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Application of Artificial Intelligence Algorithms for Robot Development
Published in S. S. Nandhini, M. Karthiga, S. B. Goyal, Computational Intelligence in Robotics and Automation, 2023
R. M. Tharsanee, R. S. Soundariya, A. Saran Kumar, V. Praveen
Modern advancement in the field of AI has paved the way for the development of intelligent robots that are more accurate in performing the selection and placement of objects in industries, in making the self-paced drones and the more allied Industrial Internet of Things. In workplace, Machine Learning has been adopted particularly when robotic systems are involved in the business processes [23]. The key reason for the widespread adoption of Machine Learning techniques in robotic systems is mainly due to the successful contributions in developing smarter robots to perform operations such as the assembly of the manufacturing units, select and place operations of the units and control of drone systems. In the subsequent section, an exhaustive description of the research and development carried out using the Machine Learning algorithms in the field of Robotics is given.
positive organizational change
Published in Bart Tkaczyk, Leading Positive Organizational Change, 2020
Besides, rapid technological change, “automation” in particular, is exerting a profound impact on corporations and markedly altering the ways we work. That being the case, many jobs may be at risk of being automated within, say, the next 20 years, including numerous advisory jobs, such as medical consultants, lawyers, financial advisors, and management consultants. Yet, by leveraging human-automation collaboration and technology for sustainable growth, future-proof enterprises should be capitalizing on the benefits of Big Data, workplace connectivity, machine learning, and artificial intelligence (AI) (Agrawal et al., 2018). The energy for embracing AI is widespread indeed. In point of fact, new research by MIT SMR Connections and SAS, based on 2,280 respondents, finds that AI implementation is transforming organizational culture and processes and creating new mandates for CEOs, chief information officers (CITOs), and other tech leaders. Specifically, 63 percent of the respondents expect AI/machine learning to drive dramatic or significant change, somewhat ahead of the cloud and well ahead of Internet-of-Things or blockchain. Besides, AI will require even more collaboration among employees trained in data management, analytics, IT infrastructure, and systems development, as well as business and operational experts. That being the case, organizational leaders will need to make sure that traditional silos will not stifle AI programs (MIT, 2020).
Enhancing OSH Management Processes through the Use of Smart Personal Protective Equipment, Wearables and Internet of Things Technologies
Published in Daniel Podgórski, New Opportunities and Challenges in Occupational Safety and Health Management, 2020
An example of machine learning applications in the area of OSH at the level of governmental administration is the Risk Group Prediction Tool (RGPT), developed by the Norwegian Labour Inspection Authority to assist labour inspectors in selecting enterprises with regard to workplace risks (Dahl and Starren 2019). This tool covers approximately 230,000 enterprises in Norway and divides them into four groups according to their OSH-related risks. It is assumed that the higher the risk group, the higher the probability that a future inspection of working conditions will detect deviations from OSH regulations in the company. RGPT was built on the basis of predictive modelling with the help of a machine learning algorithm using binary logistic regression analysis, which is a part of the supervised learning algorithms class. With the increasing number of inspections performed, the predictions made by RGPT become gradually more precise, because the algorithm adjusts itself on the basis of feedback (correct or erroneous forecasts) registered in the database containing data on already performed inspections.
Technological work environment: instrument development and measurement
Published in Behaviour & Information Technology, 2023
Waqar Akbar, Noor Ismawati Jaafar, Suhana Mohezar
The findings of our study by providing a valid instrument to measure the various aspects of technology at the workplace can be justified by stating that firms with little or no understanding of adopting and implementing technology may move in the wrong direction by taking incorrect decisions to invest in unproductive or outdated technology (Tarafdar, Pullins, and Ragu-Nathan 2015). If a firm or individual does not match their pace with technological change, they will face hurdles in effectively handling information and work effectively (Brougham and Haar 2017; Nam 2019). The instrument of our study covers three main aspects of the technological workplace: (i) connectivity, (ii) devices, and (iii) software to perform the tasks at the workplace. Prior researchers have urged employers to promote technology at work, finding this led to significant improvements in the health and productivity of employees (Chesley 2014; Chesley and Johnson 2015; Ter Hoeven, Van Zoonen, and Fonner 2016).
What about the human in human robot collaboration?
Published in Ergonomics, 2022
S. J. Baltrusch, F. Krause, A. W. de Vries, W. van Dijk, M. P. de Looze
This review has specifically linked characteristics of the robots to different aspects of job quality. Recognising these relations helps to improve human-robot collaboration. At the same time, adequate training and support are of crucial importance for successful adoption in the workplace. Based on the review we propose five design guidelines that will help the human to feel less constrained and less dependent on the robot, changing from an imposed collaboration towards a human-centered collaboration.