Искусственные нейросети в контексте науки и образования
Аннотация
Возросшая популярность искусственных нейросетей (ИНС) в образовании, науке и бизнесе может создать впечатление свершившейся революции в компьютерном моделировании и алгоритмах оптимизации. Данный короткий обзор призван обратить внимание на фундаментальные ограничения ИНС и потенциальный вред, который может нанести поощрение изучения ИНС в ущерб строгим математическим методам
Литература
1. B. Widrow. An Adaptive “Adaline” Neuron Using Chemical “Memistors”. TR No. 1553-2, Stanford University, California, USA (1960). URL: http://www-isl.stanford.edu/~widrow/papers/t1960anadaptive.pdf
2. F. Rosenblatt. Perceptron Simulation Experiments. Proceedings of the IRE, 48(3), 301-309 (1960).
3. J. Nickolls, I. Buck, M. Garland, K. Skadron. Scalable Parallel Programming with CUDA. Queue - GPU Computing, 6(2), 40-53 (2008)
4. G. E. Hinton, S. Osindero, Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554 (2006)
5. G. E. Hinton, R. R. Salakhutdinov Reducing the dimensionality of data with neural networks.
Science, 313, 504 - 507 (2006)
6. D.H. Wolpert, W.G. Macready. No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010, Santa Fe Institute, USA (1995)
7. C. Rackauckas. Algorithm efficiency comes from problem information (2018). URL: http://www.stochasticlifestyle.com/algorithm-efficiency-comes-problem-information/
8. N. Golyandina, A. Korobeynikov, A. Shlemov, K. Usevich. Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package. Journal of Statistical Software, 67(2), 1-78 (2015)
9. K. H. Jin, J. C. Ye. Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting. IEEE Transactions on Image Processing, 24(11), 3498-3511 (2015)
10. T. Simonite. AI Has a Hallucination Problem that’s Proving Hard to Fix. WIRED (2018). URL: https://www.wired.com/story/ai-has-a-hallucination-problem-thats-proving-tough-to-fix/
11. J. Su, D. V. Vargas, S. Kouichi. One pixel attack for fooling deep neural networks (2017). URL: http://arxiv.org/pdf/1710.08864
12. D. Silver et al. Mastering the game of Go without human knowledge. Nature, 550, 354-359 (2017)
13. D. Silver et al. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017). URL: http://arxiv.org/pdf/1712.01815
14. J. C. Collados. Is AlphaZero really a scientific breakthrough in AI? Medium (2017). URL: https://medium.com/@josecamachocollados/is-alphazero-really-a-scientific-breakthrough-in-ai-bf66ae1c84f2
15. S. Kuindersma et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Autonomous Robots, 40(3), 429-455 (2016).
16. C. J. Shallue, A. Vanderburg. Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 (2017). URL: http://arxiv.org/pdf/1712.05044
17. E. A. Smirnov, A. B. Markov. Identification of asteroids trapped inside three-body mean motion resonances: a machine-learning approach. Monthly Notices of the Royal Astronomical Society, 469(2), 2024–2031 (2017).
18. R. Epstein. The empty brain. Aeon (2016). URL: https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer
19. H. Markram et al. Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 163(2), 456-492 (2015)
20. B. Szigeti. OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Frontiers in Computational Neuroscience, 137 (2014)
2. F. Rosenblatt. Perceptron Simulation Experiments. Proceedings of the IRE, 48(3), 301-309 (1960).
3. J. Nickolls, I. Buck, M. Garland, K. Skadron. Scalable Parallel Programming with CUDA. Queue - GPU Computing, 6(2), 40-53 (2008)
4. G. E. Hinton, S. Osindero, Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554 (2006)
5. G. E. Hinton, R. R. Salakhutdinov Reducing the dimensionality of data with neural networks.
Science, 313, 504 - 507 (2006)
6. D.H. Wolpert, W.G. Macready. No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010, Santa Fe Institute, USA (1995)
7. C. Rackauckas. Algorithm efficiency comes from problem information (2018). URL: http://www.stochasticlifestyle.com/algorithm-efficiency-comes-problem-information/
8. N. Golyandina, A. Korobeynikov, A. Shlemov, K. Usevich. Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package. Journal of Statistical Software, 67(2), 1-78 (2015)
9. K. H. Jin, J. C. Ye. Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting. IEEE Transactions on Image Processing, 24(11), 3498-3511 (2015)
10. T. Simonite. AI Has a Hallucination Problem that’s Proving Hard to Fix. WIRED (2018). URL: https://www.wired.com/story/ai-has-a-hallucination-problem-thats-proving-tough-to-fix/
11. J. Su, D. V. Vargas, S. Kouichi. One pixel attack for fooling deep neural networks (2017). URL: http://arxiv.org/pdf/1710.08864
12. D. Silver et al. Mastering the game of Go without human knowledge. Nature, 550, 354-359 (2017)
13. D. Silver et al. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017). URL: http://arxiv.org/pdf/1712.01815
14. J. C. Collados. Is AlphaZero really a scientific breakthrough in AI? Medium (2017). URL: https://medium.com/@josecamachocollados/is-alphazero-really-a-scientific-breakthrough-in-ai-bf66ae1c84f2
15. S. Kuindersma et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Autonomous Robots, 40(3), 429-455 (2016).
16. C. J. Shallue, A. Vanderburg. Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 (2017). URL: http://arxiv.org/pdf/1712.05044
17. E. A. Smirnov, A. B. Markov. Identification of asteroids trapped inside three-body mean motion resonances: a machine-learning approach. Monthly Notices of the Royal Astronomical Society, 469(2), 2024–2031 (2017).
18. R. Epstein. The empty brain. Aeon (2016). URL: https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer
19. H. Markram et al. Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 163(2), 456-492 (2015)
20. B. Szigeti. OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Frontiers in Computational Neuroscience, 137 (2014)
Опубликован
2017-12-30
Как цитировать
Павлов, Д. А. (2017). Искусственные нейросети в контексте науки и образования. Компьютерные инструменты в образовании, (6), 25-31. извлечено от http://cte.eltech.ru/ojs/index.php/kio/article/view/1506
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