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 (interdisciplinary) discussions and future research.
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The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a serviceoriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research.
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