scholarly journals There is Not One But Many AI: A Network Perspective on Regional Demand in AI Skills

2020 ◽  
Author(s):  
Fabian Stephany
Keyword(s):  
Artificial Intelligence ◽  
Ict Skills ◽  
Job Advertisement ◽  
Regional Demand ◽  
Job Openings ◽  
Job Postings

This work proposes a network perspective in order to empirically identify the relevant ICT skills related to AI, to what extent they are systemically related, and how their composition varies across regions. With the example of 5,227 job openings from Germany advertised as postings in Artificial Intelligence, relevant skills are identified and connected in a network fashion. Two skills are connected, if they are jointly required by the same job advertisement. Similarly, regional skill networks can be constructed: Job postings are screened by city location and skill networks are constructed for this set of regional postings exclusively. The resulting networks depict the regional city ecosystem of AI skills currently in demand.

scholarly journals Using Future Employers' Expectations to Inform Global Health Fellowship Curricula

2018 ◽  
Vol 10 (5) ◽  
pp. 517-521 ◽  
Author(s):  
Heather A. Brown ◽  
Patrick Mulherin ◽  
William C. Ferrara ◽  
Mark E. Humphrey ◽  
Alexander Vera ◽  
...  
Keyword(s):  
Global Health ◽  
Master's Degree ◽  
Inclusion Criteria ◽  
Health Organizations ◽  
Middle Income ◽  
Objective Evidence ◽  
Health Practitioners ◽  
Job Openings ◽  
Master’S Degree ◽  
Job Postings

ABSTRACT Background  Fellowships in global health are increasingly popular and seek to equip physicians with the skills necessary to be effective global health practitioners. Little objective guidance exists on which skills make graduates competitive applicants from the perspective of potential global health employers. Objective  We sought to provide objective evidence for the qualifications that make applicants competitive for global health positions by analyzing the listed qualifications for current job openings in large global health organizations. Methods  The websites of 48 global health employers were systematically searched for current job postings between May and August 2017. Jobs were included for analysis if a medical degree was listed among the primary degrees accepted, and the job was based primarily in a low- or middle-income country. Results  A total of 5849 employment opportunities were posted during the search period, and 81 (1.4%) of these met inclusion criteria. Twenty-two (27%) jobs required and 35 (43%) preferred a relevant master's degree. Few jobs requested a candidate with a PhD and none mentioned tropical medicine training as a requirement or preference. Twenty-three jobs (28%) required and 19 (23%) preferred candidates to speak another language. Most jobs (69%, 56 of 81) required more than 5 years of relevant experience. Only 11 (13%) jobs were primarily clinical positions. Conclusions  For physicians pursuing a career in global health, most publicly searchable jobs require substantial previous experience and involvement in global health activities beyond clinical practice. Master's degree and language skills are frequently requested candidate qualifications.

Artificial Intelligence in Health Care? Evidence from Online Job Postings

2020 ◽  
Vol 110 ◽  
pp. 400-404 ◽  
Author(s):  
Avi Goldfarb ◽  
Bledi Taska ◽  
Florenta Teodoridis
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Statistical Analysis ◽  
Popular Press ◽  
Using Data ◽  
Job Postings

This paper documents a puzzle. Despite the numerous popular press discussions of artificial intelligence (AI) in health care, there has been relatively little adoption. Using data from Burning Glass Technologies on millions of online job postings, we find that AI adoption in health care remains substantially lower than in most other industries and that under 3 percent of the hospitals in our data posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do observe shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.

scholarly journals U.S. Demand for AI Certifications: Promise or Hype?

2021 ◽  
Author(s):  
Diana Gehlhaus ◽  
◽  
Ines Pancorbo
Keyword(s):  
Artificial Intelligence ◽  
Job Postings ◽  
The U.S

This issue brief explores whether artificial intelligence and AI-related certifications serve as potential pathways to enter the U.S. AI workforce. The authors find that according to U.S. AI occupation job postings data over 2010–2020, there is little demand from employers for AI and AI-related certifications. From this perspective, such certifications appear to present more hype than promise.

Robots, skill demand and manufacturing in US regional labour markets

2019 ◽  
Vol 13 (1) ◽  
pp. 77-97 ◽  
Author(s):  
Nancey Green Leigh ◽  
Benjamin Kraft ◽  
Heonyeong Lee
Keyword(s):  
Artificial Intelligence ◽  
Great Recession ◽  
International Federation ◽  
Labour Markets ◽  
The Other ◽  
Manufacturing Employment ◽  
The Great Recession ◽  
The Usa ◽  
Regional Labour Markets ◽  
Job Postings

Abstract Advances in robotics and artificial intelligence (AI) technology have spurred a re-examination of technology’s impacts on jobs and the economy. This article reviews several key contributions to the current jobs/AI debate, discusses their limitations and offers a modified approach, analysing two quantitative models in tandem. One uses robot stock data from the International Federation of Robotics as the primary indicator of robot use, whereas the other uses online job postings requiring robot-related skills. Together, the models suggest that since the Great Recession ended, robots have contributed positively to manufacturing employment in the USA at the metropolitan level.

AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1451-1482
Author(s):  
Bowen Lou ◽  
◽  
Lynn Wu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Development ◽  
Large Scale ◽  
New Drugs ◽  
Innovation Capability ◽  
Domain Experts ◽  
Medium Level ◽  
Job Postings ◽  
Mechanism Of Impact

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.

scholarly journals Automation and Machine Learning in Transforming the Financial Industry

2019 ◽  
Vol 9 (3) ◽  
pp. 129-138 ◽  
Author(s):  
Praveen Kumar Donepudi ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Banking Sector ◽  
Financial Industry ◽  
Major Purpose ◽  
Financial Valuation ◽  
The Past ◽  
Job Openings ◽  
The Subject ◽  
Start Ups

The major purpose of this study was to analyze the influence of machine learning on the digital age, particularly in the field of finance. This study involves the application of machine learning, its challenges, opportunities and effect on job openings and operations. This paper is based on the findings of a qualitative study of the text on the subject of machine learning in finance. The theoretical portion of this paper explores the universal framework, such as the past, existing and the next level of the machine learning, with emphasis on its advantages and drawbacks. The study also examines the global recognition of machine learning in the review of artificially intelligent development and start-ups in European countries. The research methodology used in this study was the evaluation of the qualitative methods in the paper. The study also reviewed twenty electronic records and articles on machine learning in finance. During the research on how computer technology transforms the banking sector, the implementation and impact of artificial intelligence in financing was discussed. Research shows that several financial institutions have significantly benefited from the introduction of a variety of machine learning and artificial intelligence. This paper demonstrates that there is a lack of experience in the field of machine learning, even as many unskilled or semi-qualified tasks carried out by individuals are carried out by machines. This study has shown that, through banking and financial valuation, whether it is manufacturing, data analysis or continuing to invest, there will be many more developments that can get the job done.

Humans Need Not Apply

2021 ◽  
pp. 60-85
Author(s):  
Mike Berrell
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Social Relations ◽  
New World ◽  
Legal Services ◽  
Occupational Groups ◽  
Job Advertisement ◽  
The Social ◽  
Smart Machines

Advanced technologies including artificial intelligence, robotics, and machine learning (smart machines) impact understandings about the nature of work. For professionals, semi-professionals, and ancillary workers supplying healthcare and legal services, for example, smart machines change the social relations of work and subvert notions of status and hierarchy that come with occupational groups such as doctors or lawyers. As smart machines continue to disrupt employment, job advertisement might soon carry the warning that humans need not apply. Under the prospect of a new world of work, people require additional knowledge, skills, and attitudes to cope with a future where smart machines radically alter the nature of work in settings where some people work anywhere and anytime while others work nowhere. In any future, people require skills and attitudes to cope with uncertainty. Ideas about multiple intelligences, emotional intelligence, critical thinking, creativity, and problem-solving will help employees cope with any of the futures of work predicted in the literature.

scholarly journals Penyuluhan Machine Learning dan Quantum Artificial Intelligence di Era Industri 4.0

Servirisma ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 23-35
Author(s):  
Hendra Bunyamin ◽  
Teddy Marcus Zakaria ◽  
Andreas Widjaja ◽  
Natanael Halim ◽  
Vania Sarwoko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Poor Performance ◽  
Work Force ◽  
Business Leaders ◽  
Doctoral Graduates ◽  
Digital Era ◽  
Job Openings ◽  
Youtube Videos ◽  
Catch Up

The Digital Era 4.0 has started since 2016 and two Southeast Asia countries such as Malaysia and Singapore have already adapted to the era; unfortunately, Indonesia has been struggling to adapt the era and, therefore, needs to catch up the digital competitiveness of its neighbouring countries. According to IMD World Digital Competitiveness 2020, Indonesia placed 56th of 63 countries in the digital competitiveness measurement. Despite its poor performance, Indonesia can catch up with other countries by starting from universities’ environment where Indonesia’s next generations study. Universities are prominent education institutions which prepare next generations for world digital competitiveness. According to BPS Indonesia, the unemployment of bachelor, master, and doctoral graduates reach a total number of 737.000, or 5,67% of 13 millions work force. One of the causes is the lack of technological knowledge, specifically, Artificial Intelligence (AI), from the graduates. Particularly, when they become business leaders, they are not fully prepared to create new job openings because mostly their mindsets are to find suitable jobs after study. The two webinars are results of collaboration between several universities which form NUNI (Jejaring Universitas Nusantara) whose purpose is to equip students with the knowledge of AI. Our method of counselling whose format is two webinars with both titles are Interpretable Machine Learning and Quantum Artificial Intelligence has gained appreciation in the form of average participation score which approaches excellent score (4,60 of 5,00). Additionally, these two webinars are publicly available in web blogs and Youtube videos.  

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