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 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.

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.

An Interview with Dr. Michael Zeller, Winner of ACM SIGKDD 2020 Service Award

2021 ◽  
Vol 22 (2) ◽  
pp. 6-7
Author(s):  
Michael Zeller
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Investment Company ◽  
Service Award ◽  
Virtual Conference ◽  
Learning Data

Michael Zeller, Ph.D. is the recipient of the 2020 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Zeller was honored for his years of service and many accomplishments as the secretary and treasurer for ACM SIGKDD, the organizing body of the annual KDD conference. Zeller is also head of AI strategy and solutions at Temasek, a global investment company seeking to make a difference always with tomorrow in mind. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 1999, what he learned from the first-ever virtual conference, his work at Temasek, and what excites him about the future of machine learning, data science and artificial intelligence.

An Interview with Dr. Shipeng Yu, Winner of ACM SIGKDD 2021 Service Award

2021 ◽  
Vol 23 (2) ◽  
pp. 1-2
Author(s):  
Shipeng Yu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Professional Network ◽  
Service Award ◽  
Years Of Service ◽  
Learning Data

Shipeng Yu, Ph.D. is the recipient of the 2021 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Yu was honored for his years of service and many accomplishments as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. Dr. Yu is Director of AI Engineering, Head of the Growth AI team at LinkedIn, the world's largest professional network. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 2006, the benefits and drawbacks of virtual conferences, his work at LinkedIn, and KDD's place in the field of machine learning, data science and artificial intelligence.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Human Robot Interaction ◽  
Gas Industry ◽  
Deep Dive ◽  
The Future ◽  
And Robotics

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?

scholarly journals Me, My Bot and His Other (Robot) Woman? Keeping Your Robot Satisfied in the Age of Artificial Emotion

Robotics ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 44 ◽  
Author(s):  
Rebekah Rousi
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Science Fiction ◽  
Sexual Partners ◽  
Sex Robots ◽  
Artificial Emotions ◽  
The Future ◽  
The Relationship ◽  
And Robotics

With a backdrop of action and science fiction movie horrors of the dystopian relationship between humans and robots, surprisingly to date-with the exception of ethical discussions-the relationship aspect of humans and sex robots has seemed relatively unproblematic. The attraction to sex robots perhaps is the promise of unproblematic affectionate and sexual interactions, without the need to consider the other’s (the robot’s) emotions and indeed preference of sexual partners. Yet, with rapid advancements in information technology and robotics, particularly in relation to artificial intelligence and indeed, artificial emotions, there almost seems the likelihood, that sometime in the future, robots too, may love others in return. Who those others are-whether human or robot-is to be speculated. As with the laws of emotion, and particularly that of the cognitive-emotional theory on Appraisal, a reality in which robots experience their own emotions, may not be as rosy as would be expected.

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