scholarly journals ARTS AND MACHINE CIVILIZATION INTERNATIONAL SCIENTIFIC CONFERENCE

2021 ◽  
Vol 17 (2) ◽  
pp. 11-32
Author(s):  
YEVGENY V. DUKOV ◽  
◽  
VIOLETTA D. EVALLYO ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Fine Arts ◽  
Theoretical Models ◽  
Machine Intelligence ◽  
Scientific Conference ◽  
Contemporary Culture ◽  
State Institute ◽  
The Arts

The text reviews the Arts and Machine Civilization International Scientific Conference. The conference took place on March 30—April 2, 2021, and was organized by the State Institute for Art Studies, GITR Film and Television School, and the Saint Petersburg State University. SIAS has been hosting conferences on contemporary culture, screen art and television for 17 years. This year, for the first time in the history of such forums, the researchers were tasked with analyzing the new things that machines have brought to the arts and, in general, to human life. The conference took its special place among the forums held over the past year in Russia and abroad in the following areas: artificial intelligence (Artificial Intelligence Journey, Moscow,Russia); machine learning (International Conferenceon Machine Learning, Vienna, Austria; 3rd International Conference on Machine Learning and Machine Intelligence, Hangzhou, China); civilization of knowledge (Civilization of Knowledge: Russian Realities, Moscow, Russia), etc. The novelty of the conference lies in the unification of the seemingly incompatible phenomena: art and machine civilization. As is commonly known, art was traditionally opposed to technology as something alien, sometimes hostile, although the both were born in human mind and created by human hands. Until now, the expression “machine civilization” in art has been used mainly in the genre of fantasy and with an emphasis on its negative connotations. The purpose of the conference was to comprehend the artistic practices in the era of machine civilization, get acquainted with current hypotheses, publish new facts and discuss modern terminologies (law of spontaneity, law of semantic uncertainty, algorithmic apophenia, post-opera, artificial life and new vitality). Along with the study of new challenges, old issues were raised, which became in demand in the machine civilization: originals and copies of artworks, the boundaries of conventionality and overcoming distrust in new media, narratives and poetics in serious and entertaining screen genres. The conference reports were divided into six blocks: Theoretical Models, ScreenArts—Cinema, Fine Arts, Music, PC Games, and Digitalization.

scholarly journals Editorial. AI, Arts & Design: Questioning Learning Machines

Artnodes ◽  
2020 ◽  
Author(s):  
Ruth West ◽  
Andrés Burbano
Keyword(s):  
Artificial Intelligence ◽  
Historical Development ◽  
Machine Intelligence ◽  
Special Issue ◽  
Control Structures ◽  
Learning Machines ◽  
The Arts ◽  
Production Of Culture ◽  
The Relationship ◽  
Algorithmic Bias

Explorations of the relationship between Artificial Intelligence (AI), the arts, and design have existed throughout the historical development of AI. We are currently witnessing exponential growth in the application of Machine Learning (ML) and AI in all domains of art (visual, sonic, performing, spatial, transmedia, audiovisual, and narrative) in parallel with activity in the field that is so rapid that publication can not keep pace. In dialogue with our contemplation about this development in the arts, authors in this issue answer with questions of their own. Through questioning authorship and ethics, autonomy and automation, exploring the contribution of art to AI, algorithmic bias, control structures, machine intelligence in public art, formalization of aesthetics, the production of culture, socio-technical dimensions, relationships to games and aesthetics, and democratization of machine-based creative tools the contributors provide a multifaceted view into crucial dimensions of the present and future of creative AI. In this Artnodes special issue, we pose the question: Does generative and machine creativity in the arts and design represent an evolution of “artistic intelligence,” or is it a metamorphosis of creative practice yielding fundamentally distinct forms and modes of authorship?

scholarly journals Communication Trends in the Post-Literacy Era: From Human Creativity to the Creativity of Artificial Intelligence and Human-Machine Hybrids

2021 ◽  
Vol 27 (2) ◽  
pp. 235-249
Author(s):  
M. Yu. Gudova ◽  
◽  
E. V. Rubtsova ◽  
N. A. Simbirtseva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Contemporary Art ◽  
Interdisciplinary Research ◽  
Human Life ◽  
Art Museum ◽  
Communication Media ◽  
Main Trend ◽  
Scientific Conference ◽  
Human Creativity ◽  
Mass Information

The article is based on the materials of the Fifth International Theoretical Scientific Conference “Communication trends in the post-literacy era: polylingualism, multimodality and polyculturalism as preconditions for new creativity”, which took place at the Institute of Humanities in November 26–28, 2020. The authors analyze the main communication trends that have developed under the influence of the Covid-2019 pandemic in the sociocultural space in 2020. The main trend is the use of artificial intelligence in such areas of socioculture as communication, media, education. The concept of creativity is clarified, the creative possibilities and limits of human and artificial intelligence are considered, the threats and dangers of the artificial intelligence‘s development and its implementation in various spheres of human life are analyzed, such as education, socialization and inculturation, journalism and mass information, contemporary art, museum and exhibition activity. The conclusion is made about the need for further interdisciplinary research of artificial intelligence in the humanitarian sphere.

scholarly journals Redistribution and Rekognition

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Sarah Myers West
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Community ◽  
Statistical Methods ◽  
Gender Discrimination ◽  
Human Life ◽  
Human Interaction ◽  
The People ◽  
Computer Scientists ◽  
Global Population

Computer scientists, and artificial intelligence researchers in particular, have a predisposition for adopting precise, fixed definitions to serve as classifiers (Agre, 1997; Broussard, 2018). But classification is an enactment of power; it orders human interaction in ways that produce advantage or suffering (Bowker & Star, 1999). In so doing, it obscures the messiness of human life, masking the work of the people involved in training machine learning systems, and hiding the uneven distribution of its impacts on communities (Taylor, 2018; Gray, 2019; Roberts, 2019). Feminist scholars, and particularly feminist scholars of color, have made powerful critiques of the ways in which artificial intelligence systems formalize, classify, and amplify historical forms of discrimination and act to reify and amplify existing forms of social inequality (Eubanks, 2017; Benjamin, 2019; Noble, 2018). In response, the machine learning community has begun to address claims of algorithmic bias under the rubric of fairness, accountability, and transparency. But in doing so, it has largely dealt with these issues in familiar terms, using statistical methods aimed at achieving parity and deploying fairness ‘toolkits’. Yet actually existing inequality is reflected and amplified in algorithmic systems in ways that exceed the capacity of statistical methods alone. This article outlines a feminist critique of extant methods of dealing with algorithmic discrimination. I outline the ways in which gender discrimination and erasure are built into the field of AI at a foundational level; the product of a community that largely represents a small, privileged, and male segment of the global population (Author, 2019). In so doing, I illustrate how a situated mode of inquiry enables us to more closely examine a feedback loop between discriminatory workplaces and discriminatory systems.

scholarly journals An Introduction on Interpretable Machine Learning

2020 ◽  
Vol 9 (7S) ◽  
pp. 107-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Accuracy Score ◽  
Learning Models ◽  
Ethical Practices ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
Machine Learning Models

As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

scholarly journals Modern technologies of marketing research and market analysis

2021 ◽  
Vol 2021 (2) ◽  
pp. 29-39
Author(s):  
Oleh Duma ◽  
◽  
M. Melnyk ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Human Life ◽  
Marketing Research ◽  
Research Process ◽  
Target Market ◽  
Advantages And Disadvantages ◽  
Marketing Activities ◽  
Modern Technologies

Nowadays, marketing research is increasingly important for the success of enterprises. Conducting marketing research reduces the risk of making wrong decisions in the analysis and development of marketing strategies, planning and control of marketing activities. The article provides an overview of the emergence of marketing research, explores the latest methods of marketing research, their advantages and disadvantages, the possibility of its application at different stages of marketing activities. Scientific approaches to the interpretation of the concepts "marketing research", "methods of marketing research" are systematized. The latest methods of marketing research that widely use AI, Big Data, ML, TRI * M, have been studied. The technologies of mobile advertising, areas of use of artificial intelligence, the essence and features of the formation of Big Data and machine learning were researched in the article. The benefits of using artificial intelligence, big data and machine learning to conduct marketing research were researched in the article. Analytical materials are confirmed by cases from the practice of marketing research. All research outcomes were proved by cases of Independent Media, TNS Ukraine, British Council, Chat fuel and Coca - Cola. The scheme of the marketing research process is supplemented by the possibilities of applying the latest technologies, which are grouped by stages. Any marketing research is a sequence of steps. Each of them uses a set of tools that provide collection, processing and analysis of data about the target market, customers, or economic processes. Each of these stages can be implemented using the modern technologies that are widely used in various spheres of human life. The directions of application the artificial intelligence, Big data, machine learning for carrying out office researches, field researches, pilot researches and a method of focus groups are offered. The analysis of realization of methods of marketing researches on the basis of Big Data, AI, ML is carried out.

scholarly journals Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019 ◽  
Vol 8 (10S) ◽  
pp. 292-296
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mental Disorder ◽  
Human Life ◽  
Geographic Location ◽  
Machine Learning Techniques ◽  
World Health ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Depression Detection

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

scholarly journals MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

2019 ◽  
Author(s):  
Сергей Шумский ◽  
Sergey Shumskiy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Revolution ◽  
Machine Intelligence ◽  
General Intelligence ◽  
New Science ◽  
Artificial General Intelligence ◽  
Science Of Mind ◽  
Artificial Brains

This book is about the nature of mind, both human and artificial, from the standpoint of the theory of machine learning. It addresses the problem of creating artificial general intelligence. The author shows how one can use the basic mechanisms of our brain to create artificial brains of future robots. How will this ever-stronger artificial intelligence fit into our lives? What awaits us in the next 10-15 years? How can someone who wants to take part in a new scientific revolution, participate in developing a new science of mind?

scholarly journals Augmented Realities, Artificial Intelligence, and Machine Learning: Clinical Implications and How Technology Is Shaping the Future of Medicine

2020 ◽  
Vol 9 (12) ◽  
pp. 3811 ◽  
Author(s):  
Gaby N. Moawad ◽  
Jad Elkhalil ◽  
Jordan S. Klebanoff ◽  
Sara Rahman ◽  
Nassir Habib ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Narrative Review ◽  
Advanced Technology ◽  
Medical Community ◽  
Clinical Implications ◽  
Medical Providers ◽  
Medical Specialties ◽  
Medical Advances

Technology has been integrated into every facet of human life, and whether it is completely advantageous remains unknown, but one thing is for sure; we are dependent on technology. Medical advances from the integration of artificial intelligence, machine learning, and augmented realities are widespread and have helped countless patients. Much of the advanced technology utilized by medical providers today has been borrowed and extrapolated from other industries. There remains no great collaboration between providers and engineers, which may be why medicine is only in its infancy of innovation with regards to advanced technologic integration. The purpose of this narrative review is to highlight the different technologies currently being utilized in a variety of medical specialties. Furthermore, we hope that by bringing attention to one shortcoming of the medical community, we may inspire future innovators to seek collaboration outside of the purely medical community for the betterment of all patients seeking care.

scholarly journals Human-Centric AI: The Symbiosis of Human and Artificial Intelligence

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 332
Author(s):  
Davor Horvatić ◽  
Tomislav Lipic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Data Driven ◽  
Learning Approaches ◽  
Second Wave

Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society [...]

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