AI, Machine Learning, IoT, and Analytics: Exploring the Implications for Knowledge Management and Innovation
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ItemTowards Labour Market Intelligence through Topic Modelling( 2019-01-08)Nowadays, the number of people and companies using the Web to search for and advertise job opportunities is growing apace, making data related to the Web labor market a rich source of information for understanding labor market dynamics and trends. In this paper, the emerging term labor market intelligence (LMI) refers to the definition of AI algorithms and frameworks that derive useful knowledge for labor market-related activities, by putting AI into the labor market. At the same time, another branch of AI is developing known as Explainable AI (XAI), whose goal is to obtain interpretable models from current (and future) AI algorithms, given that most of them actually act like black boxes, providing no interpretable explanations of their behavior, as in the case of machine learning. In this paper we connect these two approaches, using a graph model obtained through an NLP-based (Natural Language Processing) methodology for classifying job vacancies. We compare the results obtained with those from a European Project in LMI that employs machine learning for the classification task, to show that our approach is effective and promising.
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ItemFindings from Multipurpose IoT Solution Experimentations in Finnish SMEs: Common Expectations and Challenges( 2019-01-08)The Finnish SMEs are showing increasing interest in modern digital solutions in hopes of streamlining their work processes. At the same time, the companies are struggling with resourcing issues and find it challenging to familiarize themselves with new solutions and how those could be applied in their business environment. To support the technical transition, we provided 10 Finnish SMEs with multipurpose IoT solutions, allowing them to conduct guided experimentations with relevant objectives. The business areas of the target companies varied widely from manufacturing to transportation and accommodation, as did their individual needs and expectations towards IoT. Based on this multifaceted background, we compared the companies’ original expectations with the actualised experimentation outcomes, aiming at identifying commonly occurring challenges linked to multipurpose IoT solutions. As a result, an overview of the generalizable findings was gathered to offer further insight on how the multipurpose IoT devices could better serve the SMEs.
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ItemMachine Learning in Artificial Intelligence: Towards a Common Understanding( 2019-01-08)The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (inter¬disciplinary) discussions and future research.
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