| 1. |
- Bozic, Nina, et al.
(författare)
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Integrated AI and Innovation Management : The Beginning of a Beautiful Friendship
- 2020
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Ingår i: Technology Innovation Management Review. - : Talent First Network. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.
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| 2. |
- Bozic, Nina, et al.
(författare)
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Integrated ai and innovationmanagement : The beginning of a beautiful friendship
- 2021
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Ingår i: Technology Innovation Management Review. - : Carleton University. - 1927-0321. ; 10:11, s. 5-18
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.
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| 3. |
- Isaksson, Martin, et al.
(författare)
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Adaptive Expert Models for Personalization in Federated Learning
- 2022
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Ingår i: International Workshop on Trustworthy Federated Learningin Conjunction with IJCAI 2022 (FL-IJCAI'22).
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Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning whendata is private and sensitive. However, the state-of-the-art solutions in thisframework are not optimal when data is heterogeneous and non-Independent andIdentically Distributed (non-IID). We propose a practical and robust approachto personalization in FL that adjusts to heterogeneous and non-IID data bybalancing exploration and exploitation of several global models. To achieve ouraim of personalization, we use a Mixture of Experts (MoE) that learns to groupclients that are similar to each other, while using the global models moreefficiently. We show that our approach achieves an accuracy up to 29.78 % andup to 4.38 % better compared to a local model in a pathological non-IIDsetting, even though we tune our approach in the IID setting.
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| 4. |
- Isaksson, Martin, et al.
(författare)
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Adaptive Expert Models for Federated Learning
- 2023
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Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
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Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
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| 5. |
- John, Meenu Mary, et al.
(författare)
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Advancing MLOps from Ad hoc to Kaizen
- 2023
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Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350342352 - 9798350342369
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Konferensbidrag (refereegranskat)abstract
- Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.
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| 6. |
- John, Meenu Mary, et al.
(författare)
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Advancing MLOps from Ad hoc to Kaizen
- 2023
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Ingår i: Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023. ; , s. 94-101
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Konferensbidrag (refereegranskat)abstract
- Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.
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| 7. |
- John, Meenu Mary, et al.
(författare)
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Exploring Trade-Offs in MLOps Adoption
- 2023
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE Computer Society Digital Library. - 1530-1362. ; 30th Asia-Pacific Software Engineering Conference, APSEC 2023, s. 369-375
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Konferensbidrag (refereegranskat)abstract
- Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies. Based on our empirical results, we identify the key trade-offs we see companies make when adopting MLOps. We categorise these trade-offs into four concerns of the BAPO model: Business, Architecture, Process, and Organisation. Finally, we provide suggestions to mitigate the identified trade-offs. By identifying and detailing these trade-offs and the implications of these, this research helps companies to ensure the successful adoption of MLOps.
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| 8. |
- Martinsson, John, et al.
(författare)
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Adversarial representation learning for synthetic replacement of private attributes
- 2021
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Ingår i: Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665439022 ; , s. 1291-1299
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Konferensbidrag (refereegranskat)abstract
- Data privacy is an increasingly important aspect of many real-world analytics tasks. Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding the right balance between privacy and utility is often a tricky trade-off. In this work, we propose a novel approach for data privatization, which involves two steps: in the first step, it removes the sensitive information, and in the second step, it replaces this information with an independent random sample. Our method builds on adversarial representation learning which ensures strong privacy by training the model to fool an increasingly strong adversary. While previous methods only aim at obfuscating the sensitive information, we find that adding new random information in its place strengthens the provided privacy and provides better utility at any given level of privacy. The result is an approach that can provide stronger privatization on image data, and yet be preserving both the domain and the utility of the inputs, entirely independent of the downstream task.
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| 9. |
- Zec, Edvin Listo, et al.
(författare)
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Efficient Node Selection in Private Personalized Decentralized Learning
- 2024
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Ingår i: Proceedings of the 5th Northern Lights Deep Learning Conference, NLDL 2024. - : ML Research Press.
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Konferensbidrag (refereegranskat)abstract
- Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data. However, this approach poses significant privacy risks, as nodes may inadvertently disclose sensitive information about their data or preferences through their collaboration choices. In this paper, we propose Private Personalized Decentralized Learning (PPDL), a novel approach that combines secure aggregation and correlated adversarial multi-armed bandit optimization to protect node privacy while facilitating efficient node selection. By leveraging dependencies between different arms, represented by potential collaborators, we demonstrate that PPDL can effectively identify suitable collaborators solely based on aggregated models. Additionally, we show that PPDL surpasses previous non-private methods in model performance on standard benchmarks under label and covariate shift scenarios.
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