scholarly journals Fusion Skills and Industry 5.0: Conceptions and Challenges

2021 ◽  
Author(s):  
John Mitchell ◽  
David Guile
Keyword(s):  
Machine Learning ◽  
Engineering Education ◽  
Data Science ◽  
Core Curriculum ◽  
Digital Technologies ◽  
Education Curriculum ◽  
Engineering Knowledge ◽  
The Core ◽  
The Future

The nature of work is changing rapidly, driven by the digital technologies that underpin industry 5.0. It has been argued worldwide that engineering education must adapt to these changes which have the potential to rewrite the core curriculum across engineering as a broader range of skills compete with traditional engineering knowledge. Although it is clear that skills such as data science, machine learning and AI will become fundamental skills of the future it is less clear how these should be integrated into existing engineering education curricula to ensure relevance of graduates. This chapter looks at the nature of future fusion skills and the range of strategies that might be adopted to integrated these into the existing engineering education curriculum.

scholarly journals Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges

2021 ◽  
Vol 8 ◽  
Author(s):  
João V. Cordeiro
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Decision Making ◽  
Data Science ◽  
Digital Health ◽  
Digital Technologies ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
The Future ◽  
Human Skills

Digital technologies and data science have laid down the promise to revolutionize healthcare by transforming the way health and disease are analyzed and managed in the future. Digital health applications in healthcare include telemedicine, electronic health records, wearable, implantable, injectable and ingestible digital medical devices, health mobile apps as well as the application of artificial intelligence and machine learning algorithms to medical and public health prognosis and decision-making. As is often the case with technological advancement, progress in digital health raises compelling ethical, legal, and social implications (ELSI). This article aims to succinctly map relevant ELSI of the digital health field. The issues of patient autonomy; assessment, value attribution, and validation of health innovation; equity and trustworthiness in healthcare; professional roles and skills and data protection and security are highlighted against the backdrop of the risks of dehumanization of care, the limitations of machine learning-based decision-making and, ultimately, the future contours of human interaction in medicine and public health. The running theme to this article is the underlying tension between the promises of digital health and its many challenges, which is heightened by the contrasting pace of scientific progress and the timed responses provided by law and ethics. Digital applications can prove to be valuable allies for human skills in medicine and public health. Similarly, ethics and the law can be interpreted and perceived as more than obstacles, but also promoters of fairness, inclusiveness, creativity and innovation in health.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Sebastian Raschka ◽  
Joshua Patterson ◽  
Corey Nolet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
The Core ◽  
Critical Components ◽  
High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

scholarly journals A Human Factors Engineering Education Perspective on Data Science, Machine Learning and Automation

2019 ◽  
Vol 63 (1) ◽  
pp. 488-492
Author(s):  
Daniel Hannon ◽  
Esa Rantanen ◽  
Ben Sawyer ◽  
Raymond Ptucha ◽  
Ashley Hughes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Engineering Education ◽  
Data Science ◽  
Human Factors Engineering ◽  
Automated Systems ◽  
Learning Techniques ◽  
Black Boxes ◽  
Model Training ◽  
Novel Applications

The explosion of data science (DS) in all areas of technology coupled with the rapid growth of machine learning (ML) techniques in the last decade create novel applications in automation. Many working with DS techniques rely on the concept of “black boxes” to explain how ML works, noting that algorithms find patterns in the data that humans might not. While the mathematics are still being developed, the implications for the application of ML, specifically to questions of automation, also are being studied, but still remain poorly understood. The decisions made by ML practitioners with respect to data selection, model training and testing, data visualization, and model applications remain relatively unconstrained and have the potential to yield unexpected results at the systems level. Unfortunately, human factors engineers concerned with automation often have limited training and awareness of DS and ML applications and are unable to provide the meaningful guidance that is needed to ensure the future safety of these newly emerging automated systems. Moreover, undergraduate and graduate programs in human factors engineering (HFE) have not kept pace with these developments and future HFEs may continue to find themselves unable to contribute meaningfully to the development of automated systems based on algorithms derived from ML. In this paper, human factors engineers and educators explore some of the challenges to our understanding of automation posed by specific ML techniques and contrast this with an outline of some of the historical work in HFE that has contributed to our understanding of safe and effective automation. Examples are provided from more conventional applications using both supervised and unsupervised learning techniques, that are explored with respect to implications for algorithm performance, use in system automation, and the potential for unintended results. Implications for human factors engineering education are discussed.

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

Artificial intelligence as the core of production of the future: Machine learning and intellectual decision supports

2021 ◽  
pp. 235-256
Author(s):  
Aleksei V. Bogoviz ◽  
Anastasia A. Kurilova ◽  
Tatyana E. Kozhanova ◽  
Anastasia A. Sozinova
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
The Core ◽  
The Future

scholarly journals Dromospheric Generation

2015 ◽  
Vol 11 (2) ◽  
pp. 246-259 ◽  
Author(s):  
Felicity J. Colman
Keyword(s):  
Political Economy ◽  
Health Education ◽  
Digital Technologies ◽  
Game Play ◽  
Education Governance ◽  
The Core ◽  
Management Technology ◽  
The Future ◽  
Territorial Management ◽  
Data Consumption

Paul Virilio’s work on dromology provides a model of a political economy. Called the “dromoeconomic” system, it incorporates aspects of temporality, consumption, and technology, arguably three of the core factors for consideration of the future organization of human societies. Durational factors manifest in issues of health, education, governance, and data. Consumption facilitates the politics of resource and territorial management; technology controls communication and transmission of energy at its base forms into the complexities of every facet of life. Living in a dromoeconomy means negotiating a material field created by the speeds of the global objects of communication. This article focuses on one aspect of the dromoeconomy, the users and producers of this system, the “dromospheric generation.” It explores the generation of the 2000s, users of screen-based digital technologies, in particular focusing on the digital child (“digi-child”) as the model information worker whose operational skills of “transmission” through game play are producing the material grounds of the future by transmitting energy in the dromoeconomy.

The Education of the Human Factors Engineer in the Age of Data Science

2020 ◽  
Vol 64 (1) ◽  
pp. 480-484
Author(s):  
Daniel Hannon ◽  
Esa Rantanen ◽  
Ben Sawyer ◽  
Ashley Hughes ◽  
Katherine Darveau ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factors ◽  
Data Science ◽  
Panel Discussion ◽  
Quantitative Skills ◽  
The Future ◽  
Relevant Role ◽  
Machine Learning Applications

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.

scholarly journals Development of a learning pilot for the remote teaching of Smart Maintenance using open source tools

2021 ◽  
Author(s):  
Maira Callupe ◽  
Luca Fumagalli ◽  
Domenico Daniele Nucera
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Science ◽  
Computer Programming ◽  
Educational Institutions ◽  
Educational Tools ◽  
Vast Array ◽  
New Graduates ◽  
The Future

Technology has created a vast array of educational tools readily available to educators, but it also has created a shift in the skills and competences demanded from new graduates. As data science and machine learning are becoming commonplace across all industries, computer programming is emerging as one of the fundamental skills engineers will require to navigate the future and current workplace. It is, thus, the responsibility of educational institutions to rise to this challenge and to provide students with an appropriate training that facilitates the development of these skills. The purpose of this paper is to explore the potential of open source tools to introduce students to the more practical side of Smart Maintenance. By developing a learning pilot based mainly on computational notebooks, students without a programming background are walked through the relevant techniques and algorithms in an experiential format. The pilot highlights the superiority of Colab notebooks for the remote teaching of subjects that deal with data science and programming. The resulting insights from the experience will be used for the development of subsequent iterations during the current year.

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