Potentials of the bio-inspired approach in the development of artificial intelligence systems (trends review)

A. R. Nurutdinov   ; R. Kh. Latypov

Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 164 (2022) no. 2, pp. 244-265

Résumé

Artificial intelligence (AI) efficiently builds predictive models in engineering, politics, economics, and science, as well as provides optimal strategies for solving various problems. However, modern AIs are often far from being as accurate as one might have expected a few decades ago. As a result, a number of problems linked to the widespread use of AI hinder the positive effects of the tasks it solves. This article focuses on the difficulties and limitations in using AI systems that have arisen to date and possible ways to overcome them.

Détail

Keywords: artificial intelligence, machine learning, bio-inspired approach, cerebellum model.

Bibliographie

[1] McCarthy J., Minsky M. L., Rochester N., Shannon C. E., “A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955”, AI Mag., 27:4 (2006), 12–14 | DOI

[2] van Lent M., Fisher W., Mancuso M., “An explainable artificial intelligence system for small-unit tactical behavior”, Proc. 16th Conf. on Innovative Applications of Artificial Intelligence, AAAI Press, 2004, 900–907

[3] Dellermann D., Calma A., Lipusch N., Weber Th., Weigel S., Ebel P., “The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems”, Proc. 52nd Hawaii Int. Conf. on System Sciences, 2019, 274–283

[4] Kirsh D., “Foundations of AI: The big issues”, Artif. Intell., 47:1–3 (1991), 3–30 | DOI | MR

[5] Monett D., Lewis C. W.P., “Getting clarity by defining artificial intelligence – a survey”, Philosophy and Theory of Artificial Intelligence, PT-AI 2017, Studies in Applied Philosophy, Epistemology and Rational Ethics, 44, ed. Müller V., 2018, 212–214 | DOI | MR

[6] Turing A. M., “Computing machinery and intelligence”, Mind. New Ser., 59:236 (1950), 433–460 | DOI | MR

[7] Hayes P., Ford K., “Turing test considered harmful”, Proc. 14th Int. Joint Conf. on Artificial intelligence, IJCAI'95, v. 1, 1995, 972–977

[8] Marcus G., Rossi F., Veloso M., “Beyond the Turing test”, AI Mag., 37:1 (2016), 3–4 | DOI

[9] WIPO Technology Trends 2019 – Artificial Intelligence, WIPO, Geneva, Switzerland, 2019, 154 pp. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf/

[10] Batarseh F. A., Freeman L. Huang Ch.-H., “A survey on artificial intelligence assurance”, J. Big Data, 8 (2021), 60, 1–30 | DOI | MR

[11] Blagec K., Barbosa-Silva A., Ott S., Samwald M., “A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks”, Sci. Data, 9:1 (2022), 322, 1–10 | DOI

[12] Pearson K., “LIII. On lines and planes of closest fit to systems of points in space”, London, Edinburgh, Dublin Philos. Mag. J. Sci. Ser. 6, 2:11 (1901), 559–572 | DOI

[13] LeCun Y., Bengio Y., Hinton G., “Deep learning”, Nature., 521:7553 (2015), 436–444 | DOI | MR

[14] Russell S.J, Norvig P., Artificial Intelligence: A Modern Approach, Prentice Hall, 2010, xviii+1132 pp.

[15] Shannon C., “XXII. Programming a computer for playing chess”, Philos. Mag., Ser. 7, 41:314 (1950), 1–18 | MR

[16] Brown T. B., Mann B., Ryder N., Subbiah M., Kaplan J., Dhariwal P., Neelakantan A., Shyam P., Sastry G., Askell A., Agarwal S., Herbert-Voss A., Krueger G., Henighan T., Child R., Ramesh A., Ziegler D. M., Wu J., Winter C., Hesse Ch., Chen M., Sigler E., Litwin M., Gray S., Chess B., Clark J., Berner Ch., McCandlish S., Radford A., Sutskever I., Amodei D., Language models are few-shot learners, 2005, arXiv: 2005.14165v4 | DOI

[17] Littman M. L., Ajunwa I., Berger G., Boutilier C., Currie M., Doshi-Velez F., Hadfield G., Horowitz M. C., Isbell Ch., Kitano H., Levy K., Lyons T., Mitchell M., Shah J., Sloman St., Vallor Sh., Walsh T., Gathering Strength, Gathering Storms, The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report, Stanford Univ., Stanford, CA, 2021, 82 pp. https://ai100.stanford.edu/gathering-strength-gathering-storms-one-hundred-year-study-artificial-intelligence-ai100-2021-study/

[18] Sarker I. H., “Machine learning: Algorithms, real-world applications and research directions”, SN Comput. Sci., 2 (2021), 160, 1–21 | DOI | MR

[19] Pearl J., Mackenzie D., The Book of Why: The New Science of Cause and Effect, Basic Books, N. Y., 2018, 432 pp. | MR

[20] Nilsson N., The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge Univ. Press, 2010, 562 pp. | DOI | MR

[21] Levesque H. J., Davis E., Morgenstern L., “The Winograd schema challenge”, Proc. 13th Int. Conf. on the Principles of Knowledge Representation and Reasoning, Inst. Electr. Electron. Eng. Inc., 2012, 552–561

[22] LeCun Y., How the Machine Learns: The Revolution in Neural Networks and Deep Learning, Al'pina PRO, M., 2021, 335 pp. (In Russian)

[23] Barricelli N., “Esempi numerici di processi di evoluzione”, Methodos, 6 (1954), 45–68 (In Italian) | MR

[24] Rajkumar R., Ganapathy V., “Bio-inspiring learning style Chatbot inventory using brain computing interface to increase the efficiency of E-learning”, IEEE Access, 8 (2020), 67377–67395 | DOI

[25] Ashby R. W., An Introduction to Cybernetics, Chapman Hall, London, 1956, ix+295 pp. | MR

[26] Boisot M., McKelvey B., “Complexity and organization-environment relations: Revisiting Ashby's law of requisite variety”, The Sage Handbook of Complexity and Management, eds. Allen P., Maguire St., McKelvey B., Sage Publ., London, 2011, 279–298 | DOI

[27] Schelling C., “Dynamic models of segregation”, J. Math. Soc., 1:2 (1971), 143–186 | DOI

[28] Rosenblatt F., Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Mir, M., 1965, 480 pp. (In Russian)

[29] Middleton F. A., Strick P. L., “The cerebellum: An overview”, Trends Neurosci., 21:9 (1998), 367–936 | DOI

[30] Herculano-Houzel S., “Coordinated scaling of cortical and cerebellar numbers of neurons”, Front. Neuroanat., 4 (2019), 12, 1–8 | DOI

[31] Herculano-Houzel S., Avelino-de-Souza K., Neves K., Porfírio J., Messeder D., Mattos Feijó L., Maldonado J., Manger P. R., “The elephant brain in numbers”, Front. Neuroanat., 8 (2014), 46, 1–8 | DOI

[32] Kawato M., “Internal models for motor control and trajectory planning”, Curr. Opin. Neurobiol., 9:6 (1999), 718–727 | DOI

[33] Broucke M. E., “Adaptive internal models in neuroscience”, Found. Trends\circledR Syst. Control, 9:4 (2022), 365–550 | DOI

[34] Welniarz Q., Worbe Y., Gallea C., “The Forward Model: A Unifying Theory for the Role of the Cerebellum in Motor Control and Sense of Agency”, Front. Syst. Neurosci., 15 (2021), 644059, 1–14 | DOI

[35] Wolpert D. M., Miall R. C., “Forward models for physiological motor control”, Neural Networks, 9:8 (1996), 1265–1279 | DOI

[36] Green A. M., Hirata Y., Galiana H. L., Highstein S. M., “Localizing sites for plasticity in the vestibular system”, The Vestibular System, Springer Handbook of Auditory Research, 19, eds. Highstein S. M., Fay R. R., Popper A. N., Springer, N. Y., 2004, 423–495 | DOI

[37] Kawato M., Gomi H., “A computational model of four regions of the cerebellum based on feedback-error learning”, Biol. Cybern., 68 (1992), 95–103 | DOI

[38] Wolpert D. M., Ghahramani Z., Jordan M. I., “An internal model for sensorimotor integration”, Science, 269:5232 (1995), 1880–1882 | DOI

[39] Albus J., “A new approach to manipulator control: The cerebellar model articulation controller (CMAC)”, J. Dyn. Syst., Meas., Control, 97:3 (1975), 220–227 | DOI

[40] Rosenblatt F., Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books, Washington, DC, 1962, 616 pp. | MR

[41] Albus J. S., “A theory of cerebellar function”, Math. Biosci., 10:1–2 (1971), 25–61 | DOI

[42] Minsky M., Papert S., Perceptrons, Mir, M., 1971, 264 pp. (In Russian)

[43] Gonzalez-Serrano F. J., Figueiras-Vidal A. R., Artes-Rodriguez A., “Generalizing CMAC architecture and training”, IEEE Trans. Neural Networks, 9:6 (1998), 1509–1514 | DOI

[44] Tsa Y., Chu H.-C., Fang S.-H., Lee J., Lin C.-M., “Adaptive noise cancellation using deep cerebellar model articulation controller”, IEEE Access, 6 (2018), 37395–37402 | DOI

[45] Huynh T.-T., Lin Ch.-M., Le T.-L., Cho H.-Y., Pham Th.-Th.T., Le N.-Q.-K., Chao F., “A new self-organizing fuzzy cerebellar model articulation controller for uncertain nonlinear systems using overlapped Gaussian membership functions”, IEEE Trans. Ind. Electron., 67:11 (2020), 9671–9682 | DOI

[46] Fan R., Li Y., “An adaptive fuzzy trajectory tracking control via improved cerebellar model articulation controller for electro-hydraulic shovel”, IEEE/ASME Trans. Mechatron., 26:6 (2021), 2870–2880 | DOI

[47] Ji D., Shin D., Park J., “An error compensation technique for low-voltage DNN accelerators”, IEEE Trans. Very Large Scale Integr (VLSI) Syst., 29:2 (2021), 397–408 | DOI

[48] Agrawal K., To study the phenomenon of the Moravec's paradox, 2010, arXiv: 1012.3148 | DOI

[49] Moravec H., Mind Children: The Future of Robot and Human Intelligence, Harvard Univ. Press, Cambridge, Mass., 1988, 214 pp.

[50] Pinker S., The Language Instinct: How the Mind Creates Language, William Morrow, N. Y., 1994, 494 pp. | MR