Afterword

2021 ◽  
pp. 227-246
Keyword(s):  
Artificial Intelligence ◽  
Learning Algorithms ◽  
Learning Needs ◽  
Scientific Theories ◽  
Learning Models ◽  
Fifth Dimension ◽  
The Future

This chapter looks across the landscape of learning in the current age of algorithms and so-called ‘artificial intelligence' with a focus on issues raised in the concept of “the master algorithm” around learning models and the future of learning. Pedro Domingos identifies five “scientific” theories of learning algorithms and presents them sequentially and so capable of improvement by the theorist (and he alone). By contrast, in her conversational framework, Diana Laurillard presents four approaches to framing learning models. The authors prefer Laurillard's modelling but believe the fifth dimension of rhizomatic learning needs to be added to her framework in order to enable the learner to take the final decisions on what has been learned and what they will do subsequently, and so produce a learner-centric framework for learning and architectures of participation. They examine several histories of thinking about intelligence as well as long-term views of technology before outlining, briefly, a phenomenonology of learning as the potential countervailing ideas to AI in education.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

The Future of Artificial Intelligence in Education

2021 ◽  
pp. 187-194
Author(s):  
Fati Tahiru ◽  
Samuel Agbesi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Counseling Students ◽  
Learning Management Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
The Future ◽  
Artificial Intelligence In Education ◽  
Use Of The Internet

The key accelerating factor in the increased growth of AI is the availability of historic datasets, and this has influenced the adoption of artificial intelligence and machine learning in education. This is possible because data can be accessed through the use of various learning management systems (LMS) and the increased use of the internet. Over the years, research on the use of AI and ML in education has improved appreciably, and studies have also indicated its success. Machine learning algorithms have successfully been implemented in institutions for predicting students' performance, recommending courses, counseling students, among others. This chapter discussed the use of AI and ML-assisted systems in education, the importance of AI in education, and the future of AI in education to provide information to educators on the AI transformation in education.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

DIGITALIZATION OF LABOR MIGRATION PROCESSES IN UZBEKISTAN. WORLD EXPERIENCE

2020 ◽  
Vol 4 (3) ◽  
pp. 29-39
Author(s):  
Sulkhiya Gazieva ◽  
Keyword(s):  
Labor Market ◽  
Technological Change ◽  
Labor Migration ◽  
Smart Phone ◽  
Logic Circuits ◽  
Digital Logic ◽  
The Internet ◽  
The Future

The future of labor market depends upon several factors, long-term innovation and the demographic developments. However, one of the main drivers of technological change in the future is digitalization and central to this development is the production and use of digital logic circuits and its derived technologies, including the computer,the smart phone and the Internet. Especially, smart automation will perhaps not cause e.g.regarding industries, occupations, skills, tasks and duties

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

On Sinofuturism

Screen Bodies ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 46-62
Author(s):  
Yunying Huang
Keyword(s):  
Artificial Intelligence ◽  
Pop Culture ◽  
Chinese People ◽  
Modern Chinese ◽  
The Future ◽  
Dominant Design ◽  
Urban Rural ◽  
Cultural Expressions ◽  
Contemporary Chinese

Dominant design narratives about “the future” contain many contemporary manifestations of “orientalism” and Anti-Chineseness. In US discourse, Chinese people are often characterized as a single communist mass and the primary market for which this future is designed. By investigating the construction of modern Chinese pop culture in Chinese internet and artificial intelligence, and discussing different cultural expressions across urban, rural, and queer Chinese settings, I challenge external Eurocentric and orientalist perceptions of techno-culture in China, positing instead a view of Sinofuturism centered within contemporary Chinese contexts.

Der Wald-Wild-Konflikt – was kann die Forstökonomie zur Lösung beitragen? (Essay)

2017 ◽  
Vol 168 (4) ◽  
pp. 181-185
Author(s):  
Marc Hanewinkel
Keyword(s):  
Forest Economics ◽  
Production Risk ◽  
Damage Compensation ◽  
Land Expectation Value ◽  
Compensation Methods ◽  
The Future ◽  
Single Tree Selection ◽  
Simplifying Assumptions ◽  
Forest Enterprises

The forest-game conflict – how can forest economics contribute to solve it? (Essay) Core parameters of forest economics such as land expectation value or highest revenue show that damage caused by wild ungulates can critically influence the economic success of forest enterprises. When assessing and evaluating the damage in order to calculate damage compensation, methods are applied in Germany that look either into the past (“cost value methods”) or into the future (“expected value methods”). The manifold uncertainties related to this evaluation over long-term production periods are taken into account within a framework of conventions through strongly simplifying assumptions. Only lately, the increased production risk due to game-induced loss of species diversity is also considered. Additional aspects that should be taken into account in the future are the loss of climate-adapted species, the change of the insurance values of forest ecosystems and the impossibility of specific management systems such as single-tree selection forestry due to the influence of game. Because of high transaction costs when assessing the damage, financial compensation should only be the “ultimate measure” and a meditation between stakeholder groups with the goal to find a cooperative solution before the damage occurs should be preferred.

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