scholarly journals Dimension-Based Interactions with Virtual Assistants: A Co-Design Project with Design Fictions

2021 ◽  
Vol 5 (12) ◽  
pp. 78
Author(s):  
Hebitz C. H. Lau ◽  
Jeffrey C. F. Ho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Process ◽  
End Users ◽  
Daily Lives ◽  
Design Project ◽  
And Function ◽  
Future Technologies ◽  
Design Fiction ◽  
Unrealistic Expectations

This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs.

scholarly journals Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

2021 ◽  
Author(s):  
◽  
Hazel Darney
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gender Bias ◽  
Relevant Literature ◽  
Interdisciplinary Approach ◽  
High Stakes ◽  
Daily Lives ◽  
Science Field ◽  
Gender Biases ◽  
Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men.    Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human.   The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

scholarly journals Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

2021 ◽  
Author(s):  
◽  
Hazel Darney
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gender Bias ◽  
Relevant Literature ◽  
Interdisciplinary Approach ◽  
High Stakes ◽  
Daily Lives ◽  
Science Field ◽  
Gender Biases ◽  
Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men.    Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human.   The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

Design of diversified teaching platform of college aerobics course based on artificial intelligence

2021 ◽  
pp. 1-13
Author(s):  
Fengping Huang
Keyword(s):  
Artificial Intelligence ◽  
Design Process ◽  
Experimental Results ◽  
Average Score ◽  
Network Teaching ◽  
Teaching Platform ◽  
Student Grouping ◽  
Function Module ◽  
And Function

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.

scholarly journals Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Supraja Sankaran ◽  
Chao Zhang ◽  
Henk Aarts ◽  
Panos Markopoulos
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Social Media ◽  
Future Development ◽  
Human Interaction ◽  
Potential Threat ◽  
Daily Lives ◽  
The Social ◽  
Human Autonomy ◽  
Design Fiction

Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Author(s):  
Bongs Lainjo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Abstract Background: Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. Method: This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Results: Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. Conclusion: The vulnerabilities that come with the successful implementation of IoT systems are highlighted including the successes currently achieved by institutions promoting the benefits of IoT-related systems like the case study. Security continues to be an issue of significant importance.

Artificial Intelligence to Super Artificial Intelligence, Cyber Culture to Transhumanist Culture

2019 ◽  
pp. 1-16 ◽  
Author(s):  
Serap Uğur ◽  
Gulsun Kurubacak
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
Cultural Change ◽  
Mobile Technologies ◽  
Daily Lives ◽  
Wearable Technologies ◽  
Cyber Culture ◽  
Intelligence Studies ◽  
Technological Developments ◽  
Future Technologies

The 21st century is a period in which technological developments constantly present new innovations. This broad spectrum, from computers to mobile technologies, from augmented reality to virtual reality, from wearable technologies to artificial intelligence, is radically changing societies' economies, education systems, governments, and even cultures. Artificial intelligence studies have reached a point where people discuss how a human-like intelligence would be. This leads to the emergence of systems that regulate the work, daily lives, communications, jobs, and even family budgets of people. These developments also change the living standards and styles of individuals. The cultural texture that technology has restructured is now called “cyber” beyond digital. But how will the future technologies affect this cultural change and what kind of change awaits?

Artificial Intelligence

2021 ◽  
pp. 1-18
Author(s):  
Bistra Konstantinova Vassileva
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Future Development ◽  
Critical Review ◽  
Data Science ◽  
Daily Lives ◽  
Societal Relevance ◽  
Responsible Manner

In recent years, artificial intelligence (AI) has gained attention from policymakers, universities, researchers, companies and businesses, media, and the wide public. The growing importance and relevance of artificial intelligence (AI) to humanity is undisputed: AI assistants and recommendations, for instance, are increasingly embedded in our daily lives. The chapter starts with a critical review on AI definitions since terms such as “artificial intelligence,” “machine learning,” and “data science” are often used interchangeably, yet they are not the same. The first section begins with AI capabilities and AI research clusters. Basic categorisation of AI is presented as well. The increasing societal relevance of AI and its rising inburst in our daily lives though sometimes controversial are discussed in second section. The chapter ends with conclusions and recommendations aimed at future development of AI in a responsible manner.

scholarly journals Artificial Neural Networks in Agriculture

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Sebastian Kujawa ◽  
Gniewko Niedbała
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transport Processes ◽  
High Quality Research ◽  
Wide Range ◽  
Brain Structure And Function ◽  
Artificial Neural ◽  
And Function

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

scholarly journals Machine learning in dental, oral and craniofacial imaging: a review of recent progress

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11451
Author(s):  
Ruiyang Ren ◽  
Haozhe Luo ◽  
Chongying Su ◽  
Yang Yao ◽  
Wen Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Imaging ◽  
Medical Diagnosis ◽  
Recent Progress ◽  
Medical Science ◽  
Research Progress ◽  
Daily Lives ◽  
Major Application ◽  
Machine Learning Models

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

scholarly journals The Impact of Technology Adoption on the Success and Failure of Two Toys Industry: Hasbro and Toys R Us

2021 ◽  
pp. 96-103
Author(s):  
Hooi Kun Lee ◽  
Abdul Rafiez Abdul Raziff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Control ◽  
Technology Adoption ◽  
Social Development ◽  
Digital Transformation ◽  
Daily Lives ◽  
The World ◽  
Impact Of Technology ◽  
The Impact

The value of play has mainly stayed consistent throughout time. Playing is, without a doubt, one of the essential things we can do. Playing in addition to supporting motor, neurological, and social development improves adaptation by encouraging people to explore diverse perspectives on the world and assisting them in developing methods for dealing with problems in a safe setting. The way we play and what we play with have been heavily affected by the quickly evolving technology shaping our daily lives. Artificial intelligence (A.I.) is now found in many products, including vehicles, phones, and vacuum cleaners. This extends to children's items, with the creation of an "Internet of Toys." Many learning, remote control, and app-integrated toys include innovative playthings that employ speech recognition and machine learning to communicate with users. This study examines the impact of technology adoption on the success and failure of two toys industry – Hasbro, Inc and Toys R Us, Inc. The research methodology of this study is based on case studies where the comparison of the two industries was made from a few areas. The finding of the study determines that corporations that evolved consistently with the change of technology will continue to grow in the market. In contrast, the corporation that failed to adopt digital transformation will be a force out of the market.

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