Two Decades of AI4NETS-AI/ML for Data Networks: Challenges & Research Directions

• URL: http://arxiv.org/abs/2003.04080v1
• Date: Tue, 3 Mar 2020 00:36:17 GMT
• Title: Two Decades of AI4NETS-AI/ML for Data Networks: Challenges & Research Directions
• Authors: Pedro Casas
• Abstract summary: The popularity of Artificial Intelligence (AI) -- and of Machine Learning (ML) as an approach to AI, has dramatically increased in the last few years. Despite many attempts to turn networks into learning agents, the successful application of AI/ML in networking is limited. There is a strong resistance against AI/ML-based solutions, and a striking gap between the extensive academic research and the actual deployments.
• Score: 4.9469703779632415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The popularity of Artificial Intelligence (AI) -- and of Machine Learning (ML) as an approach to AI, has dramatically increased in the last few years, due to its outstanding performance in various domains, notably in image, audio, and natural language processing. In these domains, AI success-stories are boosting the applied field. When it comes to AI/ML for data communication Networks (AI4NETS), and despite the many attempts to turn networks into learning agents, the successful application of AI/ML in networking is limited. There is a strong resistance against AI/ML-based solutions, and a striking gap between the extensive academic research and the actual deployments of such AI/ML-based systems in operational environments. The truth is, there are still many unsolved complex challenges associated to the analysis of networking data through AI/ML, which hinders its acceptability and adoption in the practice. In this positioning paper I elaborate on the most important show-stoppers in AI4NETS, and present a research agenda to tackle some of these challenges, enabling a natural adoption of AI/ML for networking. In particular, I focus the future research in AI4NETS around three major pillars: (i) to make AI/ML immediately applicable in networking problems through the concepts of effective learning, turning it into a useful and reliable way to deal with complex data-driven networking problems; (ii) to boost the adoption of AI/ML at the large scale by learning from the Internet-paradigm itself, conceiving novel distributed and hierarchical learning approaches mimicking the distributed topological principles and operation of the Internet itself; and (iii) to exploit the softwarization and distribution of networks to conceive AI/ML-defined Networks (AIDN), relying on the distributed generation and re-usage of knowledge through novel Knowledge Delivery Networks (KDNs).

Related papers

• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
  This paper presents a comprehensive overview of AI and communication for 6G networks. We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
  arXiv  Detail & Related papers  (2024-12-19T05:36:34Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks [1.5999407512883512]
  This paper discusses the role of AI/ML in current and future Wi-Fi networks. Key challenges, standardization efforts, and major enablers are also discussed. An exemplary use case is provided to showcase the potential of AI/ML in Wi-Fi at different adoption stages.
  arXiv  Detail & Related papers  (2024-05-19T10:12:20Z)
• 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)
• Confident AI [0.0]
  We propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability.
  arXiv  Detail & Related papers  (2022-02-12T02:26:46Z)
• 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)
• Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable [5.4138734778206]
  Deep neural networks have led to many successes and new capabilities in computer vision, speech recognition, language processing, game-playing, and robotics. A concerning limitation is that even the most successful of today's AI systems suffer from brittleness. AI systems also can absorb biases-based on gender, race, or other factors-from their training data and further magnify these biases in their subsequent decision-making.
  arXiv  Detail & Related papers  (2020-12-11T00:50:09Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Problems in AI research and how the SP System may help to solve them [0.0]
  This paper describes problems in AI research and how the SP System may help to solve them. Most of the problems are described by leading researchers in AI in interviews with science writer Martin Ford.
  arXiv  Detail & Related papers  (2020-09-02T11:33:07Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

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.