scholarly journals The Teleological Nature of Digital Aesthetics – the New Aesthetic in Advance of Artificial Intelligence

2019 ◽  
pp. 105
Author(s):  
Scott Contreras-Koterbay
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Media Studies ◽  
Technological Development ◽  
Aesthetic Experiences ◽  
Real Possibility ◽  
Digital Aesthetics ◽  
Curatorial Practice ◽  
Digital Objects ◽  
The Aesthetic

If aesthetic and teleological judgments are equally reflective, then it can be argued that such judgments can be applied concurrently to digital objects, specifically those that are products of the rapidly developing sophisticated forms of artificial intelligence (AI). Evidence of the aesthetic effects of technological development are observable in more than just experienceable objects; rooted in inscrutable machine learning, AI’s complexity is a problem when it is presented as an aesthetic authority, particularly when it comes to automated curatorial practice or as a progressively determinative aesthetic force originating in an independent agency that is internally self-consistent.Rooted in theories of the post-digital and the New Aesthetic, this paper examines emerging new forms of art and aesthetic experiences that appear to reveal these capabilities of AI. While the most advanced forms of AI barely qualify for a ‘soft’ description at this point, it appears inevitable that a ‘hard’ form of AI is in the future. Increased forms of technological automation obscure the increasingly real possibility of genuine products of the imagination and the creativity of autonomous digital agencies as independent algorithmic entities, but such obfuscation is likely to fade away under the evolutionary pressures of technological development. It’s impossible to predict the aesthetic products of AI at this stage but, if the development of AI is teleological, then it might be possible to predict some of the foreseeable associated aesthetic problems. Article received: April 10, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Original scholarly paperHow to cite this article: Contreras-Koterbay, Scott. "The Teleological Nature of Digital Aesthetics – the New Aesthetic in Advance of Artificial Intelligence." AM Journal of Art and Media Studies 20 (2019): 105-112. doi: 10.25038/am.v0i20.326.

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 Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

Behavior Morphologies of Machine Learning Agents in Media Artworks

Leonardo ◽  
2019 ◽  
pp. 1-10
Author(s):  
Sofian Audry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Life ◽  
Adaptive Systems ◽  
Learning Systems ◽  
Artificial Agents ◽  
Adaptive Agents ◽  
Learning Agents ◽  
The Aesthetic

Since the 1950s, a range of artists have used artificial agents in their work, in parallel with scientific research in cybernetics, artificial intelligence (AI) and artificial life (AL). In particular, an increasing number of artists work with machine learning and other adaptive systems. Through my own engagement with such systems, I analyze adaptive agents within the broader context of the aesthetic of behavior. As a result, I propose an aesthetic framework for understanding behaviors which considers the role of the observer as an adaptive perceiving agent, the unfathomable character of machine learning systems, and the morphology of behaviors as time-based phenomenon.

scholarly journals Screens as Gestures in Interactive Art Assemblage

2018 ◽  
pp. 147
Author(s):  
Charu Maithani
Keyword(s):  
Media Studies ◽  
Preliminary Report ◽  
Interactive Media ◽  
Media Art ◽  
Interactive Art ◽  
Aesthetic Experiences ◽  
Contemporary Media ◽  
The Aesthetic ◽  
Interactive Media Art

The interaction in the contemporary media art installations can be viewed as a process of transformation as the parts of the installation engage and respond to each other. This paper considers interactive media art as assemblages and argues screens to be gestures of this assemblage. The screens activate and rearrange the relations between the elements of the assemblage by providing multiple connections between them. By examining two artworks, Breath (1991/92) by Ulrike Gabriel and Shadow 3 (2007) by Shilpa Gupta, the paper extrapolates the aesthetic experiences gestured by the screens. Article received: April 25, 2018; Article accepted: May 10, 2018; Published online: October 15, 2018; Preliminary report – Short CommunicationsHow to cite this article: Maithani, Charu. "Screens as Gestures in Interactive Art Assemblage." AM Journal of Art and Media Studies 17 (2018): 147−155. doi: 10.25038/am.v0i17.278

scholarly journals Artificial Intelligence Driven Crypto Currencies

2020 ◽  
Vol 9 (2) ◽  
pp. 107-118
Author(s):  
Apoorva Ganapathy ◽  
Md. Redwanuzzaman ◽  
Md. Mahbubur Rahaman ◽  
Wahiduzzaman Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Technological Development ◽  
Third Parties ◽  
Human Intelligence ◽  
Human Intervention ◽  
Specific Data ◽  
Development Framework ◽  
The Future

Artificial intelligence-driven cryptocurrencies are cryptocurrencies created by Artificial intelligence using the traditional human cryptocurrency development framework without human intervention. An AI explores the data from each different stream and arriving at the framework which can host these cryptocurrencies following the standards of legality. Cryptography is the encryption of specific data to conceal it and keep it a secret from unwanted third parties. Cryptocurrencies are encrypted currencies with unique keys as developed by developers. Artificial intelligence is an advanced machine programmed to simulate and emulate human intelligence by carrying tasks and reaching conclusions with little or no human intervention. This work considered the use of AI through machine learning and deep learning in the development of cryptocurrencies. The AI machine will set all the parameters and structure of the cryptocurrency. This will include how data is added, removed, and verified on the stream. Blockchain is an open ledger of a cryptocurrency's transactions. It stores files in the system, arranged in blocks, and connected on a list called chains. The article considers how AI-driven cryptocurrency will run using the blockchain network and its impact on it. Artificial intelligence and cryptocurrency are technological very essential technological development currently. The effect of the combination of both technologies would be enormous in the future as both technologies will develop each other remarkably.  

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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