Abstract
The focus of this article is related to cognitive sciences: the problems of modeling neural networks and consciousness in general are discussed. Examples of the inadequacy of artificial intelligence systems based on neural network modeling are given, and problems of the modern numerical model of neural networks are considered. In order to solve these problems, the methodology of consciousness neuromodeling based on kinetic-statistical methods is proposed. It is shown that, in the process of quasi-chaotic complication of the neuro-like graph during the percolation transition, large structures (clusters) are formed, which can be interpreted as a manifestation of the primary elements of consciousness. The manifestation of the phenomenon of self-consciousness in the process of complication of the system of neural clusters is discussed. It is possible to use the results of this research for generation of complex neural networks with criteria of finite structure evaluation (number of cycles, simple paths, and Euler’s number).
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REFERENCES
Chalmers, D.J., The Conscious Mind: In Search of a Fundamental Theory, New York: Oxford Univ. Press, 1997.
Bloom, F.E., Lazerson, A., and Hofstadter, L., Brain, Mind and Behavior, New York: W. H. Freeman and Company, 1985.
Turing, A.M., Computing machinery and intelligence, Mind, 1950, vol. 59, no. 236, pp. 433–460. https://doi.org/10.1093/mind/lix.236.433
Alekseev, A.Yu., Kompleksnyi test T’yuringa. Filosofsko-metodologicheskie i sotsiokul’turnye aspekty (The Turing Test: Philosophical, Methodological and Sociocultural Aspects), Moscow: IInteLL, 2013.
Alekseev, A.Yu., The concept of zombies and problems of consciousness, Problemy soznaniya v filosofii i nauke (Problems of Consciousness in Philosophy and Science), Dubrovsky, D.I., Ed., Moscow: Kanon+ ROOI Reabilitatsiya, 2009, p. 195.
Marcus, G. and Davis, E., Rebooting AI: Building Artificial Intelligence We Can Trust, Vintage, 2019.
Marcus, G., The next decade in AI: Four steps towards robust artificial intelligence, 2020. https://doi.org/10.48550/arXiv.2002.06177
Langlotz, C.P., Allen, B., Erickson, B.J., Kalpathy-Cramer, Ja., Bigelow, K., Cook, T.S., Flanders, A.E., Lungren, M.P., Mendelson, D.S., Rudie, J.D., Wang, G., and Kandarpa, K., A roadmap for foundational research on artificial intelligence in medical imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop, Radiology, 2019, vol. 291, no. 3, pp. 781–791. https://doi.org/10.1148/radiol.2019190613
Garbuk, S.V., Intellimetry as a way to ensure AI trustworthiness, 2018 Int. Conf. on Artificial Intelligence Applications and Innovations (IC-AIAI), Nicosia, Cyprus, 2018, IEEE, 2018, pp. 27–30. https://doi.org/10.1109/IC-AIAI.2018.8674447
Stewart, M., The limitations of machine learning, Towards Data Science, 2019. https://towardsdatascience.com/the-limitations-of-machine-learning-a00e0c3040c6.
Chollet, F., On the measure of intelligence, 2019. https://doi.org/10.48550/arXiv.1911.01547
Nie, Yi., Williams, A., Dinan, E., Bansal, M., Weston, J., and Kiela, D., Adversarial NLI: A new benchmark for natural language understanding, Proc. 58th Annu. Meeting of the Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2020, pp. 4885–4901. https://doi.org/10.18653/v1/2020.acl-main.441
Heaven, D., Why deep-learning AIs are so easy to fool: Artificial-intelligence researchers are trying to fix the flaws of neural networks, Nature, 2019, vol. 574, no. 7777, pp. 163–166. https://doi.org/10.1038/d41586-019-03013-5
Deng, L., The MNIST database of handwritten digit images for machine learning research [Best of the Web], IEEE Signal Process. Mag., 2012, vol. 29, no. 6, pp. 141–142. https://doi.org/10.1109/MSP.2012.2211477
Image classification on ImageNet, Paperswithcode.com. https://paperswithcode.com/sota/image-classification-on-imagenet,
Garbuk, S.V. and Gubinskii, A.M., Iskusstvennyi intellekt v vedushchikh stranakh mira: strategii razvitiya i voennoe primenenie (Artificial Intelligence in Leading World Countries: Strategies of Development and Military Use), Moscow: Znanie, 2020.
Keaten, J. and O’Brien, M., UN urges moratorium on use of AI that imperils human rights, AP News, 2021. https://apnews.com/article/technology-business-laws-united-nations-artificial-intelligence-efafd7b1a5bf47afb1376e198842e69d.
Simonov, N.A., Spots concept for problems of artificial intelligence and algorithms of neuromorphic systems, Russ. Microelectron., 2020, vol. 49, no. 6, pp. 431–444. https://doi.org/10.1134/S106373972005008X
Simonov, N., The SPOT model for representation and processing of qualitative data and semantic information, CEUR Workshop Proc., 2021, vol. 3044, p. 8. https://ceur-ws.org/Vol-3044/paper08.pdf
Hall, P., Curtis, J., and Pandey, P., Machine Learning for High-Risk Applications: Techniques for Responsible AI, O’Reilly Media, 2023.
GOST R (State Standard) 59898-2021: Quality assurance of artificial intelligence systems. General, 2021
Ben-Naim, E. and Krapivsky, P.L., Kinetic theory of random graphs: From paths to cycles, Phys. Rev. E, 2005, vol. 71, no. 2, p. 026129. https://doi.org/10.1103/PhysRevE.71.026129
Krapivsky, P.L. and Redner, S., Emergent network modularity, J. Stat. Mech.: Theory Exp., 2017, vol. 2017, p. 073405. https://doi.org/10.1088/1742-5468/aa7a3f
Krapivsky, P.L., Redner, S., and Ben-Naim, E., A Kinetic View of Statistical Physics, Cambridge: Cambridge Univ. Press, 2010.
Albert, R. and Barabási, A.-L., Statistical mechanics of complex networks, Rev. Mod. Phys., 2002, vol. 74, no. 1, p. 47. https://doi.org/10.1103/RevModPhys.74.47
Yang, W., Miller, J.K., Carillo-Reid, L., Pnevmatikakis, E., Paninski, L., Yuste, R., and Peterka, D.S., Simultaneous multi-plane imaging of neural circuits, Neuron, 2016, vol. 89, no. 2, pp. 269–284. https://doi.org/10.1016/j.neuron.2015.12.012
Severino, F.P.U., Ban, J., Song, Q., Tang, M., Bianconi, G., Cheng, G., and Torre, V., The role of dimensionality in neuronal network dynamics, Sci. Rep., 2016, vol. 6, p. 29640. https://doi.org/10.1038/srep29640
Aristov, V.V., Zabelok, S.A., and Frolova, A.A., Modelirovanie neravnovesnykh struktur kineticheskimi metodami (Modeling of Nonequilibrium Structures by Kinetic Methos), Moscow: Fizmatkniga, 2017.
Aristov, V.V. and Il’in, O.V., Methods and problems in the kinetic approach for simulating biological structures, Komp’yuternye Issled. Model., 2017, vol. 10, no. 6, pp. 851–866. https://doi.org/10.20537/2076-7633-2018-10-6-851-866
Aristov, V.V., Biological systems as nonequilibrium structures described by kinetic methods, Results Phys., 2019, vol. 13, p. 102232. https://doi.org/10.1016/j.rinp.2019.102232
Simpson, S.L., Moussa, M.N., and Laurienti, P.J., An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks, Neuroimage, 2012, vol. 60, no. 2, pp. 1117–1126. https://doi.org/10.1016/j.neuroimage.2012.01.071
Kozma, R. and Puljic, M., Random graph theory and neuropercolation for modeling brain oscillations at criticality, Curr. Opin. Neurobiol., 2015, vol. 31, pp. 181–188. https://doi.org/10.1016/j.conb.2014.11.005
Alivisatos, A.P., Chun, M., Church, G.M., Greenspan, R.J., Roukes, M.L., and Yuste, R., The brain activity map project and the challenge of functional connectomics, Neuron, 2012, vol. 74, no. 6, pp. 970–974. https://doi.org/10.1016/j.neuron.2012.06.006
Bouchard, K.E., Mesgarani, N., Johnson, K., and Chang, E.F., Functional organization of human sensorimotor cortex for speech articulation, Nature, 2013, vol. 495, no. 7441, pp. 327–332. https://doi.org/10.1038/nature11911
Stepanyan, I.V. and Petoukhov, S.V., The matrix method of representation, analysis and classification of long genetic sequences, Information, 2017, vol. 8, no. 1, p. 12. https://doi.org/10.3390/info8010012
Tsygankov, V.D., On the neurocomputer model of the “living state” of matter and its “biological field” (in the light of G. Ling and E. Bauer’s works), Int. J. Gen. Syst., 2015, vol. 44, no. 6, pp. 642–654. https://doi.org/10.1080/03081079.2015.1032526
Stepanyan, I.V., Methodology and tools for designing binary neural networks, Programm. Comput. Software, 2020, vol. 46, pp. 49–56. https://doi.org/10.1134/S0361768820010065
Tavanaei, A., Ghodrati, M., Kheradpisheh, S.R., Masquelier, T., and Maida, A.S., Deep learning in spiking neural network, Neural Networks, 2018, vol. 111, pp. 47–63. https://doi.org/10.1016/j.neunet.2018.12.002
Funding
The section “Anti-speculative direction in modeling consciousness in artificial intelligence” was supported by the State Academic University for the Humanities according to the results of the selection of scientific projects conducted by the Ministry of Higher Education and Science of the Russian Federation and ESISI, project no. 1221010000041-6.
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Translated by O. Pismenov
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Alekseev, A.Y., Aristov, V.V., Garbuk, S.V. et al. Kinetic–Statistical Neuromodeling and Problems of Trust in Artificial Intelligence Systems. J. Mach. Manuf. Reliab. 52, 779–790 (2023). https://doi.org/10.1134/S105261882307004X
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DOI: https://doi.org/10.1134/S105261882307004X