scholarly journals Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)

2020 ◽  
Vol 25 (1) ◽  
pp. 74-88 ◽  
Author(s):  
S Shyam Sundar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Media Effects ◽  
Interactive Media ◽  
Dual Process ◽  
User Perceptions ◽  
Human Control ◽  
Process Framework ◽  
Applications Of Machine Learning ◽  
Media And Communication

Abstract Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.

scholarly journals Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2882
Author(s):  
Thi Thu Em Vo ◽  
Hyeyoung Ko ◽  
Jun-Ho Huh ◽  
Yonghoon Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sustainable Development ◽  
Species Classification ◽  
The Sustainable Development ◽  
Aquaculture System ◽  
Aquaculture Industry ◽  
Intelligent Technology ◽  
Applications Of Machine Learning ◽  
Aquaculture Production

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

Introduction to Machine Learning and Its Application

2020 ◽  
pp. 262-290
Author(s):  
Ladly Patel ◽  
Kumar Abhishek Gaurav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Algorithm ◽  
Past Data ◽  
Applications Of Machine Learning ◽  
Learning Frameworks ◽  
Learning Data

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

Role of Artificial Intelligence in Modeling Psychometrics to Detect Neurodevelopmental Disorders

2020 ◽  
pp. 213-227
Author(s):  
Navjot Singh ◽  
Amarjot Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodevelopmental Disorders ◽  
Machine Learning Techniques ◽  
Diagnosis And Prognosis ◽  
Processing Power ◽  
Learning Techniques ◽  
Numerous Data ◽  
Applications Of Machine Learning ◽  
Micro Patterns

The objective of the present chapter is to highlight applications of machine learning and artificial intelligence (AI) in clinical diagnosis of neurodevelopmental disorders. The proposed approach aims at recognizing behavioral traits and other cognitive aspects. The availability of numerous data and high processing power, such as graphic processing units (GPUs) or cloud computing, enabled the study of micro-patterns hundreds of times faster compared to manual analysis. AI, being a new technological breakthrough, enables study of human behavior patterns, which are hidden in millions of micro-patterns originating from human actions, reactions, and gestures. The chapter will also focus on the challenges in existing machine learning techniques and the best possible solution addressing those problems. In the future, more AI-based expert systems can enhance the accuracy of the diagnosis and prognosis process.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

scholarly journals The other Cambridge analytics: Early ‘artificial intelligence’ in American Political Science

2020 ◽  
Author(s):  
Fenwick McKelvey
Keyword(s):  
Social Sciences ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
The Social ◽  
Political Management ◽  
American Academia ◽  
Political Epistemology ◽  
Applications Of Machine Learning ◽  
American Political Science

Harold Lasswell, quoted in a 1961 issue of Harper’s Magazine, described the Simulmatics Corporation as the “A-bomb of the social sciences.” Simulmatics had attracted his attention after publicizing its use of computer modeling to predict public opinion for the 1960 Kennedy Presidential Campaign. A preeminent figure in the American academia, Lasswell’s quotes reflects the long promise of “artificial intelligence” in a broad sense as a technology to better know politics and populations. Simulmatics was one application of this research agenda developed at MIT along with Project Cambridge. These under-studied cases are a needed counterpoint to theorize the contemporary applications of machine learning and deep learning for political management as popularized by the defunct psychographics firm Cambridge Analytica.Building on the pre-conference’s periodization of AI from rule-based to today’s temporal flows of classifications, I distinguish modern AI epistemology (machine learning and deep learning) from its predecessors through two key applications at MIT, the Simulmatics Corporation and its academic equivalent Project Cambridge. Drawing on archival research, I analyze the constitutive discourses that formulated the problems to be solved and the artifacts of code that actualized these projects. Simulmatics Corporation and Project Cambridge marked an important passage point of the cyborg sciences into politics and governance, integrating behaviouralism with mathematical modeling in hopes of rendering populations more knowable and manageable. In doing so, these other analytics at Cambridge erased the boundaries between artificial intelligence and political intelligence, an erasure necessary for AI to be seen as a political epistemology today.

scholarly journals IoTs, Machine Learning (ML), AI and Digital Transformation Affects Various Industries - Principles and Cybersecurity Risks Solutions

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 501-513
Author(s):  
Nguyen Dinh Trung ◽  
Dinh Tran Ngoc Huy ◽  
Trung-Hieu Le
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Data ◽  
Digital Transformation ◽  
Development Path ◽  
Public Health Data ◽  
Infrared Rays ◽  
Manufacturing Sectors ◽  
Applications Of Machine Learning

Our purpose to conduct this research is that we would like to present advantages and applications of internet of things (IoTs), Machine learning (ML), AI - Artificial intelligence and digital transformation in Education, Medicine-hospitals, Tourism and Manufacturing Sectors. In this paper authors will use methods such as empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that in education sector, ML and IoTs and AI has affected methods of teaching and methods of evaluating students in classroom and from then, teachers or instructors can decide suitable career development path for learners. Last but not least, ML and IoTs and AI together also has certain impacts in hospitals and medicine sector where public health data and patients information and diseases information are recorded and processed faster with Big Data. Till the end, we have enough information to propose implications for future researches on applications of machine learning in each specific sector and also, cybersecurity Risk management also need for implementing and applying ML and IoTs and AI.

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