Bridging the Gap Between Java and Python in Mobile Software Development to Enable MLOps

Dautov, Rustem; Husom, Erik Johannes; Gonidis, Fotis; Papatzelos, Spyridon; Malamas, Nikolaos

doi:10.1109/wimob55322.2022.9941679

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…This enables near-realtime inference, reduces latency, saves bandwidth, enhances privacy, and enables offline functionality even in the absence of the Internet connection. All these features are especially important to the healthcare domain where physiological data collected by wearable or portable medical devices are processed either directly on those devices or on a smartphone acting as a wireless gateway [6], [7]. Similarly, the data privacy and network bandwidth constraints are usually critical aspects in various image and video recognition scenarios involving CCTV cameras [8], [9].…”

Section: B Edge Artificial Intelligencementioning

confidence: 99%

Towards Community-Driven Generative AI

Dautov,

Husom,

Sen

et al. 2023

Annals of Computer Science and Information Systems

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While the emerging market of Generative Artificial Intelligence (AI) is increasingly dominated and controlled by the Tech Giants, there is also a growing interest in open-source AI code and models from smaller companies, research organisations and individual users. They often have valuable data that could be used for training, but their computing resources are limited, while data privacy concerns prevent them from sharing this data for public training. A possible solution to overcome these two issues is to utilise the crowd-souring principles and apply federated learning techniques to build a distributed privacypreserving architecture for training Generative AI. This paper discusses how these two key enablers, together with some other emerging technologies, can be effectively combined to build a community-driven Generative AI ecosystem, allowing even small actors to participate in the training of Generative AI models by securely contributing their training data. The paper also discusses related non-technical issues, such as the role of the community and intellectual property rights, and outlines further research directions associated with AI moderation.

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