scholarly journals Special Issue on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research

2020 ◽  
Vol 48 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Zhehui Wang ◽  
J. Luc Peterson ◽  
Cristina Rea ◽  
David Humphreys
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Special Issue ◽  
Plasma Research ◽  
Learning Data

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.

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.

How We Became Instrumentalists (Again)

2018 ◽  
Vol 48 (5) ◽  
pp. 673-684 ◽  
Author(s):  
Matthew L. Jones
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Data Science ◽  
Predictive Analytics ◽  
The United States ◽  
Applied Statistics ◽  
Computational Statistics ◽  
Special Issue ◽  
Epistemic Values

In the last two decades, a highly instrumentalist form of statistical and machine learning has achieved an extraordinary success as the computational heart of the phenomenon glossed as “predictive analytics,” “data mining,” or “data science.” This instrumentalist culture of prediction emerged from subfields within applied statistics, artificial intelligence, and database management. This essay looks at representative developments within computational statistics and pattern recognition from the 1950s onward, in the United States and beyond, central to the explosion of algorithms, techniques, and epistemic values that ultimately came together in the data sciences of today. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

scholarly journals Governing artificial intelligence: ethical, legal and technical opportunities and challenges

2018 ◽  
Vol 376 (2133) ◽  
pp. 20180080 ◽  
Author(s):  
Corinne Cath
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Law Enforcement ◽  
Urban Infrastructure ◽  
Humanitarian Aid ◽  
Special Issue ◽  
Theme Issue ◽  
Ethical Frameworks ◽  
Recent Developments ◽  
Risk Areas

This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

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