conference papers, and books related to AI in physics education was obtained. The selected studies were analyzed and synthesized to develop a coherent framework for understanding the vari

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Terzopoulos, Demetri. "Artificial Animals and Humans: From Physics to Intelligence." Computer Graphics Forum 21, no. 3 (2002): xvii. http://dx.doi.org/10.1111/1467-8659.t01-3-00577.

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17

Antonacci, M. A., and M. A. Maize. "Physics writing in the era of artificial intelligence." American Journal of Physics 91, no. 8 (2023): 575. http://dx.doi.org/10.1119/5.0159871.

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18

BUSOL, O. "The potential danger of artificial intelligence." INFORMATION AND LAW, no. 2(14) (August 19, 2015): 121–28. http://dx.doi.org/10.37750/2616-6798.2015.2(14).272708.

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In the article opinions of famous scientists with world names and the other scientists in the field of philosophy, cybernetics, futurology, cosmology, physics about danger of creation of artificial intelligence for mankind in future are considered. Lack of new approaches to creation of the monitoring system, first of all, of ethical character, over artificial intelligence is accented.

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19

Raikov, A. N. "Weak vs strong artificial intelligence." Informatization and communication, no. 1 (March 31, 2020): 81–88. http://dx.doi.org/10.34219/2078-8320-2020-11-1-81-88.

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It is proposed to distinguish between the traditional, so-called, weak, artificial intelligence (AI) and strong artificial intelligence, or artificial general intelligence (AGI). The latter is not considered as the evolutionary stage of the development of AI, but something else related to the construction of an informal and non-causal phenomenological space, as a kind of opposite to AI. AGI is determined by inverse problems solving on topologies, using analogies of quantum physics and optics, connecting aspects of relativistic theory. It is noted that the AGI continual power is tens of orders

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20

Gontina, Wulan, and Rayandra Asyhar. "DAMPAK ARTIFICIAL INTELLIGENCE TERHADAP PEMBELAJARAN IPA/FISIKA DI SEKOLAH." SILAMPARI JURNAL PENDIDIKAN ILMU FISIKA 5, no. 2 (2023): 238–50. http://dx.doi.org/10.31540/sjpif.v5i2.2609.

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The evolutionary era of Industry 4.0. and the era of society 5.0. make changes to learning activities at school. The use of science/physics learning media, methods and models based on Artificial Intelligence is able to visualize science/physics learning material which is mostly abstract in nature. The teacher's role in making science/physics learning activities effective, interactive and communicative is very large. For this reason, teachers need to increase their insight and upgrade their knowledge of Artificial Intelligence-based digital technology so that learning objectives can be achieved

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21

Orucov, Vidadi. "DIDACTICS OF ARTIFICIAL INTELLIGENCE IN PHYSICS EDUCATION: NEW APPROACHE." Scientific Works 92, no. 3 (2025): 122–25. https://doi.org/10.69682/arti.2025.92(3).122-125.

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The article explores the role of artificial intelligence (AI) in physics education and the innovations it can bring to the teaching process. It highlights the alignment of physics education with 21st-century skills, the evolving pedagogical roles of teachers, and the promotion of interactive, research-based learning through technology. Future perspectives on AI in education and new directions for research in this field are also discussed.

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22

Prahani, Binar K., Hanandita V. Saphira, Firmanul C. Wibowo, and Muhammad A. H. Bunyamin. "Mapping research on integrating artificial intelligence into physics learning." Perpsectives of science and education 71, no. 5 (2024): 305–17. http://dx.doi.org/10.32744/pse.2024.5.18.

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The problem and the aim of the study. The incorporation of AI in physics education promises to create a more personalized, streamlined, and efficient learning environment that caters to the varied needs of both students and educators. This study aims to provide insights into the evolving research landscape, identify key contributors and emerging trends, and help close gaps in current knowledge. This research is a descriptive analysis using bibliometrics with primary source database used is Scopus. Research methods. This research is a descriptive analysis using bibliometrics with primary source

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23

Ripley, Stephen B. "Robot Consciousness: Physics and Metaphysics Here and Abroad." Journal of Big History 7, no. 4 (2024): 46–72. http://dx.doi.org/10.22339/jbh.v7i4.7403.

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Interest has been renewed in the study of consciousness, both theoretical and applied, following developments in 20th and early 21st century logic, metamathematics, computer science, and the brain sciences. In this evolving historical narrative, I explore several theoretical questions about the types of artificial intelligence and offer several conjectures about how they affect possible future developments in this exceptionally transformative field of research. I also address the practical significance of the advances in artificial intelligence in view of the cautions issued by prominent scien

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24

Pan, Ziyu. "The Application of Artificial Intelligence in Game." Highlights in Science, Engineering and Technology 76 (December 31, 2023): 462–66. http://dx.doi.org/10.54097/cyd0yy78.

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AI has been extensively applied in various aspects of game development over the past few decades. One critical application is the use of AI algorithms to simulate intelligent decision-making processes for non-player character behavior. Another application is procedural content generation, where AI algorithms generate game content such as level designs or missions to enhance gameplay diversity. This paper aims to explore the application of AI in game development by analyzing specific cases and discussing the current state of the field. The paper also focuses on related topics such as motion and

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25

D., P. Deshmukh, R. Yerawar G., U. Rahangdale V., and L. Gadre A. "AI in Physics: A Concept of Modern Era." International Journal of Advance and Applied Research S6, no. 18 (2025): 771–75. https://doi.org/10.5281/zenodo.15266565.

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<em>The exploration of physics is shaping a new approach to artificial intelligence (AI) discovery. By leveraging data, knowledge, prior information, and fundamental laws, physics has provided valuable insights into the AI paradigm across various scales of matter, energy, and space-time. Simultaneously, AI draws from and contributes to the principles of physics to enhance its own advancement. This article will explore the relationship between AI and key areas of physics, including classical mechanics, electromagnetism, statistical physics, and quantum mechanics. Also, Artificial intelligence (

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26

姜, 涛. "Intelligent Physics Experiment Teaching Based on Artificial Intelligence Technology." Creative Education Studies 12, no. 06 (2024): 540–46. http://dx.doi.org/10.12677/ces.2024.126420.

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27

Müller, U. "Artificial intelligence—applications in high energy and nuclear physics." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (2003): 811–14. http://dx.doi.org/10.1016/s0168-9002(03)00607-7.

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28

Hogg, Tad, and B. A. Huberman. "Artificial intelligence and large scale computation: A physics perspective." Physics Reports 156, no. 5 (1987): 227–310. http://dx.doi.org/10.1016/0370-1573(87)90096-2.

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29

姜, 涛. "The Application of Artificial Intelligence in College Physics Teaching." Creative Education Studies 12, no. 05 (2024): 423–30. http://dx.doi.org/10.12677/ces.2024.125304.

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30

Sommer, Bernhard, and Galo Moncayo. "Machine Learning and Artificial Intelligence in Building Physics Education." Bauphysik 45, no. 6 (2023): 305–14. http://dx.doi.org/10.1002/bapi.202300020.

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AbstractThis series presents articles on the use of computer programmes in university teaching of building physics and building technology for architects and civil engineers. This contribution highlights the utilization of machine learning and artificial intelligence (AI) in building physics education, drawing upon the expertise of the Energy Design department. The research conducted within the department encompasses the development of custom‐made digital tools, prototyping, and integration of sensors into energy concepts and the implementation of these concepts into building elements. It show

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31

Mahecha-Gómez, Jorge E. "ARTIFICIAL INTELLIGENCE WITH NEURAL NETWORKS NOBEL PRIZES IN PHYSICS AND CHEMISTRY 2024." MOMENTO, no. 70 (January 30, 2025): I—XXI. https://doi.org/10.15446/mo.n70.118564.

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John Joseph Hopfield began his career studying excitons in condensed matter physics, but his most important contributions were in the physics of computation and information, including his 1982 work on neural networks. Geoffrey Hinton, known as the “godfather” of artificial intelligence, laid the foundations for deep neural networks and developed the “backpropagation” method in 1986. These advances, along with Hopfield networks and the “Boltzmann machine”, constitute the beginning of artificial intelligence. David Baker is a pioneer in the design and prediction of three-dimensional protein stru

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32

Biró, Tamás Sándor, and Antal Jakovác. "Entropy of Artificial Intelligence." Universe 8, no. 1 (2022): 53. http://dx.doi.org/10.3390/universe8010053.

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We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the ent

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Zha, Jiali. "Artificial Intelligence in Agriculture." Journal of Physics: Conference Series 1693 (December 2020): 012058. http://dx.doi.org/10.1088/1742-6596/1693/1/012058.

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34

LeCun, Yann. "Artificial Intelligence in Scientific Research: Transforming Data Analysis and Discovery." International Journal of Innovative Computer Science and IT Research 1, no. 01 (2025): 1–9. https://doi.org/10.63665/ijicsitr.v1i01.01.

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Artificial Intelligence (AI) has become a transformative tool in scientific research, reshaping traditional methodologies by enabling advanced data analysis, hypothesis testing, and predictive modeling. The integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) has significantly accelerated discoveries in medicine, physics, chemistry, environmental science, and other disciplines. AI-driven technologies allow researchers to process large datasets, identify complex patterns, and generate predictive insights with unprecedented accuracy and speed. These inn

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Liu Che, 刘彻, 马骞 Ma Qian, 李廉林 Li Lianlin, and 崔铁军 Cui Tiejun. "Artificial Intelligence Metamaterials." Acta Optica Sinica 41, no. 8 (2021): 0823004. http://dx.doi.org/10.3788/aos202141.0823004.

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36

Kotsis, Konstantinos T. "Artificial Intelligence and the Scientific Process: A Review of ChatGPT’s Role to Foster Experimental Thinking in Physics Education." European Journal of Contemporary Education and E-Learning 3, no. 3 (2025): 183–98. https://doi.org/10.59324/ejceel.2025.3(3).14.

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This paper offers a thorough examination of the incorporation of artificial intelligence, specifically ChatGPT, in the realm of physics education. This compendium synthesizes diverse literature that explores how artificial intelligence tools enhance experimental thinking, foster inquiry-based learning, and support personalized instructional strategies in the intricate domain of physics. The review emphasizes recorded advantages, such as improved critical thinking and problem-solving abilities, increased student engagement, and a more profound comprehension of abstract physical concepts. The an

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37

Avanzo, Michele, Annalisa Trianni, Francesca Botta, Cinzia Talamonti, Michele Stasi, and Mauro Iori. "Artificial Intelligence and the Medical Physicist: Welcome to the Machine." Applied Sciences 11, no. 4 (2021): 1691. http://dx.doi.org/10.3390/app11041691.

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Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Med

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38

Verbovaya, Olga, Gulmira Talapova, Saida Akimbekova, Zhana Salimbayeva, and Aigul Ordabayeva. "Responsibility for damages done by robots: The European Union experience." Scientific Herald of Uzhhorod University Series Physics 2024, no. 55 (2023): 1792–801. http://dx.doi.org/10.54919/physics/55.2024.179ge2.

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Relevance Today, artificial intelligence is being actively implemented in many important areas of human activity, so any malfunction in the activities of the robot can lead to negative consequences, the liability for which remains a controversial issue.Purpose. The research aims to investigate the grounds, types, and peculiarities of the application of liability for damage caused by a robot within the European Union (EU).Methodology. The following methods were used: analysis and synthesis, induction, deduction, comparison, statistical analysis, formal-legal method and interdisciplinary approac

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39

Yehya, Fouad, Areej ElSayary, Ghadah Al Murshidi, and Ahmed Al Zaabi. "Artificial intelligence integration and teachers’ self-efficacy in physics classrooms." Eurasia Journal of Mathematics, Science and Technology Education 21, no. 8 (2025): em2679. https://doi.org/10.29333/ejmste/16660.

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The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence

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40

Pérez-Vicente, Conrad J. "Nobel prize in physics 2024: ideas that transcend physics." Europhysics News 56, no. 1 (2025): 13–14. https://doi.org/10.1051/epn/2025105.

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The Hopfield model, a recurrent artificial neural network introduced in 1982, has had profound implications in computational neuroscience, synchronization phenomena, and artificial intelligence (AI). In computational neuroscience, it provides a framework for understanding associative memory, attractor dynamics, and error correction in biological neural systems. The model’s energy minimization properties have also been explored in synchronization studies, particularly in coupled oscillatory systems and network stability analysis. In AI, Hopfield networks have influenced optimization methods, co

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Oss, Stefano. "Artificial intelligence at school: please handle with care." Physics Education 58, no. 4 (2023): 046501. http://dx.doi.org/10.1088/1361-6552/acd7c0.

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Abstract Physics teachers at any school level are given a simple but heartfelt hint: artificial intelligence platforms are certainly important and can be useful in many situations; however, they cannot replace natural intelligence when it comes to doing the ‘dirty work’ of gradually learning the study method and mathematical tools.

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Longhi, Sonia, Salvador Ventura, Sandra Macedo-Ribeiro, et al. "When artificial intelligence meets protein research." Open Research Europe 5 (July 15, 2025): 185. https://doi.org/10.12688/openreseurope.20628.1.

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The 2024 Nobel Prizes in Chemistry and Physics mark a watershed moment in the convergence of artificial intelligence (AI) and molecular biology. This article explores how AI, particularly deep learning and neural networks, has revolutionized protein science through breakthroughs in structure prediction and computational design. It highlights the contributions of 2024 Nobel laureates John Hopfield, Geoffrey Hinton, David Baker, Demis Hassabis, and John Jumper, whose foundational work laid the groundwork for AI tools such as AlphaFold. These tools are transforming our understanding of protein fo

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43

El-Adawy, Shams, Isaac Liao, Vedang Lad, Mohamed Abdelhafez, and Peter Dourmashkin. "Streamlining Physics Problem Generation to Support Physics Teachers in Using Generative Artificial Intelligence." Physics Teacher 62, no. 7 (2024): 595–98. http://dx.doi.org/10.1119/5.0201458.

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Arini, Wahyu, Yaspin Yolanda, and Ovilia Putri Utami Gumay. "The Utilization of Artificial Intelligence (AI) in Physics Learning for Physics Education Students." Jurnal Penelitian Pendidikan IPA 11, no. 5 (2025): 882–89. https://doi.org/10.29303/jppipa.v11i5.11552.

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This study aims to examine the effectiveness of utilizing Artificial Intelligence (AI) in physics learning for students in the Physics Education program. The research employed a quasi-experimental design with a one-group pretest-posttest approach. The sample consisted of 24 students selected through purposive sampling. Data were collected using physics understanding tests administered before (pretest) and after (posttest) the implementation of AI-based learning. Data analysis involved paired sample t-tests to determine significant differences between pretest and posttest scores, as well as N-G

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45

Nakata, Rie, Nori Nakata, Pu Ren, et al. "Simulating seismic wavefields using generative artificial intelligence." Leading Edge 44, no. 2 (2025): 123–32. https://doi.org/10.1190/tle44020123.1.

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Simulating realistic seismic wavefields is crucial for a range of seismic tasks, including acquisition designing, imaging, and inversion. Conventional numerical seismic wave simulators are computationally expensive for large 3D models, and discrepancies between simulated and observed waveforms arise from wave equation selection and input physical parameters such as the subsurface elastic models and the source parameters. To address these challenges, we adopt a data-driven artificial intelligence approach and propose a conditional generative modeling (CGM) framework for seismic wave simulation.

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Diaz, Oliver, Gabriele Guidi, Oleksandra Ivashchenko, Niall Colgan, and Federica Zanca. "Artificial intelligence in the medical physics community: An international survey." Physica Medica 81 (January 2021): 141–46. http://dx.doi.org/10.1016/j.ejmp.2020.11.037.

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47

Andersson, Jonas, Tufve Nyholm, Crister Ceberg, et al. "Artificial intelligence and the medical physics profession - A Swedish perspective." Physica Medica 88 (August 2021): 218–25. http://dx.doi.org/10.1016/j.ejmp.2021.07.009.

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48

Mansour, Bavely Mansour Saleh, Beshoy Mahrous Morsy Bekhet, Mina Emad Alfy Nagy, Mohammed Magdy Ismael Mohammed, and Osama Ashraf Youssef. "Artificial intelligence technology as a method to simplify physics learning." البحوث التطبيقية في العلوم والانسانيات 1, no. 1 (2024): 181–206. http://dx.doi.org/10.21608/aash.2024.376869.

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JHO*, Hunkook. "Discussion for how to Apply Artificial Intelligence to Physics Education." New Physics: Sae Mulli 70, no. 11 (2020): 974–84. http://dx.doi.org/10.3938/npsm.70.974.

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Christy, Lowell F. Jr. "What is Intelligence? The Looming Philosophical Question behind Artificial Intelligence." Voprosy Filosofii, no. 10 (2022): 208–12. http://dx.doi.org/10.21146/0042-8744-2022-10-208-212.

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The Looming Problem Behind Artificial Intelligence (AI) and Natural Science is buried deep in how Occidental or Western Civilization has chosen to define its peculiar ideas of Truth (Epistemology) & Ontology (Metaphysics of us / universe). This paper identify how this Scientific Materialism and its physics paradigm has become inadequate and destructive to support living systems. AI is based on the ideas of first-order cybernetics, sufficient in creating “smart thinking appliances”. Second- and third-order cybernetics, requiring intelligence beyond a given context or mission driven task,

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