Artificial Intelligence at the Edge
- URL: http://arxiv.org/abs/2012.05410v1
- Date: Thu, 10 Dec 2020 02:08:47 GMT
- Title: Artificial Intelligence at the Edge
- Authors: Elisa Bertino and Sujata Banerjee
- Abstract summary: 5G mobile communication networks increase communication capacity, reduce transmission latency and error, and save energy.
The envisioned future 6G technology will integrate many more technologies, including for example visible light communication.
Many applications require computations and analytics close to application end-points: that is, at the edge of the network, rather than in a centralized cloud.
- Score: 25.451110446336276
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Internet of Things (IoT) and edge computing applications aim to support a
variety of societal needs, including the global pandemic situation that the
entire world is currently experiencing and responses to natural disasters.
The need for real-time interactive applications such as immersive video
conferencing, augmented/virtual reality, and autonomous vehicles, in education,
healthcare, disaster recovery and other domains, has never been higher. At the
same time, there have been recent technological breakthroughs in highly
relevant fields such as artificial intelligence (AI)/machine learning (ML),
advanced communication systems (5G and beyond), privacy-preserving
computations, and hardware accelerators. 5G mobile communication networks
increase communication capacity, reduce transmission latency and error, and
save energy -- capabilities that are essential for new applications. The
envisioned future 6G technology will integrate many more technologies,
including for example visible light communication, to support groundbreaking
applications, such as holographic communications and high precision
manufacturing. Many of these applications require computations and analytics
close to application end-points: that is, at the edge of the network, rather
than in a centralized cloud. AI techniques applied at the edge have tremendous
potential both to power new applications and to need more efficient operation
of edge infrastructure. However, it is critical to understand where to deploy
AI systems within complex ecosystems consisting of advanced applications and
the specific real-time requirements towards AI systems.
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) - Reducing the Barriers to Entry for Foundation Model Training [0.28756346738878485]
The world has recently witnessed an unprecedented acceleration in demands for Machine Learning and Artificial Intelligence applications.
This spike in demand has imposed tremendous strain on the underlying technology stack in supply chain.
We propose a fundamental change in the AI training infrastructure throughout the technology ecosystem.
arXiv Detail & Related papers (2024-04-12T20:58:25Z) - 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) - Tactile based Intelligence Touch Technology in IoT configured WCN in
B5G/6G-A Survey [8.604882842499208]
This study proposes an intelligent touch-enabled system for B5G/6G and IoT based wireless communication network that incorporates the AR/VR technologies.
The tactile internet and network slicing serve as the backbone of the touch technology which incorporates intelligence from techniques such as AI/ML/DL.
It is anticipated for the next generation system to provide numerous opportunities for various sectors utilizing AR/VR technology in robotics and healthcare facilities.
arXiv Detail & Related papers (2023-01-11T06:39:07Z) - Future Computer Systems and Networking Research in the Netherlands: A
Manifesto [137.47124933818066]
We draw attention to CompSys as a vital part of ICT.
Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
arXiv Detail & Related papers (2022-05-26T11:02:29Z) - 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) - Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and
Applications [39.223546118441476]
6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence"
Deep learning and big data analytics based AI systems require tremendous computation and communication resources.
edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
arXiv Detail & Related papers (2021-11-24T11:47:16Z) - 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) - Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms.
The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
arXiv Detail & Related papers (2021-01-25T13:01:16Z) - 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) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)
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.