When two science disciplines meet: Evaluating dynamics of conjunction. The encounter between astrophysics and artificial intelligence

2021 ◽  
pp. 053901842110258
Author(s):  
Anne Marcovich ◽  
Terry Shinn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Mind ◽  
Mathematical Tool ◽  
Reliability Of Results ◽  
Science Disciplines

This article points out some issues raised by the encounter between astrophysics (AP) and a newly emergent mathematical tool/discipline, namely artificial intelligence (AI). We suggest that this encounter has interesting consequences in terms of science evaluation. Our discussion favors an intra science perspective, both on the institutional and cognitive side. This encounter between machine learning (ML) and astrophysics points to three different consequences. (1) As a transverse tool, a same ML algorithm can be used for a diversity of very different disciplines and questions. This ambition and analytic intellectual architecture frequently identify similarities among apparently differentiated fields. (2) The perimeter of the disciplines involved in a research can lead to many and novel ways of collaboration between scientists and to new ways of evaluation of their work. And (3), the impossibility for the human mind to understand the processes involved in ML work raises the question of the reliability of results.

scholarly journals Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence

2021 ◽  
Author(s):  
J. Eric T. Taylor ◽  
Graham Taylor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Neural Networks ◽  
Experimental Approach ◽  
Well Being ◽  
Human Mind ◽  
Black Box ◽  
Psychological Science ◽  
Black Boxes ◽  
Explainable Artificial Intelligence

Artificial intelligence powered by deep neural networks has reached a levelof complexity where it can be difficult or impossible to express how a modelmakes its decisions. This black-box problem is especially concerning when themodel makes decisions with consequences for human well-being. In response,an emerging field called explainable artificial intelligence (XAI) aims to increasethe interpretability, fairness, and transparency of machine learning. In thispaper, we describe how cognitive psychologists can make contributions to XAI.The human mind is also a black box, and cognitive psychologists have overone hundred and fifty years of experience modeling it through experimentation.We ought to translate the methods and rigour of cognitive psychology to thestudy of artificial black boxes in the service of explainability. We provide areview of XAI for psychologists, arguing that current methods possess a blindspot that can be complemented by the experimental cognitive tradition. Wealso provide a framework for research in XAI, highlight exemplary cases ofexperimentation within XAI inspired by psychological science, and provide atutorial on experimenting with machines. We end by noting the advantages ofan experimental approach and invite other psychologists to conduct research inthis exciting new field.

scholarly journals Governance and Ethics of AI

2021 ◽  
Vol 9 (11) ◽  
pp. 1467-1471
Author(s):  
Atharv Jangam
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer System ◽  
Human Mind ◽  
Privacy And Security ◽  
Autonomous Machine

Abstract: In this article we discuss ways AI can be fruitful and inimical at the same time and consider hurdles in implementing ethics and governance of AI. We conclude with presenting solutions to overcome this issue. Artificial intelligence (AI) is a technology that allows a computer system to mimic the human mind. AI, like humans, is capable of learning and developing itself through doing tasks such as planning, organizing, and executing numerous activities. However, as we develop and expand our understanding of AI, there are a few advantages and downsides that should be addressed. Privacy and security are vital, but they conflict with the advancement of AI technology since computers and AI require a large quantity of data to Comprehend and anticipate outcomes. With the advancement of technology, we should be able to maximize security and eliminate the current drawbacks. Keywords: Artificial Intelligence (AI), Autonomous, Machine Learning (ML), Governance, Ethics, Deepfake

Machine Learning in Healthcare

2020 ◽  
pp. 92-109
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Human Brain ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Definition Of

Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

scholarly journals The Scope and Applications of Artificial Intelligence in the Medical Sector

2021 ◽  
pp. 18-25
Author(s):  
Madhuri Kumar ◽  
John Alen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Mind ◽  
Major Research ◽  
Medical Field ◽  
Computing Systems ◽  
Medical Practices ◽  
Medical Sector ◽  
Effective Delivery ◽  
Smart Machines

The terminology Artificial Intelligence (AI) describes the application computing systems and technology to effectively simulate smart actions and smart thinking compared to the human mind. The concept of AI was introduced as the engineering and science of making smart machines that can operate without the engagement of humans using Machine Learning (ML). This research provides a wider scope of the concept of AI in the medical field, handling the various concepts and terms associated with the concept, including the present and future implementation of the concept. The major research materials applied are Google and PubMed searches, which were conducted using the “Artificial Intelligence” as the basic keyword. More references were retrieved by cross-referencing major publications. The advancements in AI technology in recent times and the present application of medicine have been analyzed critically. This paper ends with an assumption that AI focuses on implementing changes in the medical practices in previously unidentified ways. However, many of the application are still in the initial stages and require exploration and development. In addition, clinical experts have to comprehend and adapt with development for effective delivery of medical services.

Machine Learning in Healthcare, Introduction and Real World Application Considerations

2018 ◽  
Vol 7 (2) ◽  
pp. 27-36 ◽  
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Real World Application ◽  
Definition Of

Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

Machine Learning in Healthcare, Introduction and Real World Application Considerations

2020 ◽  
pp. 13-23
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Real World Application ◽  
Definition Of

Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

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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