scholarly journals The A.I. storyteller: Perpetuating orality using developments in machine learning

2021 ◽  
Author(s):  
Soo Anne Mahabir Mahabir
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Physical Computing ◽  
Generative Art ◽  
Artificial Intelligence Research ◽  
Theatrical Design ◽  
Video Recordings ◽  
Art Philosophy ◽  
Key Aspects

Oral histories are a part of all cultures and societies, however, our knowledge and interest in these practices has waned, and arguably with it, a sense of social identity and belonging in many contemporary communities and cultures. This paper pulls from aspects of experiential and theatrical design, generative art philosophy, physical computing, and machine learning in artificial intelligence research combined with a theoretical foundation of Adorno, Benjamin, and Ong to discuss and propose the creation of an embodied and immersive story experience. This project will overturn key aspects of traditional orality to encourage interactivity with, and ownership of, the stories and will prompt discussion about its use as an archival process that will promote perpetuation rather than preservation, moving beyond the current processes of audio and video recordings.

scholarly journals The A.I. storyteller: Perpetuating orality using developments in machine learning

2021 ◽  
Author(s):  
Soo Anne Mahabir Mahabir
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Physical Computing ◽  
Generative Art ◽  
Artificial Intelligence Research ◽  
Theatrical Design ◽  
Video Recordings ◽  
Art Philosophy ◽  
Key Aspects

Oral histories are a part of all cultures and societies, however, our knowledge and interest in these practices has waned, and arguably with it, a sense of social identity and belonging in many contemporary communities and cultures. This paper pulls from aspects of experiential and theatrical design, generative art philosophy, physical computing, and machine learning in artificial intelligence research combined with a theoretical foundation of Adorno, Benjamin, and Ong to discuss and propose the creation of an embodied and immersive story experience. This project will overturn key aspects of traditional orality to encourage interactivity with, and ownership of, the stories and will prompt discussion about its use as an archival process that will promote perpetuation rather than preservation, moving beyond the current processes of audio and video recordings.

Where the Sun never Shines

2018 ◽  
Vol 4 (1) ◽  
pp. 133-154
Author(s):  
Johannes Bruder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Neuroscience ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Artificial Intelligence Research ◽  
Creative Cognition ◽  
Long Time ◽  
Creative Problem ◽  
Spontaneous Thought

Abstract In this paper, I elaborate on deliberations of “post-enlightened cognition” between cognitive neuroscience, psychology and artificial intelligence research. I show how the design of machine learning algorithms is entangled with research on creativity and pathology in cognitive neuroscience and psychology through an interest in “episodic memory” and various forms of “spontaneous thought”. The most prominent forms of spontaneous thought - mind wandering and day dreaming - appear when the demands of the environment abate and have for a long time been stigmatized as signs of distraction or regarded as potentially pathological. Recent research in cognitive neuroscience, however, conceptualizes spontaneous thought as serving the purpose of, e. g., creative problem solving and hence invokes older discussions around the links between creativity and pathology. I discuss how attendant attempts at differentiating creative cognition from its pathological forms in contemporary psychology, cognitive neuroscience, and AI puts traditional understandings of rationality into question.

scholarly journals Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

2021 ◽  
Vol 14 (7) ◽  
pp. 645
Author(s):  
Shaymaa A. Abd-algaleel ◽  
Hend M. Abdel-Bar ◽  
Abdelkader A. Metwally ◽  
Rania M. Hathout
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Molecular Dynamic ◽  
Docking Studies ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations ◽  
Polymeric Nanocarriers ◽  
Modeling And Prediction ◽  
Wet Lab ◽  
Key Aspects

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

scholarly journals Mapping the landscape of Artificial Intelligence applications against COVID-19

2020 ◽  
Vol 69 ◽  
pp. 807-845 ◽  
Author(s):  
Joseph Bullock ◽  
Alexandra Luccioni ◽  
Katherine Hoffman Pham ◽  
Cynthia Sin Nga Lam ◽  
Miguel Luengo-Oroz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Open Science ◽  
Data Sources ◽  
World Health ◽  
Artificial Intelligence Research ◽  
Non Invasive ◽  
Multiple Data ◽  
The World ◽  
Health Organization

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

scholarly journals Research of approaches and methods of applying artificial intelligence and machine learning in socio-economic processes

2020 ◽  
Vol 18 (2) ◽  
pp. 65-79
Author(s):  
Darya Yu. Sakhanevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Social Economic ◽  
Historical Period ◽  
Model Data ◽  
Machine Learning Methods ◽  
The Right ◽  
One Machine ◽  
Statistical Indicators

One of the problems hindering the development of the socio-economic sphere in the innovative direction is the lack of structuring of approaches and methods used in machine learning as part of the introduction of artificial intelligence (AI) in socio-economic processes. The same problem hinders the growth of the pace of innovative development and, as a result, the improvement of the scientific and technical level of the country. The article classifies and systematizes aspects of machine learning, focuses on the need to accelerate the construction and implementation of algorithms as the basis of AI for increasing the efficiency of managing socio-economic processes. To achieve this goal, the following results are presented: analysis of the concepts of machine learning and AI, study of analytical materials regarding approaches and methods to the introduction of artificial intelligence and prospects for its application in socio-economic processes. There were systematized approaches to machine learning introduction to artificial intelligence depending on the historical period, the implementation of AI, and another, and methods according to the method of machine learning, predictive model data for creating AI algorithms (e.g., probabilistic), and the idea or the nature of the research that uses this technology (assessment and collection of statistical indicators, analysis). The study of the material related to machine learning and AI construction allowed us to draw the following conclusions. The theoretical foundation in the form of mathematical and statistical methods as the basis for building algorithms for creating AI in the framework of machine learning is a necessary part of the process of teaching computers human qualities. However, information about machine learning methods and approaches is mostly scattered, and it is necessary to form a unified methodological base in order to simplify the stage of searching for the right method of creating AI to solve any social, economic or other problem. The presence of such a database will create opportunities to replace one machine learning method for creating AI with another in different fields of activity and socio-economic processes.

scholarly journals ARTIFICIAL INTELLIGENCE IN COMPUTER VISION

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Aryan Karn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Theoretical Foundation ◽  
Statistical Technique ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Interdisciplinary Field ◽  
The World ◽  
Abstract Level

Computer vision is an area of research concerned with assisting computers in seeing. Computer vision issues aim to infer something about the world from observed picture data at the most abstract level. It is a multidisciplinary subject that may be loosely classified as a branch of artificial intelligence and machine learning, both of which may include using specific techniques and using general-purpose learning methods. As an interdisciplinary field of research, it may seem disorganized, with methods taken and reused from various engineering and computer science disciplines. While one specific vision issue may be readily solved with a hand-crafted statistical technique, another may need a vast and sophisticated ensemble of generic machine learning algorithms. Computer vision as a discipline is at the cutting edge of science. As with any frontier, it is thrilling and chaotic, with often no trustworthy authority to turn to. Numerous beneficial concepts lack a theoretical foundation, and some theories are rendered ineffective in reality; developed regions are widely dispersed, and often one seems totally unreachable from the other.

scholarly journals Artificial intelligence in healthcare: possibilities of patent protection

2021 ◽  
Vol 14 (2) ◽  
pp. 270-276
Author(s):  
T. N. Erivantseva ◽  
Yu. V. Blokhina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patent Protection ◽  
Legal Protection ◽  
Patent Law ◽  
Process Automation ◽  
Scientific Publications ◽  
Key Aspects ◽  
Modern Technologies

The article provides an overview of the advantages and issues associated with the use of artificial intelligence (AI) and machine learning (ML) in medicine. Based on the analysis of scientific publications, the leading healthcare areas using AI and ML have been identified. The applied problems that modern technologies allow to solve are described, as well as the goals that can be achieved using such technologies. The legal protection issues of technologies using AI are highlighted. A comparison is given of the key aspects of copyright and patent law, and the advantages of patent law and comprehensive patent protection of technologies for process automation in healthcare are presented. The possibilities of complex patent protection and its strategy in the leading areas of AI use in healthcare are considered on specific examples.

scholarly journals Automated Creation of Expert Systems with the InteKRator Toolbox

2021 ◽  
Author(s):  
Daan Apeldoorn ◽  
Torsten Panholzer
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Medical Informatics ◽  
Learning Approaches ◽  
Artificial Intelligence Research ◽  
New Evidence

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning.

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