Comparison of the most popular operating systems in terms of functionalities

The main purpose of research is comparison of the following modern operating systems: Windows 10, Windows 11, MacOS Catalina and Linux Ubuntu 20.04 LTS. An analysis was made in terms of functionalities and time needed to perform basic activities. The systems were selected on the basis on performed popularity analysis, by using StatCounter [1] statistic. To study each operating system it was necessary to create two test stands corresponding to the requirements of the systems. Conducted research were divided on two sections. In the first one, analysis of the possessed functionalities, assessment of the advancement and ease of using them was performed. In the second section, examination was carried out to compare the operating system in terms of the time of performing specific activities

Context Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcription

The paper presents how organizational practices enable the improvement and maintenance of task performance in a learning human-in-the-loop system exposed to a wide range of context changes. We investigate how the case company tripled the efficiency of medical transcribers by leveraging its machine learning-based automatic speech recognition technology. We find that the focal system operated across stable, drifting, and jumping contexts. Despite changes, it continued to improve or maintained performance thanks to two sets of organizational practices aligning it with the context: extending and refining. This paper makes two key contributions: It shows the importance of considering context changes in the design and operation of learning human-in-the-loop systems. Our empirical findings help with resolving some contradictory outcomes of the recent conceptual work. Secondly, we show that context alignment practices are situated at the sociotechnical system level and, thus, are not just technical solution nor can be detached from social elements

Beyond MLOps: The Lifecycle of Machine Learning-based Solutions

Organizations increasingly use machine learning (ML) to transform their operations. The technical complexity and unique challenges of ML lead to the emergence of ML operations (MLOps) practices. However, the research on MLOps is in its infancy and is fragmented across disciplines. We extend and integrate these conversations by developing a framework that accounts for the technical, organizational, behavioral, and temporal aspects of the overarching ML-based solution lifecycle. We identify the key components of ML-based solution lifecycle and their configuration through an in-depth study of Finland’s Artificial Intelligence Accelerator (FAIA) and follow-up semi-structured interviews with experts from multiple international organizations outside FAIA. This study contributes to the recent IS literature concerned with the sociotechnical aspects of ML. We bring new insights into the discussion on organizational learning, conjoined agency, and automation and augmentation. These insights extend and complement MLOps practices, thereby helping organizations better realize the potential of ML technology