<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.1d1" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher">REMEDIUM</journal-id><journal-title-group><journal-title>REMEDIUM</journal-title></journal-title-group><issn publication-format="print">1561-5936</issn><issn publication-format="electronic">2658-3534</issn><publisher><publisher-name>Joint-Stock Company Chicot</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">1071</article-id><article-id pub-id-type="doi">10.21518/1561-5936-2019-04-36-43</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>MODERN ADVANCED ARTIFICIAL INTELLIGENCE FOR SMART MEDICINE</article-title></title-group><contrib-group><contrib contrib-type="author"><name name-style="western"><surname>Kolesnichenko</surname><given-names>O. Yu.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Martynov</surname><given-names>A. V.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Pulit</surname><given-names>V. V.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Kolesnichenko</surname><given-names>Yu. Yu.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Shakirov</surname><given-names>V. V.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-4"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Mazelis</surname><given-names>L. S.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-5"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Varlamov</surname><given-names>O. O.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-6"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Minushkina</surname><given-names>L. O.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-7"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Sotnik</surname><given-names>A. Yu.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-8"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Zhilina</surname><given-names>T. N.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Dorofeev</surname><given-names>V. P.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-9"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Smorodin</surname><given-names>G. N.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-10"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Zhaparov</surname><given-names>M. K.</given-names></name><bio></bio><email>-</email><xref ref-type="aff" rid="aff-11"/></contrib></contrib-group><aff id="aff-1">Institute of Social Sciences, Sechenov First Moscow State Medical University</aff><aff id="aff-2">SP.ARM</aff><aff id="aff-3">Uzgraph.ru</aff><aff id="aff-4">Scientific Research Institute of System Studies of RAS, Department of Neuroinformatics</aff><aff id="aff-5">Vladivostok State University of Economics and Service</aff><aff id="aff-6">Bauman Moscow State Technical University</aff><aff id="aff-7">Central State Medical Academy at the Department of Presidential Affairs</aff><aff id="aff-8">ZAO (CJSC) Firm CV PROTEK</aff><aff id="aff-9">Moscow Institute of Physics and Technology</aff><aff id="aff-10">Bonch-Bruevich St. Petersburg State University of Telecommunications</aff><aff id="aff-11">Suleyman Demirel University, Kaskelen</aff><pub-date date-type="epub" iso-8601-date="2019-12-04" publication-format="electronic"><day>04</day><month>12</month><year>2019</year></pub-date><issue>4</issue><fpage>36</fpage><lpage>43</lpage><history><pub-date date-type="received" iso-8601-date="2022-03-18"><day>18</day><month>03</month><year>2022</year></pub-date></history><permissions><copyright-statement>Copyright © 2019,</copyright-statement><copyright-year>2019</copyright-year></permissions><abstract>Artificial Intelligence is no longer just the topic of discussion. Today this technology is mostly based on Artificial Neural Networks. Pavlov Principle formulated by W.L. Dunin-Barkowski is used for their training. Mathematics compared Pavlov's doctrine with Deep Reinforcement Learning. AI technologies are divided into Computer Vision, images recognition and generation; Speech Recognition and Synthesis; Natural Language Processing; Graph Logic AI, MIVAR logic technology. All of this separately is Narrow AI. Artificial General Intelligence, equal to human, hasn’t been created yet. AGI should include all mentioned technologies. Given social and linguistic nature of the intelligence emergence, developers are paying attention to NLP algorithms and multi-agent environment. Simultaneously with the development of neural networks, adversary attacks emerged, which using the same learning mechanism force a trained neural network to make mistakes. This fact calls in question the future of neural networks in medicine. Big Data and data sets are the environment for AI. European experts have already begun to regulate Big Data for safe Health Care and drugs creation. Despite the difficulties and lack of clear rules, AI is actively being introduced into the private medicine. Due to AI the three new business models have already been created.</abstract><kwd-group xml:lang="en"><kwd>Artificial Intelligence</kwd><kwd>Artificial Neural Networks</kwd><kwd>Big Data</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>искусственные нейронные сети</kwd><kwd>большие данные</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Bartol T.M., Bromer C., Kinney J., Chirillo M.A., Bourne J.N., Sejnowski T.J. et al. Nanoconnectomic upper bound on the variability of synaptic plasticity. eLife J. 2015;4:e10778. DOI: 10.7554/eLife.10778.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Howard D., Eiben A.E., Kennedy D.F., Mouret J.-B., Valencia P., Winkler D. Evolving embodied intelligence from materials to machines. Nature Machine Intelligence. 2019;1:12-19. DOI. org/10.1038/s42256-018-0009-9.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Дунин-Барковский В.Л., Соловьева К.П. Принцип Павлова в проблеме обратного конструирования мозга. XVIII Международная конференция «Нейроинформатика-2016». Сборник научных трудов, ч. 1. М.: Национальный исследовательский ядерный университет «МИФИ», 2016:11-23.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Dunin-Barkowski W., Solovyeva K. Pavlov Principle and Brain Reverse Engineering. IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB- 2018. Saint Louis, Missouri, USA. 2018; Paper #37: 1-5. DOI: 10.1109/CIBCB.2018.8404975.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Shakirov V.V., Solovyeva K.P., Dunin-Barkowski W.L. Review of State-of-the-Art in Deep Learning Artificial Intelligence. Optical Memory and Neural Networks. 2018;27(2):65-80. DOI: 10.3103/S1060992X18020066.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Dunin-Barkowski W.L., Shakirov V.V. A Way toward Human Level Artificial Intelligence. Optical Memory and Neural Networks. 2019;28(1):21-26. DOI: 10.3103/S1060992X19010041.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Varlamov O.O. Wi!Mi Expert System Shell as the Novel Tool for Building Knowledge-Based Systems with Linear Computational Complexity. The International Review of Automatic Control (IREACO). 2018;11(6):314-325. DOI.org/10.15866/ireaco.v11i6.15855.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Varlamov O.O., Chuvikov D.A., Adamova L.E., Kolesnichenko O.Yu., Petrov M.A., Zabolotskaya I.K., Zhilina T.N. Logical, Philosophical and Ethical Aspects of AI in Medicine. International Conference on Computer Science and Information Technology (ICCSIT-2018), International Journal of Machine Learning and Computing. 2019. В печати.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Booth Jo., Booth Ja. Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine. arXiv:1902.09097v125 Feb 2019.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Pathak D., Lu C., Darrell T., Isola P., Efros A.A. Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. arXiv:1902.05546v114 Feb 2019.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Tassa Y., Doron Y., Muldal A., Erez T., Li Y., Lillicrap T. et al. Deepmind control suite. arXiv:1801.00690v12 Jan 2018.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Wang R., Lehman J., Clune J., Stanley K.O. Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions. arXiv:1901.01753v321 Feb 2019.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Gopalakrishnan A., Mali A., Kifer D., Lee Giles C., Ororbia A.G. A Neural Temporal Model for Human Motion Prediction. arXiv:1809.03036v46 Dec 2018.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Hernandez-Ruiz A., Gall J., Moreno-Noguer F. Human Motion Prediction via Spatio-Temporal Inpainting. arXiv:1812.05478v113 Dec 2018.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Qiu J., Huang G., Lee T.S. A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction. arXiv:1901.09002v125 Jan 2019.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Radford A., Jozefowicz R., Sutskever I. Learning to Generate Reviews and Discovering Sentiment. arXiv:1704.01444v26 Apr 2017.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Radford A., Narasimhan K., Salimans T., Sutskever I. Improving Language Understanding by Generative Pre-Training, 2018. URL: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Radford A., Wu J., Child R., Luan D., Amodei D., Sutskever I. Language Models are Unsupervised Multitask Learners, 2019. https://github.com/openai/gpt-2.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Yogatama D., De Masson d’Autume C., Connor J., Kocisky T., Chrzanowski M., Kong L. et al. Learning and Evaluating General Linguistic Intelligence. arXiv:1901.11373v131 Jan 2019.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Aharoni R., Johnson M., Firat O. Massively Multilingual Neural Machine Translation. arXiv:1903.00089v128 Feb 2019.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Lample G., Conneau A. Cross-lingual Language Model Pretraining. arXiv:1901.07291v122 Jan 2019.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Nachmani E., Wolf L. Unsupervised Polyglot Text To Speech. arXiv:1902.02263v16 Feb 2019.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Haque A, Guo M, Verma P, Fei-Fei L. Audio-Linguistic Embeddings for Spoken Sentences. arXiv:1902.07817v120 Feb 2019.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Gupta A., Vedaldi A., Zisserman A. Learning to Read by Spelling: Towards Unsupervised Text Recognition. arXiv:1809.08675v29 Dec 2018.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Finlayson S.G., Bowers J.D., Ito J., Zittrain J.L., Beam A.L., Kohane I.S. Adversarial attacks on medical machine learning. Science, 2019;363(6433):1287-1289. DOI: 10.1126/science.aaw4399.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Finlayson S.G., Chung H.W., Kohane I.S., Beam A.L. Adversarial Attacks Against Medical Deep Learning Systems. arXiv:1804.05296v34 Feb 2019.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Kolesnichenko Yu., Kolesnichenko O., Smorodin G. 3-Dimensional Vector Analysis of 2-Dimensional Ultrasound Diagnostic Images. 21st Conference of Open Innovations Association FRUCT, University of Helsinki, Finland, 2017:428-434.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>HMA-EMA Joint Big Data Taskforce, Summary report. Heads of Medicines Agencies EU, European Medicines Agency. EMA/105321/2019.13 February 2019, 48.</mixed-citation></ref></ref-list></back></article>
