| 1. |
- Berndtsson, Mikael, et al.
(författare)
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Scaling Up Data-Driven Pilot Projects
- 2020
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Ingår i: The AI Magazine. - : Association for the Advancement of Artificial Intelligence. - 0738-4602 .- 2371-9621. ; 41:3, s. 94-102
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Tidskriftsartikel (refereegranskat)abstract
- Conducting pilot projects are a common approach among organizations to test and evaluate new technology. A pilot project is often conducted to remove uncertainties from a large-scale project and should be limited in time and scope. Nowadays, several organizations are testing and evaluating artificial intelligence techniques and more advanced forms of analytics via pilot projects. Unfortunately, many organizations are experiencing problems in scaling-up the findings from pilot projects to the rest of the organization. Hence, results from pilot projects become siloed with limited business value. In this article, we present an overview of barriers for conducting and scaling-up data-driven pilot projects. Lack of senior management support is a frequently mentioned top barrier in the literature. In response to this, we present our recommendations on what type of activities can be performed, to increase the chances of getting a positive response from senior management regarding scaling-up the usage of artificial intelligence and advanced analytics within an organization.
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| 2. |
- Gulla, Jon Atle, et al.
(författare)
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Recommending news in traditional media companies
- 2021
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Ingår i: The AI Magazine. - : AMER ASSOC ARTIFICIAL INTELL. - 0738-4602. ; 42:3, s. 55-69
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Tidskriftsartikel (refereegranskat)abstract
- The adoption of recommender systems in online news personalization has made it possible to tailor the news stream to the individual interests of each reader. Previous research on commercial recommender systems has emphasized their use in large-scale media houses and technology companies, and real-world experiments indicate substantial improvements of click rates and user satisfaction. It is less understood how smaller media houses are coping with this new technology, how the technology affects their business models, their editorial processes, and their news production in general. Here we report on the experiences from numerous Scandinavian media houses that have experimented with various recommender strategies and streamlined their news production to provide personalized news experiences. In addition to influencing the content and style of news stories and the working environment of journalists, the news recommender systems have been part of a profound digital transformation of the whole media industry. Interestingly, many media houses have found it undesirable to automate the entire recommendation process and look for approaches that combine automatic recommendations with editorial choices.
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| 3. |
- Taitler, Ayal, et al.
(författare)
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The 2023 International Planning Competition
- 2024
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Ingår i: The AI Magazine. - : AMER ASSOC ARTIFICIAL INTELL. - 0738-4602.
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting-edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the competition introduced a combination of well-established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.
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