Аннотації

Вплив штучного інтелекту, комп'ютерного зору та машинного навчання на життя людини

Автор(и):
Касянчук А. В., Гоц В. В., Попович Н. Л., Хроленко В. М.
Автор(и) (англ)
Kasianchuk A., Popovych N., Gots V., Khrolenko V.
Дата публікації:

30.11.2023

Анотація (укр):

Розглянуто майбутнє штучного інтелекту (ШІ), комп'ютерного зору та машинного навчання, а також те, як ці технології можуть вплинути на наше щоденне життя. Ці технології мають потужність впливати на спосіб, яким ми живемо і взаємодіємо з ними, відкриваючи нові можливості в таких галузях, як охорона здоров'я, транспорт та освіта. Проте майбутнє ШІ, комп'ютерного зору та машинного навчання також викликає етичні і соціальні питання. Розвиток цих технологій має ґрунтуватися на етичних принципах, щоб забезпечити їх використання на благо людства, а не на шкоду. Загалом ця тема підкреслює величезний потенціал штучного інтелекту, комп'ютерного зору та машинного навчання, а також можливість визначати наше майбутнє. Продовжуючи розширювати межі технологій, важливо враховувати етичні наслідки і забезпечувати відповідальне використання цих технологій на благо всіх.

Анотація (рус):

Анотація (англ):

Based on the hypothesis of the existence of wave long-range action and its role in the development of self-ordering processes, expressed by us in previous works, we carried out a methodological analysis of the applicability of wave representations in systems of identical particles. The main attention in the analysis is paid to the practical application of de Broglie waves in systems of interacting particles. For this purpose, Bohr's theory of the hydrogen atom has been revised and inconsistencies that contradict modern ideas have been corrected. Two conclusions are made: wave representations of particles are of a material nature; the de Broglie wavelength should be determined in terms of the relative momentum of the interacting particles . Based on the materiality of wave representations, the features of the long-wave interaction of particles are determined. It is emphasized that in many manifestations this interaction has a resonant character. This interaction is the foundation for the universal mechanism for the development of self-ordering processes in systems of identical particles. The paper presents an algorithm for the emergence and functioning of a universal mechanism. The condition for the emergence is always any withdrawal of the system from the state of isotropic chaos and the formation of an initial subgroup of particles with the same magnitude and direction of momenta. The most common cause leading to the fulfillment of the condition is monoenergization of the spectrum of particles in the system. The causative agent of the mechanism is direct collisions of particles, in which the probability of long-wavelength representations is the highest. The development of the mechanism is limited only by the fulfillment of the percolation condition – overcoming the percolation threshold. The final stage of the mechanism is the formation of a superfluid component – a kind of macroparticle that does not interact with walls and other molecules. A characteristic wave feature of such a macroparticle is its coherence. Several final stages in the development of self-ordering processes for typical special phenomena in systems of identical particles are analyzed.

Література:

  1. Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Moreno-García, C. F., Jayne, C., Mostafa Kamal Sarker, M. (2022). Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Art. Int. Surg, 2, 24–45. http://dx.doi.org/10.20517/ais.2021.15.
  2. Report of IBM Computer systems. https://www.ibm.com/topics/computer-vision.
  3. Yann, LeCun, Yoshua, Bengio, Geoffrey, Hinton. (2015). Deep Learning. University of Montreal.
  4. Kaiming He, et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.
  5. Carmigniami, et al. (2011). Augmented Reality: An Overview Handbook of Augmented Reality, Springer, New York, pp. 3–46.
  6. O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., et al. (2019). Deep learning vs. traditional computer vision K. Arai (Ed.). Science and Information Conference, Springer, pp. 128–144.
  7. Jordan, M. I., Mitchell, T. M. (2015). Machine learning: trends, perspectives, and prospects. Science, 349, 255–260.
  8. Zhang, X.-D. (2020). Machine learning. A Matrix Algebra Approach to Artificial Intelligence, Springer, 223–440.
  9. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT press.
  10. Deng, L. Yu. D. (2014). Deep learning: methods and applications. FNT. Signal Processing, 7, 197–387.
  11. Bostrom, Nick, Yudkowsky, Eliezer. (2011). The ethics  of artificial intelligence. Cambridge University Press.
  12. Amodei, Dario, Olah, Chris, Steinhardt, Jacob, Christiano, Paul, Schulman, John, Mané, Dan. (2016). Concrete Problems in AI Safety. Cornell University.
  13. Artificial Intelligence and Life in 2030. (2016). Stanford University's One Hundred Year Study on Artificial Intelligence.

 

References:

  1. Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Moreno-García, C. F., Jayne, C., Mostafa Kamal Sarker, M. (2022). Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Art. Int. Surg, 2, 24–45. http://dx.doi.org/10.20517/ais.2021.15.
  2. Report of IBM Computer systems. https://www.ibm.com/topics/computer-vision.
  3. Yann, LeCun, Yoshua, Bengio, Geoffrey, Hinton. (2015). Deep Learning. University of Montreal.
  4. Kaiming He, et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.
  5. Carmigniami, et al. (2011). Augmented Reality: An Overview Handbook of Augmented Reality, Springer, New York, pp. 3–46.
  6. O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., et al. (2019). Deep learning vs. traditional computer vision K. Arai (Ed.). Science and Information Conference, Springer, pp. 128–144.
  7. Jordan, M. I., Mitchell, T. M. (2015). Machine learning: trends, perspectives, and prospects. Science, 349, 255–260.
  8. Zhang, X.-D. (2020). Machine learning. A Matrix Algebra Approach to Artificial Intelligence, Springer, 223–440.
  9. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT press.
  10. Deng, L. Yu. D. (2014). Deep learning: methods and applications. FNT. Signal Processing, 7, 197–387.
  11. Bostrom, Nick, Yudkowsky, Eliezer. (2011). The ethics  of artificial intelligence. Cambridge University Press.
  12. Amodei, Dario, Olah, Chris, Steinhardt, Jacob, Christiano, Paul, Schulman, John, Mané, Dan. (2016). Concrete Problems in AI Safety. Cornell University.
  13. Artificial Intelligence and Life in 2030. (2016). Stanford University's One Hundred Year Study on Artificial Intelligence.