| 1. |
- Ahlstrand, Jim, et al.
(författare)
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Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles
- 2023
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Ingår i: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023. - : Linköping University Electronic Press. - 9789180752749 ; , s. 68-76
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Konferensbidrag (refereegranskat)abstract
- During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.
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| 2. |
- Arvidsson, Victor, et al.
(författare)
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Evaluation of Defense Methods Against the One-Pixel Attack on Deep Neural Networks
- 2023
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Ingår i: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023. - : Linköping University Electronic Press. - 9789180752749 ; , s. 49-57
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Konferensbidrag (refereegranskat)abstract
- The one-pixel attack is an image attack method for creating adversarial instances with minimal perturbations, i.e., pixel modification. The attack method makes the adversarial instances difficult to detect as it only manipulates a single pixel in the image. In this paper, we study four different defense approaches against adversarial attacks, and more specifically the one-pixel attack, over three different models. The defense methods used are: data augmentation, spatial smoothing, and Gaussian data augmentation used during both training and testing. The empirical experiments involve the following three models: all convolutional network (CNN), network in network (NiN), and the convolutional neural network VGG16. Experiments were executed and the results show that Gaussian data augmentation performs quite poorly when applied during the prediction phase. When used during the training phase, we see a reduction in the number of instances that could be perturbed by the NiN model. However, the CNN model shows an overall significantly worse performance compared to no defense technique. Spatial smoothing shows an ability to reduce the effectiveness of the one-pixel attack, and it is on average able to defend against half of the adversarial examples. Data augmentation also shows promising results, reducing the number of successfully perturbed images for both the CNN and NiN models. However, data augmentation leads to slightly worse overall model performance for the NiN and VGG16 models. Interestingly, it significantly improves the performance for the CNN model. We conclude that the most suitable defense is dependent on the model used. For the CNN model, our results indicate that a combination of data augmentation and spatial smoothing is a suitable defense setup. For the NiN and VGG16 models, a combination of Gaussian data augmentation together with spatial smoothing is more promising. Finally, the experiments indicate that applying Gaussian noise during the prediction phase is not a workable defense against the one-pixel attack. ©2023, Copyright held by the authors
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| 3. |
- Olsson, Ella, et al.
(författare)
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Urdarbrunnen: Towards an AI-enabled mission system for Combat Search and Rescue operations
- 2023
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Ingår i: Proceedings of the 35th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2023). - : Linköping University Electronic Press. - 9789180752749 ; , s. 38-45
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Konferensbidrag (refereegranskat)abstract
- The Urdarbrunnen project is a Saab-led exploratory initiative that aims to develop an operator-assisted AI-enabled mission system for basic autonomous functions. In its first iteration, presented in this project paper, the system is designed to be capable of performing the search task of a combat search and rescue mission in a complex and dynamic environment, while providing basic human machine interaction support for remote operators. The system enables a team of agents to cooperatively plan and execute a search mission while also interfacing with the WARA-PS core system that allows human operators and other agents to monitor activities and interact with each other. The aim of the project is to develop the system iteratively, with each iteration incorporating feedback from simulations and real-world experiments. In future work, the capability of the system will be extended to incorporate additional tasks for other scenarios, making it a promising starting point for the integration of autonomous capabilities in a future air force.
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| 4. |
- Ryazanov, Igor, 1996-, et al.
(författare)
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How does the language of 'threat' vary across news domains? : a semi-supervised pipeline for understanding narrative components in news contexts
- 2023
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Ingår i: SAIS 2023. - : Swedish Artificial Intelligence Society. - 9789180752749 ; , s. 94-99
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Konferensbidrag (refereegranskat)abstract
- By identifying and characterising the narratives told in news media we can better understand political and societal processes. The problem is challenging from the perspective of natural language processing because it requires a combination of quantitative and qualitative methods. This paper reports on work in progress, which aims to build a human-in-the-loop pipeline for analysing how the variation of narrative themes across different domains, based on topic modelling and word embeddings. As an illustration, we study the language associated with the threat narrative in British news media.
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| 5. |
- Sundstedt, Veronica, 1979-, et al.
(författare)
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HINTS : Human-Centered Intelligent Realities
- 2023
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Ingår i: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023. - : Linköping University Electronic Press. - 9789180752749 ; , s. 9-17
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Konferensbidrag (refereegranskat)abstract
- During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors
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