An Empirical Study of AI Techniques in Mobile Applications

• URL: http://arxiv.org/abs/2212.01635v3
• Date: Fri, 27 Sep 2024 09:45:07 GMT
• Title: An Empirical Study of AI Techniques in Mobile Applications
• Authors: Yinghua Li, Xueqi Dang, Haoye Tian, Tiezhu Sun, Zhijie Wang, Lei Ma, Jacques Klein, Tegawendé F. Bissyandé,
• Abstract summary: We conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study has strong implications for AI app developers, users, and AI R&D.
• Score: 10.43634556488264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. In this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: 1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; 2) Framework and model analysis, where we analyze AI framework usage and AI model protection; 3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R\&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

Related papers

• Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
  We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
  arXiv  Detail & Related papers  (2024-10-28T23:10:06Z)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [56.44953602790945]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. This article offers an exhaustive tutorial for novices in NSVAD. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• Rapid Mobile App Development for Generative AI Agents on MIT App Inventor [12.091916451641021]
  We present a methodology for the rapid development of AI agent applications using the development platform provided by MIT App Inventor. We share the development journey of three distinct mobile applications: SynchroNet for fostering sustainable communities; ProductiviTeams for addressing procrastination; and iHELP for enhancing community safety.
  arXiv  Detail & Related papers  (2024-04-01T02:35:19Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Apps [17.630597106970466]
  A good interaction design is important to make an AI feature usable and understandable. Existing guidelines and tools either do not cover AI features or consider mobile apps. We conducted the first empirical study to explore user-AI-interaction in mobile apps.
  arXiv  Detail & Related papers  (2023-07-03T05:04:34Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• A Comprehensive Study on Artificial Intelligence Algorithms to Implement Safety Using Communication Technologies [1.2710179245406195]
  The study aims at providing a comprehensive picture of the state of the art AI based safety solutions that uses different communication technologies. The results demonstrate that automotive domain is the one applying AI and communication the most to implement safety. The use of non-cellular communication technologies is dominant however a clear trend of a rapid increase in the use of cellular communication is observed specially from 2020 with the roll-out of 5G technology.
  arXiv  Detail & Related papers  (2022-05-17T14:38:38Z)
• AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap [0.0]
  This paper focuses on the applications of artificial intelligence in authentication. With the emerging AI-assisted authentication schemes, our survey provides an overall understanding on a high level. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication.
  arXiv  Detail & Related papers  (2022-04-25T21:16:55Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Quality Management of Machine Learning Systems [0.0]
  Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. This paper presents a view of a holistic quality management framework for ML applications based on the current advances.
  arXiv  Detail & Related papers  (2020-06-16T21:34:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.