A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks
- URL: http://arxiv.org/abs/2404.06946v1
- Date: Wed, 10 Apr 2024 11:55:33 GMT
- Title: A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks
- Authors: Athanasios Karapantelakis, Alexandros Nikou, Ajay Kattepur, Jean Martins, Leonid Mokrushin, Swarup Kumar Mohalik, Marin Orlic, Aneta Vulgarakis Feljan,
- Abstract summary: Mobile networks are expected to broaden their services and coverage to accommodate a larger user base and diverse user needs.
This paper examines the current status of GenAI algorithms with critical thinking capabilities and investigates their potential applications in telecom networks.
- Score: 35.117146617535184
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In the near future, mobile networks are expected to broaden their services and coverage to accommodate a larger user base and diverse user needs. Thus, they will increasingly rely on artificial intelligence (AI) to manage network operation and control costs, undertaking complex decision-making roles. This shift will necessitate the application of techniques that incorporate critical thinking abilities, including reasoning and planning. Symbolic AI techniques already facilitate critical thinking based on existing knowledge. Yet, their use in telecommunications is hindered by the high cost of mostly manual curation of this knowledge and high computational complexity of reasoning tasks. At the same time, there is a spurt of innovations in industries such as telecommunications due to Generative AI (GenAI) technologies, operating independently of human-curated knowledge. However, their capacity for critical thinking remains uncertain. This paper aims to address this gap by examining the current status of GenAI algorithms with critical thinking capabilities and investigating their potential applications in telecom networks. Specifically, the aim of this study is to offer an introduction to the potential utilization of GenAI for critical thinking techniques in mobile networks, while also establishing a foundation for future research.
Related papers
- Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces.
While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks.
This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Causal Reasoning: Charting a Revolutionary Course for Next-Generation
AI-Native Wireless Networks [63.246437631458356]
Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native.
This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning.
We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
arXiv Detail & Related papers (2023-09-23T00:05:39Z) - Towards Artificial General Intelligence (AGI) in the Internet of Things
(IoT): Opportunities and Challenges [55.82853124625841]
Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities.
This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things.
The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
arXiv Detail & Related papers (2023-09-14T05:43:36Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - A Survey on Explainable Artificial Intelligence for Cybersecurity [14.648580959079787]
Explainable Artificial Intelligence (XAI) aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions.
In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats.
arXiv Detail & Related papers (2023-03-07T22:54:18Z) - 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) - Pervasive AI for IoT Applications: Resource-efficient Distributed
Artificial Intelligence [45.076180487387575]
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services.
This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams.
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
arXiv Detail & Related papers (2021-05-04T23:42:06Z) - Interdisciplinary Approaches to Understanding Artificial Intelligence's
Impact on Society [7.016365171255391]
AI has come with a storm of unanticipated socio-technical problems.
We need tighter coupling of computer science and those disciplines that study society and societal values.
arXiv Detail & Related papers (2020-12-11T00:43:47Z)
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.