MODERN INFORMATICS: FROM ROBOTICS TO ARTIFICIAL INTELLIGENCE

2018 ◽  
Vol 1 (8) ◽  
pp. 2-5 ◽  
Author(s):  
L. L. Bosova ◽  
N. N. Samylkina
Keyword(s):  
Artificial Intelligence ◽  
Different Levels

The article describes the work of Informatics Club in the framework of the project "Children University of MPSU". It is considered how it is possible to realize the development of complex questions of informatics in the framework of Club work with students of different levels of education.

scholarly journals Measuring Digital Transformation Maturity of Supply Chain

2021 ◽  
Vol 15 (2) ◽  
pp. 199-204
Author(s):  
Krešimir Buntak ◽  
Matija Kovačić ◽  
Maja Mutavdžija
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Supply Chain ◽  
Industry 4.0 ◽  
Digital Transformation ◽  
Strategic Decision ◽  
Traditional Practices ◽  
Long Run ◽  
Efficiency And Effectiveness ◽  
Different Levels

Digital transformation signifies changes in all components and systems of the supply chain. It is also a strategic decision of the organization which, in the long run, can result in the creation of competitive advantage in the market. Digital transformation is affecting all organizations, regardless of their activity. Digital transformation of the supply chain involves the use of industry 4.0 based technologies as well as the replacement of traditional practices with new ones based on digital solutions. The implementation of digital solutions, such as artificial intelligence, IoT, cloud computing, etc., therefore, improve communication between stakeholders in the supply chain, as well as improve efficiency and effectiveness. When conducted, digital transformation must be measured by different levels of maturity. In this paper, authors research current models of measuring digital transformation maturity in supply chain and propose a new model based on identified theories and needs.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

scholarly journals Automation

Management ◽  
2019 ◽  
Author(s):  
Manav Raj ◽  
Robert C. Seamans
Keyword(s):  
Artificial Intelligence ◽  
Labor Market ◽  
21St Century ◽  
Innovation Management ◽  
Major Work ◽  
Dominant Firms ◽  
Occupational Task ◽  
And Robotics ◽  
Different Levels ◽  
Task Level

Since the first decades of the 20th century, there has been concern that automation, including mechanization, computing, and more recently robotics and artificial intelligence (AI), will take away jobs and damage the labor market. There has also been concern that large, dominant firms will capture whatever value is created by automating technologies. In an effort to understand these issues, a wide variety of scholars have studied automation. Automation has been studied at a number of levels, including country, industry, firm, occupation, and even the occupational-task level, and by a range of disciplines, including economics, innovation, management, organizational theory, sociology, and strategy. This annotated bibliography attempts to include a range of literature that speaks to these different levels and different disciplines. It includes articles that are older, foundational pieces so readers can familiarize themselves with the major work in the area, as well as more recent articles so readers can get a sense of current research interests and opportunities. Notably, much of the recent research is focused on the effects of AI and robotics on workers, firms, and the economy. It is likely that there will be a large increase in research in this space in the coming years, especially as more data on the adoption of these technologies becomes available, and that this research will tell us much more about how these technologies are affecting our economy in the 21st century as well as inform our understanding of automation more generally.

scholarly journals Comparison of subsurface object recognition by artificial neural networks and correlation method

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Object Recognition ◽  
Noise Immunity ◽  
Cross Correlation ◽  
Fdtd Method ◽  
Data Set ◽  
Electrodynamic Problem ◽  
Artificial Neural ◽  
Different Levels

Background: The problem of searching for subsurface objects has a particular interest for construction, archeology and humanitarian demining. Detection of underground mines with the help of remote sensing devices replaces the traditional procedure of finding explosive objects, as it excludes the presence of a human in the area of possible damage during a charge explosion. Objectives: The aim of the work is to improve the recognition of three-dimensional objects and demonstrate the benefits of using a more informative data set obtained by a special antenna system with four receiving antennas. In addition, it is necessary to compare the effectiveness of artificial intelligence and the method of cross-correlation for recognition by subsurface radar, taking into account the additive noise of different levels present in practice. Materials and methods: The electrodynamic problem was solved by the finite difference time domain (FDTD) method. An artificial neural network (ANN) is trained on ideal signals to detect the features of the field that will be found in noisy data to determine to the position of the object. Cross-correlation also involves the use of an array of ideal signals, which will be correlated with noisy real signals. Results: The optimal and effective ANN structure for work with the received signals is created. It was tested for noise immunity. The recognition problem was also solved by the classical method of cross-correlation, and the influence of noise of different levels on its responses was studied. In addition, a comparison of the efficiency of their recognition using 1 and 4 sensors was made. Conclusions: For subsurface survey problems, a deep neural networks with at least three hidden layers of neurons should be used. This is due to the complexity and multidimensionality of the processes taking place in the surveyed space. It has been shown that artificial intelligence and cross-correlation techniques perform the object recognition well, and it is difficult to identify the best among them. Both approaches showed good noise immunity. The use of a larger data set of four receivers has a positive effect on the recognition results.

Neural Networks with Disabilities: An Introduction to Complementary Artificial Intelligence

2021 ◽  
pp. 1-36
Author(s):  
Vagan Terziyan ◽  
Olena Kaikova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Cognitive Skills ◽  
Autonomous Systems ◽  
Cognitive Model ◽  
Network Architectures ◽  
Artificial Agent ◽  
Good Tool ◽  
Optimal Behavior ◽  
Different Levels

Abstract Machine learning is a good tool to simulate human cognitive skills as it is about mapping perceived information to various labels or action choices, aiming at optimal behavior policies for a human or an artificial agent operating in the environment. Regarding autonomous systems, objects and situations are perceived by some receptors as divided between sensors. Reactions to the input (e.g., actions) are distributed among the particular capability providers or actuators. Cognitive models can be trained as, for example, neural networks. We suggest training such models for cases of potential disabilities. Disability can be either the absence of one or more cognitive sensors or actuators at different levels of cognitive model. We adapt several neural network architectures to simulate various cognitive disabilities. The idea has been triggered by the “coolability” (enhanced capability) paradox, according to which a person with some disability can be more efficient in using other capabilities. Therefore, an autonomous system (human or artificial) pretrained with simulated disabilities will be more efficient when acting in adversarial conditions. We consider these coolabilities as complementary artificial intelligence and argue on the usefulness if this concept for various applications.

scholarly journals Correction to: An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

2021 ◽  
Author(s):  
Xi Zhang ◽  
Xianhai Wang ◽  
Hongke Zhao ◽  
Patricia Ordóñez de Pablos ◽  
Yongqiang Sun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Effectiveness Analysis ◽  
Different Levels

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2018 ◽  
pp. 2025-2041
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2017 ◽  
Vol 7 (3) ◽  
pp. 40-53 ◽  
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

Non-symbolic approaches to artificial intelligence and the mind

1994 ◽  
Vol 349 (1689) ◽  
pp. 87-102 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Computational Neuroscience ◽  
External World ◽  
The Mind ◽  
Symbolic Approach ◽  
Different Levels ◽  
New Algorithms

Current theories of artificial intelligence and the mind are dominated by the notion that thinking involves the manipulation of symbols. The symbols are intended to have a specific semantics in the sense that they represent concepts referring to objects in the external world and they conform to a syntax, being operated on by specific rules. I describe three alternative, non-symbolic approaches, each with a different emphasis but all using the same underlying computational model. This is a network of interacting computing units, a unit representing a nerve cell to a greater or lesser degree of fidelity in the different approaches. Computational neuroscience emphasizes the development and functioning of the nervous system; the approach of neural networks examines new algorithms for specific applications in, for example, pattern recognition and classification; according to the sub-symbolic approach , concepts are built up of entities called sub-symbols, which are the activities of individual processing units in a neural network. A frequently debated question is whether theories formulated at the subsymbolic level are ‘mere implementations’ of symbolic ones. I describe recent work due to Foster, who proposes that it is valid to view a system at many different levels of description and that, whereas any theory may have many different implementations, in general sub-symbolic theories may not be implementations of symbolic ones.

Prospects For the Introduction of Artificial Intelligence Technologies in Higher and General Education

2021 ◽  
pp. 20-27
Author(s):  
Irina Ivanovna Nekrasova ◽  
◽  
Konstantin Vladimirovich Rozov ◽  
Boris Aleksandrovich Schreiner ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Teacher Education ◽  
General Education ◽  
Computer Science ◽  
Python Language ◽  
State Pedagogical ◽  
Learning Programming ◽  
School Computer ◽  
Different Levels

At present, comprehensive research on the implementation of artificial intelligence technologies is of particular relevance. The article is devoted to the implementation of artificial intelligence technologies in the field of education. The perspective directions of using artificial intelligence in the sphere of higher and general education are considered and analyzed. The article actualizes the problem of implementing artificial intelligence technologies in teaching students and schoolchildren. The purpose of the article is to reveal the specifics of the use of artificial intelligence technologies in the field of education. The study is theoretical in nature and includes an analysis of the possibilities of using artificial intelligence technologies at different levels of education. The program of the discipline “Artificial Intelligence Technologies”, developed in the areas of Teacher education training at the Novosibirsk State Pedagogical University, is presented. The possibilities of studying artificial intelligence technologies using the Python language in a school computer science course are considered, which will allow you to move to a new level of learning programming both in schools and in higher education.

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