Is This Time Different?

2021 ◽  
pp. 1-20
Author(s):  
Cynthia Estlund
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Technological Innovations ◽  
Job Destruction ◽  
Policy Responses ◽  
Large Potential ◽  
And Robotics

Chapter 1 introduces the debate over whether “this time is different”—whether contemporary innovations in artificial intelligence, machine learning, and robotics are more likely than past rounds of technological innovations to yield net job destruction—and the parallel debate over whether we should welcome or worry about that future. It begins with a tour of some of the innovations that are allowing algorithms and robots to replace human workers at a range of tasks, and explains why the recent COVID-19 crisis is accelerating automation along several vectors. The chapter previews the book’s overall claims that a future of less work is foreseeable, even likely, if not inevitable; that it holds both perils and promise for ordinary workers and the society as a whole; and that it should be met with policy responses that can mitigate the losses and fairly distribute the large potential gains from a more automated economy.

Disruptions of the Fourth Industrial Revolution

2021 ◽  
pp. 189-199
Author(s):  
Idris Olayiwola Ganiyu ◽  
Ola Olusegun Oyedele ◽  
Evelyn Derera
Keyword(s):  
Artificial Intelligence ◽  
Technological Innovation ◽  
Industrial Revolution ◽  
Technological Innovations ◽  
Fourth Industrial Revolution ◽  
Skill Gaps ◽  
The World ◽  
The Future ◽  
The Impact ◽  
And Robotics

The Fourth Industrial Revolution has resulted in the disruption of the world of work whereby technological innovation such as artificial intelligence (AI) and robotics. These disruptions may be creative in that as some jobs are lost due to the development of artificial intelligence, new ones are created. This chapter explored the impact of disruptive technological innovations on the future of work. The skill gaps brought about by the emergence of the Fourth Industrial Revolution was also explored in this chapter.

Robots, Replicants, and Surrogates

2021 ◽  
pp. 84-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science Fiction ◽  
Intelligent Systems ◽  
New World ◽  
Attitudes And Perceptions ◽  
The World ◽  
Recent Developments ◽  
Brave New World ◽  
And Robotics

The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. This has given rise to machine learning, intelligent systems, and robotics. Robots and AI might right now still seem the reserve of blockbuster science fiction movies and documentaries, but it's no doubt the world is changing. This chapter explores the origins, attitudes, and perceptions of robotics and the multiple types of robots that exist today. Perhaps most importantly, it focuses on ethical and societal concerns over the question: Are we heading for a brave new world or a science fiction horror-show where AI and robots displace or, perhaps more worryingly, replace humans?

The future of cherry production.

2021 ◽  
pp. 377-380
Author(s):  
Lynn E. Long ◽  
Gregory A. Lang ◽  
Clive Kaiser
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Invasive Species ◽  
Significant Contribution ◽  
The Future ◽  
Production Technologies ◽  
New Challenges ◽  
Electronic Sensing ◽  
And Robotics

Abstract This chapter provides information on significant contribution of various advances in horticultural production technologies, including electronic sensing, autonomous orchard equipment, machine learning and artificial intelligence and robotics to future cherry production trends. New challenges due to invasive species, climate change and the ever unpredictable geopolitical landscape are also discussed.

Artificial Intelligence in the Hospitality Sector

2021 ◽  
pp. 257-278
Author(s):  
Debdutta Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Changing Roles ◽  
Hospitality Sector ◽  
Long Term Impact ◽  
The Cost ◽  
And Robotics

Hospitality is one of the most important sectors of the economy and offers employment to thousands of people. The recent advances in technology has seen that quite a few of the players in this industry have successfully deployed artificial intelligence, machine learning, and robotics. This chapter delves into the details of such deployment in the various processes in this sector and discusses the short-term, medium-term, and long-term impact of these technologies on all the major stakeholders of this industry. The author also looks at the cost benefit analysis of this technologies and concludes that most players sooner, rather than later would be forced by competition to strongly adopt them. The chapter also briefly discusses the changing roles of human employees in this scenario.

Digitalism and Jobs of the Future

2022 ◽  
pp. 1-20
Author(s):  
Gamze Sart ◽  
Orkun Yildiz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Strong Relationship ◽  
New Order ◽  
Occupational Groups ◽  
The Future ◽  
And Robotics ◽  
Learning Data

There has been a strong relationship between digitalism and the future of jobs. Reports by OECD and WEF examined the jobs in the coming decades, and the findings show that there is a completely new order in the professions that we are not familiar with. In addition, how the impacts of artificial intelligence (AI), machine learning, data science, and robotics have affected labour, the market is analyzed. The findings in the reports clearly would affect the careers of the next generations. With the post-pandemic developments and the rapid advancement of technology in many areas worldwide, digitalization has gained significant momentum. This situation manifested itself in professions and workforce. However, it is obvious that in the coming years, with digitalization, many occupational groups and accordingly, differences in skills will be seen. While some occupational groups disappear completely, it is seen that some new occupational groups will emerge and, some will transform.

The Evolution and Future of Retailing and Retailing Education

2018 ◽  
Vol 40 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Dhruv Grewal ◽  
Scott Motyka ◽  
Michael Levy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phones ◽  
Smart Devices ◽  
The Internet ◽  
Classroom Discussions ◽  
The Past ◽  
Blockchain Technology ◽  
The Internet Of Things ◽  
And Robotics

The pace of retail evolution has increased dramatically, with the spread of the Internet and as consumers have become more empowered by mobile phones and smart devices. This article outlines significant retail innovations that reveal how retailers and retailing have evolved in the past several decades. In the same spirit, the authors discuss how the topics covered in retail education have shifted. This article further details the roles of current technologies, including social media and retailing analytics, and emerging areas, such as the Internet of things, machine learning, artificial intelligence, blockchain technology, and robotics, all of which are likely to change the retail landscape in the future. Educators thus should incorporate these technologies into their classroom discussions through various means, from experiential exercises to interactive discussions to the reviews of recent articles.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

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.

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