A Comprehensive Survey on Artificial Intelligence Empowered Edge Computing on Consumer Electronics

Syu, Jia-Hao; Lin, Jerry Chun-Wei; Srivastava, Gautam; Yu, Keping

doi:10.1109/tce.2023.3318150

Cited by 12 publications

(2 citation statements)

References 114 publications

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“…Interactive AI focuses on the human-computer interaction aspect of generative AI technology [30]. Concrete examples of generative, interactive AI encompass software such as chatbot platforms for customer service; content management systems that help summarize unstructured data [38]; synthetic media generation for creative work; and hardware such as voice assistants [39], automated vehicles, and robots within the realm of consumer electronics [40].…”

Section: Defining Generative/interactive Aimentioning

confidence: 99%

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

Novozhilova,

Mays,

Paik

et al. 2024

MAKE

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Modern AI applications have caused broad societal implications across key public domains. While previous research primarily focuses on individual user perspectives regarding AI systems, this study expands our understanding to encompass general public perceptions. Through a survey (N = 1506), we examined public trust across various tasks within education, healthcare, and creative arts domains. The results show that participants vary in their trust across domains. Notably, AI systems’ abilities were evaluated higher than their benevolence across all domains. Demographic traits had less influence on trust in AI abilities and benevolence compared to technology-related factors. Specifically, participants with greater technological competence, AI familiarity, and knowledge viewed AI as more capable in all domains. These participants also perceived greater systems’ benevolence in healthcare and creative arts but not in education. We discuss the importance of considering public trust and its determinants in AI adoption.

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Section: Defining Generative/interactive Aimentioning

confidence: 99%

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

Novozhilova,

Mays,

Paik

et al. 2024

MAKE

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“…In recent years a number of authors have addressed the topic of combining edge-based IT solutions with technologies like Blockchain and 5G networks [8]. There has also been an increase in the number of articles dealing with the application of Edge Computing in Smart Healthcare and intelligent analysis in recent years [14], as well as combining Edge Computing and technologies such as digital twins, that can be used to provide support for remote working, retail industries and digital advertising [13].…”

Section: Current Trendsmentioning

confidence: 99%

Opportunities and Pitfalls of IT Architectures for Edge Computing

Četina,

Pavlič

2024

Frontiers in Artificial Intelligence and Applications

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Networks and data centres are under a lot of stress due to the rapid growth of connected devices and the significant amount of data that they produce. While data centres are struggling with heavy workloads, the cloud-native IT solutions underutilise the increasingly powerful clients. By bringing computational capabilities closer to the edge, the Edge Computing direction offers a way to alleviate the pressure on data centres, reduce network traffic, and opens the way for novel applications that would take full advantage of the benefits it brings. Edge Computing can also supplement existing applications by giving them the resources to run new, more complex operations, or improve existing ones partially. Edge applications are less reliant on the cloud, and provide more stability and customisation to globally distributed systems.

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LSSMSD: defending against black-box DNN model stealing based on localized stochastic sensitivity

Zhang,

Chen,

et al. 2024

Int. J. Mach. Learn. & Cyber.

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A Comprehensive Survey on Artificial Intelligence Empowered Edge Computing on Consumer Electronics

Cited by 12 publications

References 114 publications

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

Opportunities and Pitfalls of IT Architectures for Edge Computing

LSSMSD: defending against black-box DNN model stealing based on localized stochastic sensitivity

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