Contemporary Imagetics and Post-Images in Digital Media Art

2020 ◽  
pp. 1-24
Author(s):  
Jose Alberto Raposo Pinheiro
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Media ◽  
Digital Information ◽  
Media Art ◽  
Biometric Data ◽  
New Developments

Scientific studies have contributed, over the last years, to an expansion of the Image concept, in articulation with new developments in Computational Media, based in a stratification around technical digital properties, which frame its existence to the form of digital information – extending it beyond a visual surface idea. Biometric data, artificial intelligence, bitcoin, glitches or machine learning are examples of instantiation tools used by artists to explore elements of mediation included in Post-images. This chapter addresses today's perspectives in Contemporary Imagetics emerging from the field of Digital Media Art (DMA), curating contributions from classic postproduction techniques to computational media instantiations and contextualizing imagery creation practice in DMA.

Media Technologies in Communication and Critical Cultural Studies

2018 ◽  
Author(s):  
Ned O'Gorman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Studies ◽  
Digital Media ◽  
Media Studies ◽  
21St Century ◽  
Social Life ◽  
Media Technologies ◽  
Cultural Processes ◽  
The Relationship

Media technologies are at the heart of media studies in communication and critical cultural studies. They have been studied in too many ways to count and from a wide variety of perspectives. Yet fundamental questions about media technologies—their nature, their scope, their power, and their place within larger social, historical, and cultural processes—are often approached by communication and critical cultural scholars only indirectly. A survey of 20th- and 21st-century approaches to media technologies shows communication and critical cultural scholars working from, for, or against “deterministic” accounts of the relationship between media technologies and social life through “social constructivist” understandings to “networked” accounts where media technologies are seen embedding and embedded within socio-material structures, practices, and processes. Recent work on algorithms, machine learning, artificial intelligence, and platforms, together with their manifestations in the products and services of monopolistic corporations like Facebook and Google, has led to new concerns about the totalizing power of digital media over culture and society.

scholarly journals New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1384
Author(s):  
Chris Rhodes ◽  
Richard Allmendinger ◽  
Ricardo Climent
Keyword(s):  
Machine Learning ◽  
Digital Media ◽  
Wearable Sensors ◽  
Model Performance ◽  
Biometric Data ◽  
Interactive Music ◽  
Music Practice ◽  
Model Mapping ◽  
Continuous Models ◽  
Piano Practice

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.

Research on the development trend and application of digital media art in graphic design education

2021 ◽  
pp. 002072092098431
Author(s):  
Ying Liu
Keyword(s):  
New Media ◽  
Digital Media ◽  
Graphic Design ◽  
Design Education ◽  
Rapid Development ◽  
Development Trend ◽  
Digital Information ◽  
Media Art ◽  
Visual Communication Design ◽  
Teaching Concept

The rapid development of new media technology has a significant impact on the traditional graphic design art. It not only promotes the rapid development of graphic design field, but also accelerates the transformation and upgrading of the whole visual communication design education concept. The traditional teaching concept of graphic design cannot meet the needs of students, but the education of graphic design under digital information can improve the satisfaction of students with gorgeous visual effects. Therefore, it is of great significance to study the reform of modern graphic design education mode in the digital media environment. This paper first analyzes the development of digital media art and graphic design education, then studies the relationship between graphic design teaching and mobile digital media, and finally discusses the value of digital media art in graphic design teaching and the significance of reform.

scholarly journals AI and the Form of Capital – Preliminary Notes on the Media Culture of Artificial Intelligence

2019 ◽  
Author(s):  
Jens Schröter ◽  
Christoph Ernst
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Media ◽  
Media Culture ◽  
Central Topic ◽  
Technological Developments ◽  
The Media

Recently there is much talk about the coming possibilities of “Artificial Intelligence” or “Robotics”. But these technological developments are embedded in a media culture. The recent data surge is at once the precondition for machine learning as is the analysis of this data one of the goals of AI-research. The economicand ideological forms of society shape the media culture of AI. In this essay, we try to sketch these implications for understanding AI as a central topic of recent digital media culture.

scholarly journals Data-Driven Journalistic Operation: Reshaping the Idea of News Values with Algorithms, Artificial Intelligence and Increased Personalization

2020 ◽  
Vol 16 (3) ◽  
pp. 458-475
Author(s):  
Márcio Carneiro Dos Santos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Media ◽  
Social Engagement ◽  
Current Level ◽  
Data Driven ◽  
News Values ◽  
News Organizations ◽  
Media Ecosystem

This article discusses the strategic reconfiguration of data in news organizations through the use of algorithms and artificial intelligence, more specifically machine learning, to increase the idea of value around information, which has suffered from fragmented audiences, a massive increase in the number of broadcasters, indirect competition from large technology companies, and changes to the digital media ecosystem. We propose to identify patterns of interest, predict social engagement, and allocate resources for new coverage as forms for increasing the current level of personalization offered to news consumers.Discute-se a reconfiguração estratégica da utilização de dados dentro das organizações jornalísticas, através de algoritmos e soluções de inteligência artificial, entre elas especificamente o aprendizado de máquina, como alternativa para elevação da percepção de valor sobre o produto informativo, reduzida pela fragmentação das audiências, explosão de emissores, concorrência indireta das grandes empresas de tecnologia e transformações do ecossistema de meios digitais. Tal abordagem propõe a identificação de padrões de interesse, a predição de engajamento social e a alocação de recursos para coberturas como formas de expandir o atual nível de personalização oferecido aos consumidores de notíciaSe discute la reconfiguración estratégica del uso de datos en las organizaciones periodísticas a través de algoritmos y soluciones de inteligencia artificial, específicamente, el aprendizaje automático como una alternativa para aumentar la percepción de valor sobre el producto de información, el cual se reduce por la fragmentación de las audiencias, el aumento en la cantidad de emisores, la competencia indirecta de grandes compañías tecnológicas y las transformaciones del ecosistema de medios digitales. Tal enfoque propone la identificación de patrones de interés, la predicción del compromiso social y la asignación de recursos para la cobertura como formas de expandir el nivel actual de personalización ofrecido a los consumidores de noticias.

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