scholarly journals Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job

2018 ◽  
Vol 40 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Peter Fleming
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Justice ◽  
Positive Development ◽  
Public Organization ◽  
Organization Studies ◽  
Scholarly Community ◽  
High Profile ◽  
Employment Sector ◽  
Near Future

A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won’t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of ‘bounded automation’ to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

scholarly journals Discovering hidden information in biosignals from patients using artificial intelligence

2020 ◽  
Vol 73 (4) ◽  
pp. 275-284
Author(s):  
Dukyong Yoon ◽  
Jong-Hwan Jang ◽  
Byung Jin Choi ◽  
Tae Young Kim ◽  
Chang Ho Han
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Condition ◽  
Real Life ◽  
Black Box ◽  
Basic Knowledge ◽  
Hidden Information ◽  
Near Future ◽  
Invasive Evaluation

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

Development of an Artificial Intelligence Education Model of Classification Techniques for Non-computer Majors

2021 ◽  
Vol 5 (2) ◽  
pp. 113
Author(s):  
Youngseok Lee ◽  
Jungwon Cho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Model Concept ◽  
Core Technology ◽  
Machine Learning Model ◽  
The Social ◽  
Education Model ◽  
Intelligence Education ◽  
Near Future

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. AI is being rapidly applied across industries and is developing as a core technology to enable a society led by knowledge and information. An AI education focused on problem solving and learning is efficient for computer science education. Thus, the time has come to prepare for AI education along with existing software education so that they can adapt to the social and job changes enabled by AI. In this paper, we explain a classification method for AI machine learning models and propose an AI education model using teachable machines. Non-computer majors can understand the importance of data and the AI model concept based on specific cases using AI education tools to understand and experiment with AI even without the knowledge of mathematics, and use languages such as Python, if necessary. Through the application of the machine learning model, AI can be smoothly utilized in their field of interest. If such an AI education model is activated, it will be possible to suggest the direction of AI education for collaboration with AI experts through the application of AI technology.

scholarly journals Artificial Intelligence Model of Drive-Through Vaccination Simulation

2020 ◽  
Vol 18 (1) ◽  
pp. 268
Author(s):  
Ali Asgary ◽  
Svetozar Zarko Valtchev ◽  
Michael Chen ◽  
Mahdi M. Najafabadi ◽  
Jianhong Wu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
General Public ◽  
Mass Vaccination ◽  
Simulation Tool ◽  
Online Application ◽  
Machine Learning Model ◽  
Different Types ◽  
Near Future ◽  
Rapid Mass

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

scholarly journals A Study on the Awareness of Artificial Intelligence Among Youth and Its Impact on Employment

2021 ◽  
pp. 461-465
Author(s):  
Ms. Aswathy K A ◽  
Dr. Rosalind Gonzaga ◽  
Ms. Josna Susan Francis
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Secondary Analysis ◽  
Negative Attitude ◽  
Computer Programs ◽  
Skilled Labor ◽  
Intelligent Machines ◽  
Employment Sector ◽  
Near Future ◽  
Intelligent Computer

Artificial Intelligence (A.I.) is the Engineering and Science of making Intelligent Machines and Intelligent Computer Programs. It develops Intelligence in Machines and enables them to think like Humans. The study was conducted to find out level of awareness about artificial intelligence among youth and its impact on employment. From the study it was found that artificial intelligence is career threatening and the youth shows a negative attitude towards it. As a result of development of artificial intelligence the employment sector now demands more technological skills from the employees. It was found that majority of the respondents are of the opinion that artificial intelligence will replace the human workers in the near future. It is evident from the secondary analysis that low skilled jobs are at risk due to artificial intelligence, because the replacement is more in low skilled jobs as compared to high skilled jobs. It can be conclude that , demand of skilled jobs is on the rise and there might be a temporary shortage of skilled labor but if the current pace of development in education and retraining continues it will be resolved soon.

Technology and social cohesion: deploying artificial intelligence in mediating herder-farmer conflicts in Nigeria

2021 ◽  
Vol 9 (3) ◽  
pp. 15-32
Author(s):  
Adeolu Oluwaseyi Oyekan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Cohesion ◽  
Social Transformation ◽  
Indigenous Communities ◽  
Political Dimension ◽  
Political Consequences ◽  
Security Challenges ◽  
Innovative Thinking ◽  
Near Future

This paper argues for the role of technology, such as artificial intelligence, which includes machine learning, in managing conflicts between herders and farmers in Nigeria. Conflicts between itinerant Fulani herders and farmers over the years have resulted in the destruction of lives, properties, and the displacement of many indigenous communities across Nigeria, with devastating social, economic and political consequences. Over time, the conflicts have morphed into ethnic stereotypes, allegations of ethnic cleansing, forceful appropriation and divisive entrenchment of labels that are inimical to national existence. The reality of climate change and increased urbanization suggest that conflicts are likely to exacerbate over shrinking resources in the near future. Finding solutions to the conflicts, therefore requires innovative thinking capable of addressing the limits of past approaches. While mindful of the human and political dimension of the conflicts, I argue using the method of philosophical analysis that technology possesses the capacity for social transformation, and make a case for the modernization of grazing culture and the curbing of crossborder grazing through machine learning (ML) and other forms of artificial intelligence. Machine Learning represents a transformative technology that addresses the security challenges of irregular migration, accommodates the nomadic and subsistent mode of farming associated with the conflicting parties while enabling a gradual but stable transition to full modernization. I conclude that machine learning holds many prospects for minimizing conflicts and attaining social cohesion between herders and farmers when properly complemented by other mechanisms of social cohesion that may be political in nature.

scholarly journals A review of the use of artificial intelligence in orthodontics

2021 ◽  
Vol 38 (SI-2) ◽  
pp. 157-162
Author(s):  
Serdar AKDENİZ ◽  
Muhammet Emir TOSUN
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Current Literature ◽  
Treatment Process ◽  
Review Article ◽  
Clinical Use ◽  
Efficient Treatment ◽  
Artificial Intelligence Technology ◽  
Near Future

The clinical use of artificial intelligence technology in orthodontics has increased significantly in recent years. Artificial intelligence can be utilized in almost every part of orthodontic workflow. It is an important decision making aid as well as a tool for building more efficient treatment mechanics. The use of artificial intelligence reduces costs, speeds up the diagnosis and treatment process and reduces or even eliminates the need for manpower. This review article evaluates the current literature on artificial intelligence and machine learning in the field of orthodontics. The areas that the artificial intelligence is still lacking were also discussed in detail. Despite its shortcomings, artificial intelligence is considered to have an integral part of orthodontic practice in the near future.

scholarly journals Application of Artificial Intelligence to Address Issues Related to the COVID-19 Virus

2021 ◽  
pp. 247263032098381
Author(s):  
M. Senthilraja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Vaccine Development ◽  
De Novo ◽  
Biologically Active ◽  
Treatment Regimens ◽  
Synthesis Planning ◽  
Novel Coronavirus ◽  
Near Future

Artificial intelligence (AI) plays a major role in addressing novel coronavirus 2019 (COVID-19)-related issues and is also used in computer-aided synthesis planning (CASP). AI, including machine learning, is used by artificial neural networks such as deep neural networks and recurrent networks. AI has been used in activity predictions like physicochemical properties. Machine learning in de novo design explores the generation of fruitful, biologically active molecules toward expected or finished products. Several examples establish the strength of machine learning or AI in this field. AI techniques can significantly improve treatment consistency and decision making by developing useful algorithms. AI is helpful not only in the treatment of COVID-19-infected patients but also for their proper health monitoring. It can track the crisis of COVID-19 at different scales, such as medical, molecular, and epidemiological applications. It is also helpful to facilitate the research on this virus by analyzing the available data. AI can help in developing proper treatment regimens, prevention strategies, and drug and vaccine development. Combination with synthesis planning and ease of synthesis are feasible, and more and more automated drug discovery by computers is expected in the near future to eradicate the COVID-19 virus.

scholarly journals Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner’s hype cycle

2020 ◽  
Vol 5 (10) ◽  
pp. 593-603
Author(s):  
Jacobien H.F. Oosterhoff ◽  
Job N. Doornberg ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Orthopaedic Surgery ◽  
High Volume ◽  
Clinical Use ◽  
Hype Cycle ◽  
Computer Vision Applications ◽  
Near Future ◽  
Support Decision Making

Artificial Intelligence (AI) in general, and Machine Learning (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery. The greatest benefit of ML is in its ability to learn from real-world clinical use and experience, and thereby its capability to improve its own performance. Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients’ care and doctors’ workflows. The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment. Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks. In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. ‘If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine’.76 Cite this article: EFORT Open Rev 2020;5:593-603. DOI: 10.1302/2058-5241.5.190092

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