Artificial intelligence based neurofeedback

Plotnikov, Sergei A.; Lipkovich, Mikhail; Semenov, Danila M.; Fradkov, Alexander L.

doi:10.35470/2226-4116-2019-8-4-287-291

Cited by 9 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(3) Building new algorithms based on the principles of the human brain, evolutionary theories, cognitive science, neuroscience, and other sciences previously associated only with humans and animals (Plotnikov et al, 2019;Kropotov and Pakhomov, 1984). Scientists have been studying the process of functioning of the human brain for many years, but even with the availability of modern technologies, it is not possible to simulate its operation.…”

Section: Ai Development Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Co-evolutionary hybrid intelligence is a key concept for the world intellectualization

Krinkin

Shichkina

Ignatyev

2022

View full text Add to dashboard Cite

PurposeThis study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).Design/methodology/approachThe global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).FindingsThe key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.Research limitations/implicationsResistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.Practical implicationsRevision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.Originality/valueThe novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

show abstract

Section: Ai Development Limitationsmentioning

confidence: 99%

“…, 1996). Building new algorithms based on the principles of the human brain, evolutionary theories, cognitive science, neuroscience, and other sciences previously associated only with humans and animals (Plotnikov et al. , 2019; Kropotov and Pakhomov, 1984).…”

Section: Introductionmentioning

confidence: 99%

Co-evolutionary hybrid intelligence is a key concept for the world intellectualization

Krinkin

Shichkina

Ignatyev

2022

View full text Add to dashboard Cite

show abstract

“…Recent studies have shown that machine learning methods, including neural networks, have potential for simulating nonequilibrium gas-dynamic problems [Stokes et al, 2020;Schmidt et al, 2019;Brunton et al, 2020]. The use of machine learning methods for modeling of physical systems has grown sharply, see [Fradkov, 2022;Plotnikov et al, 2019;Fradkov and Shepeljavyi, 2022]. Machine learning methods help to accurately predict physical quantities by processing large amounts of available data, which significantly reduces computational effort and allows for implementing detailed models of physical-chemical kinetics and transport processes [Istomin and Kustova, 2021;Campoli et al, 2022;Bushmakova and Kustova, 2022].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of state-specific transport properties using machine learning methods

Истомин

Кустова

Lagutin

et al. 2023

CAP

View full text Add to dashboard Cite

In this study, machine learning algorithms are employed to calculate state-to-state transport coefficients in nonequilibrium reacting gas flows. The focus is on the evaluation of thermal conductivity, shear viscosity, and bulk viscosity coefficients under conditions of strong coupling between vibrational-chemical kinetics and gas dynamics. In order to solve a regression problem for evaluating state-to-state transport coefficients, a specific software application with user interface is developed, which allows loading, processing, and saving of data arrays; configuring model architecture; training and evaluating models with various optimizers, loss functions, and metrics; making predictions using trained models. Using the developed software the multi-layer perceptron regression model is constructed and trained. The model is assessed in a binary mixture of molecular and atomic nitrogen taking into account 48 vibrational states; the coefficients are computed in the wide temperature range for the varying mixture composition. Good agreement of the results with the original transport coefficients calculated using rigorous but computationally expensive kinetic theory algorithms is shown. Applying machine learning techniques yields a significant speedup of about two orders of magnitude in the computation of transport coefficients. It is concluded that implementation of machine learning methods may considerably reduce the computational efforts required for nonequilibrium flow simulations.

show abstract

“…Сказанное объясняет важность повторной публикации в разделе «Математический архив» статьи В. А. Якубовича, впервые опубликованной в 1963 г. Важно отметить также, что теория адаптивных и интеллектуальных систем, основы которой заложены в статье, представляемой вниманию читателей «Вестника», успешно развивается на кафедре теоретической кибернетики СПбГУ, где в течение многих лет публикуются статьи, монографии, учебные пособия [12][13][14][15][16][17][18][19][20][21].…”

unclassified

Machine learning and artificial intelligence in the works of V. A. Yakubovich

Fradkov¹

2021

Vestnik SPbSU. Mathematics. Mechanics. Astronomy

View full text Add to dashboard Cite

show abstract

Artificial intelligence based neurofeedback

Cited by 9 publications

References 12 publications

Co-evolutionary hybrid intelligence is a key concept for the world intellectualization

Co-evolutionary hybrid intelligence is a key concept for the world intellectualization

Evaluation of state-specific transport properties using machine learning methods

Machine learning and artificial intelligence in the works of V. A. Yakubovich

Contact Info

Product

Resources

About