AI-generated Content (AIGC) holds immense potential, projected to generate trillions of dollars in economic value. However, its true power extends beyond mere content generation; it lies in personalization and customization, tailoring outputs to cater to individual user needs and preferences. As our digital landscape diversifies, encompassing users from diverse cultural backgrounds, languages, and preferences, the need for effective customization strategies becomes increasingly important. Central to this challenge is the role of various computing devices, ranging from consumer devices to edge devices and data centers. Each of these devices brings unique capabilities and opportunities for enhancing the customization of AIGC, facilitating the collection and processing of user data, and allowing for rapid feedback and personalized interactions.
This Research Topic aims to explore and identify innovative methodologies for the customization and personalization of AI-generated content, catering to diverse user groups with varying cultural backgrounds, languages, and preferences. In the data-driven era, this level of customization presents a significant challenge as it necessitates in-depth understanding of user behavior, preferences, and data privacy concerns. This Research Topic will investigate the role of various computing devices, from consumer devices and edge devices to data centers, in the creation of personalized AI-generated content. Specifically, it will delve into the role of consumer devices in user data collection, local processing, and immediate feedback for prompt engineering, while also examining the potential of edge devices in providing low-latency, privacy-preserving AI computations closer to the data source. Furthermore, the topic will explore how data centers, with their extensive computational capacity, facilitate large-scale data analysis, model training, and centralized updates to enhance customization. The topic will also include the synergies between hardware and software in these devices, focusing on their implications for customization, energy efficiency, and user experience. Through this holistic approach, the topic aims to shed light on the intersection of AI-generated content, customization, and the evolving digital infrastructure, paving the way for more personalized and efficient AI applications in the future.
Topics of interest include but are not limited to:
1. Prompt Engineering: exploring techniques for prompt engineering to optimize AI-generated content on consumer devices.
2. Customization Algorithms: developing algorithms for personalizing AI-generated content based on user behavior and preferences.
3. Role of Consumer Devices: discussing consumer devices' role in local user data processing.
4. User Data Collection and Privacy: studying user data collection via devices, considering privacy and ethical aspects.
5. Edge Computing and AI: investigating edge devices' role in providing low-latency, privacy-preserving AI computations.
6. Data Centers and Large-scale Data Processing: exploring data centers' role in large-scale data analysis and customization.
7. Cultural, Linguistic, and Personal Preferences: exploring customization methodologies for diverse user groups with varying backgrounds and preferences.
8. Future of Personalized AI Applications: predicting the future of personalized AI applications considering evolving digital infrastructure.
Keywords:
AI-generated content, Customization, Personalization, Consumer devices, Edge devices, Data centers, User behavior, User preferences, Data privacy, Prompt engineering, Low-latency computations, Large-scale data analysis, Model training, Hardware-software synergies, Energy efficiency, User experience, Diverse user groups, Cultural backgrounds, Linguistic diversity, Future of AI applications
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
AI-generated Content (AIGC) holds immense potential, projected to generate trillions of dollars in economic value. However, its true power extends beyond mere content generation; it lies in personalization and customization, tailoring outputs to cater to individual user needs and preferences. As our digital landscape diversifies, encompassing users from diverse cultural backgrounds, languages, and preferences, the need for effective customization strategies becomes increasingly important. Central to this challenge is the role of various computing devices, ranging from consumer devices to edge devices and data centers. Each of these devices brings unique capabilities and opportunities for enhancing the customization of AIGC, facilitating the collection and processing of user data, and allowing for rapid feedback and personalized interactions.
This Research Topic aims to explore and identify innovative methodologies for the customization and personalization of AI-generated content, catering to diverse user groups with varying cultural backgrounds, languages, and preferences. In the data-driven era, this level of customization presents a significant challenge as it necessitates in-depth understanding of user behavior, preferences, and data privacy concerns. This Research Topic will investigate the role of various computing devices, from consumer devices and edge devices to data centers, in the creation of personalized AI-generated content. Specifically, it will delve into the role of consumer devices in user data collection, local processing, and immediate feedback for prompt engineering, while also examining the potential of edge devices in providing low-latency, privacy-preserving AI computations closer to the data source. Furthermore, the topic will explore how data centers, with their extensive computational capacity, facilitate large-scale data analysis, model training, and centralized updates to enhance customization. The topic will also include the synergies between hardware and software in these devices, focusing on their implications for customization, energy efficiency, and user experience. Through this holistic approach, the topic aims to shed light on the intersection of AI-generated content, customization, and the evolving digital infrastructure, paving the way for more personalized and efficient AI applications in the future.
Topics of interest include but are not limited to:
1. Prompt Engineering: exploring techniques for prompt engineering to optimize AI-generated content on consumer devices.
2. Customization Algorithms: developing algorithms for personalizing AI-generated content based on user behavior and preferences.
3. Role of Consumer Devices: discussing consumer devices' role in local user data processing.
4. User Data Collection and Privacy: studying user data collection via devices, considering privacy and ethical aspects.
5. Edge Computing and AI: investigating edge devices' role in providing low-latency, privacy-preserving AI computations.
6. Data Centers and Large-scale Data Processing: exploring data centers' role in large-scale data analysis and customization.
7. Cultural, Linguistic, and Personal Preferences: exploring customization methodologies for diverse user groups with varying backgrounds and preferences.
8. Future of Personalized AI Applications: predicting the future of personalized AI applications considering evolving digital infrastructure.
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Keywords:
AI-generated content, Customization, Personalization, Consumer devices, Edge devices, Data centers, User behavior, User preferences, Data privacy, Prompt engineering, Low-latency computations, Large-scale data analysis, Model training, Hardware-software synergies, Energy efficiency, User experience, Diverse user groups, Cultural backgrounds, Linguistic diversity, Future of AI applications
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
