scholarly journals AI in learning

2021 ◽  
Vol 15 ◽  
pp. 183449092110381 ◽  
Author(s):  
Hannele Niemi
Keyword(s):  
Machine Learning ◽  
Ethical Issues ◽  
Cognitive Task ◽  
Future Research ◽  
Human Beings ◽  
Special Issue ◽  
Recent Discussion ◽  
Data Ownership ◽  
Digital Pedagogy ◽  
Intelligent Tools

This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher’s role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

Synthesis: foraging decisions link plants, herbivores and human beings

2015 ◽  
Vol 55 (3) ◽  
pp. 411 ◽  
Author(s):  
F. D. Provenza ◽  
P. Gregorini ◽  
P. C. F. Carvalho
Keyword(s):  
Foraging Behaviour ◽  
Food Selection ◽  
Secondary Compounds ◽  
Social Benefits ◽  
Future Research ◽  
Social Environments ◽  
Human Beings ◽  
Special Issue ◽  
Foraging Decisions ◽  
Temporal And Spatial

Herbivores make decisions about where to forage and what combinations and sequences of foods to eat, integrating influences that span generations, with choices manifest daily within a lifetime. These influences begin in utero and early in life; they emerge daily from interactions among internal needs and contexts unique to biophysical and social environments; and they link the cells of plants with the palates of herbivores and humans. This synthesis summarises papers in the special issue of Animal Production Science that explore emerging understanding of these dynamics, and suggests implications for future research that can help people manage livestock for the benefit of landscapes and people by addressing (1) how primary and secondary compounds in plants interact physiologically with cells and organs in animals to influence food selection, (2) temporal and spatial patterns of foraging behaviours that emerge from these interactions in the form of meal dynamics across landscapes, (3) ways humans can manage foraging behaviours and the dynamics of meals for ecological, economic and social benefits, and (4) models of foraging behaviour that integrate the aforementioned influences.

scholarly journals Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 199 ◽  
Author(s):  
Yash Raj Shrestha ◽  
Yongjie Yang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Recommender Systems ◽  
Minority Groups ◽  
Ethical Issues ◽  
Fixed Number ◽  
Future Research ◽  
Design Of Algorithms ◽  
Voting Machine

Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.

Applying Rule Induction in Software Prediction

2011 ◽  
pp. 265-286
Author(s):  
Bhekisipho Twala ◽  
Michelle Cartwright ◽  
Martin Shepperd
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Software Engineering ◽  
Principal Components ◽  
Research Work ◽  
Rule Induction ◽  
Future Research ◽  
Human Beings ◽  
Data Application ◽  
Key Issues

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, for example, cost and defect processes. An important advantage of machine learning over statistical analysis as a modelling technique lies in the fact that the interpretation of production rules is more straightforward and intelligible to human beings than, say, principal components and patterns with numbers that represent their meaning. The main focus of this chapter is upon rule induction (RI): providing some background and key issues on RI and further examining how RI has been utilised to handle uncertainties in data. Application of RI in prediction and other software engineering tasks is considered. The chapter concludes by identifying future research work when applying rule induction in software prediction. Such future research work might also help solve new problems related to rule induction and prediction.

Pengaruh Lintas Budaya Pada Pemasaran Internasional Dengan Pendekatan Perilaku Konsumen

2017 ◽  
Vol 1 (2) ◽  
pp. 168-176
Author(s):  
Endy Gunanto ◽  
Yenni Kurnia Gusti
Keyword(s):  
Consumer Behavior ◽  
Marketing Strategy ◽  
Consumer Culture ◽  
International Organization ◽  
Future Research ◽  
Special Issue ◽  
Cross Culture ◽  
Several Variables ◽  
Cultural Orientations ◽  
The Impact

In this article we present a conceptual of the effect of cross culture on consumer behavior incorporating the impact of globalization. This conceptual idea shows that culture inûuences various domains of consumer behavior directly as well as through international organization to implement marketing strategy. The conceptual identify several factors such as norm and value in the community, several variables and also depicts the impact of other environmental factors and marketing strategy elements on consumer behavior. We also identify categories of consumer culture orientation resulting from globalization. Highlights of each of the several other articles included in this special issue in Asia region. We conclude with the contributions of the articles in terms of the consumer cultural orientations and identify directions for future research.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

The Changing Nature of Knowledge and Service Work in the Age of Intelligent Machines

2020 ◽  
pp. 343-368
Author(s):  
Crispin Coombs ◽  
Donald Hislop ◽  
Stanimira Taneva ◽  
Sarah Barnard
Keyword(s):  
Ethical Issues ◽  
Work Practice ◽  
Academic Disciplines ◽  
Service Work ◽  
Future Research ◽  
Human Capabilities ◽  
Academic Knowledge ◽  
Intelligent Machines ◽  
Technological Developments ◽  
The Impact

One of the most significant recent technological developments concerns the application of intelligent machines to jobs that up to now have been considered safe from automation. These changes have generated considerable debate regarding the impacts that the widespread adoption of intelligent machines could have on the nature of work. This chapter provides a thematic review, across multiple academic disciplines, of the current state of academic knowledge regarding the impact of intelligent machines on knowledge and service work. Adopting a work-practice perspective, the chapter reviews the extant literature concerning changing relations between workers and intelligent machines, the adoption and acceptance of intelligent machines, and ethical issues associated with greater machine human collaboration. A key finding is that much of the research discusses intelligent machines complementing and extending human capabilities rather than removing humans from work processes. The concept of augmentation of humans and human work, rather than wholesale replacement from automation, flows through the literature across a range of domains. The chapter concludes with a discussion of the main gaps in existing knowledge and ways in which future research may provide a deeper understanding of how people (currently and in the near future) experience intelligent machines in their day-to-day work practice. These include the need for multi-disciplinary research, the role of contexts, the need for more and better empirical research, the changing relationships between humans and intelligent machines, the adoption and acceptance of the technology, and ethical issues.

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