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Introductory Chapter: Artificial Intelligence - Challenges and Applications [chapter]

Dinesh G. Harkut, Kashmira Kasat
2019 Artificial Intelligence - Scope and Limitations  
Further, deep learning has deep impact on computer vision which is complimented by the evolution and low-cost availability of large-scale computing and the availability of large amounts of data.  ...  However, it will result in emerging new job domain with different quality job profile.  ... 
doi:10.5772/intechopen.84624 fatcat:rb5gzx5ehzfpjegk6foylipcim

Innovation, on-the-job learning, and labor contracts: an organizational equilibria approach

Stefano Dughera, Francesco Quatraro, Claudia Vittori
2021 Journal of Institutional Economics  
on a permanent basis; in the 'low-road' equilibrium, they invest more in routine projects and hire on a temporary basis.  ...  The goal of this paper is to show the existence of interlocking complementarities between the firm's technological and hiring strategies.  ...  The authors thank Cristiano Antonelli, Fabio Landini, Ugo Pagano, Massimiliano Vatiero, and Filippo Belloc for useful comments on an earlier version of this work, the editors of JOIE and five anonymous  ... 
doi:10.1017/s1744137421000497 fatcat:rykxm3txpjcadfsqjg6hsf7apy

Machine learning challenges and impact: an interview with Thomas Dietterich

Zhi-Hua Zhou
2017 National Science Review  
Machine learning is also valuable for web search engines, recommendation systems and personalized advertising.  ...  But with recent advances in machine learning, we now have systems that can perform these tasks with accuracy that matches human performance (more or less).  ...  I suspect that it will not be cost-effective to completely automate most existing jobs. Instead, maybe 80% of each job will be automated, but a human will need to do the remaining 20%.  ... 
doi:10.1093/nsr/nwx045 fatcat:33zq6mkftzh2zgmsuedmc7u7mi

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions [article]

Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
2021 arXiv   pre-print
of the recent trends of deep reinforcement learning in recommender systems.  ...  In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview  ...  Why Deep Reinforcement Learning for Recommendation?  ... 
arXiv:2109.03540v2 fatcat:5gwrbfcj3rc7jfkd54eseck5ga

A Survey on Skill Identification from Online Job Ads

Imane Khaouja, Ismail Kassou, Mounir Ghogho
2021 IEEE Access  
In particular, we evaluated and classified the prior work aiming to identify the skill bases used for analyzing job market needs; the type of extracted skills; the skill identification methods; the studied  ...  sector and the skill identification granularity.  ...  use of deep learning for skill tagging from job ads, e.g  ... 
doi:10.1109/access.2021.3106120 fatcat:6qesk5koenh37bgoxpfztlk4wa

The Science of Training and Development in Organizations

Eduardo Salas, Scott I. Tannenbaum, Kurt Kraiger, Kimberly A. Smith-Jentsch
2012 Psychological Science in the Public Interest  
1.05 Procedural knowledge-skills = 1.09 Attitudes = 0.29 Job behavior = 0.25 1 All effect sizes are d values, with a positive effect size indicating a significant effect for training compared to a control  ...  1.01 Expertise-subjective outcomes = .38 Keith and Frese (2008) Error management training Overall effect = .44 Powell and Yalcin (2010) Managerial training Posttest only, with control: Learning-objective  ...  Grant N000140610446), both awarded to the University of Central Florida, and by NASA Grant NNX11AR22G to The Group for Organizational Effectiveness.  ... 
doi:10.1177/1529100612436661 pmid:26173283 fatcat:mtspkuj5crffxnyccrqdfijyey

A management framework for training providers to improve workplace skills development in South Africa

Tom Bisschoff, Cookie Govender
2007 Education, Knowledge and Economy  
In accordance with the DoE's and DoL's call to revolutionise skills development, this research calls upon training providers to undergo deep change and revolutionise their internal management strategies  ...  on-the-job performance (C13) Inquire about previous learning (C8); Assess during learning (C9); Assess after learning (C10); Provide feedback on progress (C11) Are partners with our organisation  ... 
doi:10.1080/17496890601128332 fatcat:fiow5s2i35cylbkh4jr6drbbae

Organizational Learning from Experience in High-Hazard Industries: Problem Investigations as Off-line Reflective Practice

John S. Carroll, Jenny W. Rudolph, Sachi Hatakenaka
2002 Social Science Research Network  
with rules, (3) open learning prompted by acknowledgement of doubt and desire to learn, and (4) deep learning based on skillful inquiry and systemic mental models.  ...  and (2) what are the differences between learning practices that focus on control, elimination of surprises, and single-loop incremental "fixing" of problems with those that focus on deep or radical learning  ...  The concepts and skills of deep learning seem to be difficult to master and to require significant commitment, discipline, and learning-in-action.  ... 
doi:10.2139/ssrn.305718 fatcat:vyuze5pyojdy5imrigaggsqsz4

Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

Ajay Agrawal, Joshua S. Gans, Avi Goldfarb
2019 Journal of Economic Perspectives  
Other techniques are just entering the econometrician's toolkit: regression trees, neural networks, and reinforcement learning (for discussions in this journal, see Varian 2014; Mullainathan and Spiess  ...  Most of the concepts in standard machine learning textbooks (like Alpaydin 2010; and Hastie, Tibshirani, and Friedman 2009) are familiar to economists, like regression, maximum likelihood estimation, clustering  ...  As deep-learning pioneer Geoffrey Hinton (2016) said, "Take any old problem where you have to predict something and you have a lot of data, and deep learning is probably going to make it work better than  ... 
doi:10.1257/jep.33.2.31 fatcat:ikcn3ztivneo5m3e6henkfkswm

The Road to Intelligent Automation in the Energy Sector

Sorin ANAGNOSTE, Bucharest University of Economic Studies
2013 Management Dynamics in the Knowledge Economy  
These solutions have learning capabilities attached to it and analyze decisions and act just like humans.  ...  With Robotic Process Automation (RPA) nearly at its peak in terms of awareness and capabilities, organizations are exploring what's beyond it.  ...  All these new processes will make obsolete many jobs and will create new jobs with new knowledge and skills requirements.  ... 
doi:10.25019/mdke/6.3.08 fatcat:7nelhddfazfczeksasswuc3lsi

Learning from experience in high-hazard organizations

John S. Carroll, Jenny W. Rudolph, Sachi Hatakenaka
2002 Research in organizational behavior  
and (2) what are the differences between learning practices that focus on control, elimination of surprises, and single-loop incremental "fixing" of problems with those that focus on deep or radical learning  ...  Learning from experience, the cyclical interplay of thinking and doing, is increasingly important as organizations struggle to cope with rapidly changing environments and more complex and interdependent  ...  Assistance in collecting, coding, analyzing, and interpreting data was provided by Marcello Boldrini, Deborah Carroll, Christopher Resto, Khushbu Srivastava, Annique Un, and Theodore Wiederhold. Dr.  ... 
doi:10.1016/s0191-3085(02)24004-6 fatcat:wq46752rpbbxfabryiumeavqpu

Artificial Intelligence in Business

Matthew N. O. Sadiku, OmobayodeI. Fagbohungbe, Sarhan M. Musa
2020 International Journal of Engineering Research and Advanced Technology  
AI can increase productivity, gain competitive advantage, compliment human intelligence. and reduce cost of operations.  ...  Artificial intelligence (AI) is a field of computer science that is dedicated to developing software dealing with intelligent decisions, reasoning, and problem solving.  ...  The most common ML algorithms are supervised learning, unsupervised learning, reinforcement learning, and deep learning.  ... 
doi:10.31695/ijerat.2020.3625 fatcat:gzjelhqeavdkldxy7ackthtwuy

On-the-Job Evidence-Based Medicine Training for Clinician-Scientists of the Next Generation

Elaine Yl Leung, Sadia M Malick, Khalid S Khan, EBM-CONNECT Collaboration
2013 Clinical biochemist reviews  
In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation.  ...  Tailored e-learning EBM packages and evidence-based journal clubs have been shown to improve knowledge and skills of EBM.  ...  In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation.  ... 
pmid:24151345 pmcid:PMC3799223 fatcat:2oxfk3hjpzcyfeqdltbhbapth4

Failing to Learn and Learning to Fail (Intelligently)

Mark D. Cannon, Amy C. Edmondson
2005 Long range planning  
Organizations are widely encouraged to learn from their failures, but it is something most find easier to espouse than to effect.  ...  associations, and view it instead as a critical first step in a journey of discovery and learning.  ...  We would also like to thank the LRP editor, as well as the Special Issue editors and the anonymous reviewers for very helpful feedback.  ... 
doi:10.1016/j.lrp.2005.04.005 fatcat:owbzjbkxive5vgt5quriu74gsy

A Conceptual Framework on Defining Businesses Strategy for Artificial Intelligence

Salih Caner, Feyza Bhatti
2020 Contemporary Management Research  
AI is increasingly used in diverse business functions, including marketing, customer service, cost reduction, and product improvement.  ...  industries and AI, and regulations and ethics of AI on defining AI business strategy.  ...  Figure 3 3 Learning Systems a) Supervised Learning b) Reinforcement Learning c) Unsupervised Learning.  ... 
doi:10.7903/cmr.19970 fatcat:3qu7eg5rzrbs3coorvgbqrnley
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