Sep 2026· Advances in computational intelligence and robotics book series· 34 references
Abstract
Ambient Intelligence (AmI) is a new vision of human–technology interaction enabling digital environments to be reactive, adaptable, and attentive to the needs of individuals. The symbiotic complementary roles of four facilitating technologies Internet of Things (IoT), Artificial Intelligence (AI), Edge Computing, and Cloud Computing for assigning AmI ecosystems are investigated in this article. In a comparative study through violin plots, line graphs, and heatmaps, this study evaluates the performance of these technologies in different application areas including smart homes, healthcare, industrial automation, and city infrastructure. From empirical facts of primary sources such as real-world case studies and industry surveys, a layered integration model stands out as the most scalable and efficient Ambient Intelligence model. The study also explores issues like latency, interoperability, energy efficiency, and data privacy. It combines theoretical insights and application-based evaluation to identify a comprehensive model for the creation of future-proof, smart environments.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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