Continual And Reinforcement Learning For Edge Ai
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Continual and Reinforcement Learning for Edge AI
This book provides a comprehensive introduction to continual and reinforcement learning for edge AI, which investigates how to build an AI agent that can continuously solve new learning tasks and enhance the AI at resource-limited edge devices. The authors introduce readers to practical frameworks and in-depth algorithmic foundations. The book surveys the recent advances in the area, coming from both academic researchers and industry professionals. The authors also present their own research findings on continual and reinforcement learning for edge AI. The book also covers the practical applications of the topic and identifies exciting future research opportunities.
Reinforcement Learning
Author: Richard S. Sutton
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
EDGE AI: MERGING IOT AND MACHINE LEARNING FOR REAL-TIME ANALYTICS
Author: Dr. D. Srinivasa Rao
language: en
Publisher: Xoffencer International Book Publication house
Release Date: 2024-10-10
In order to provide real-time analytics directly at the edge of the network, edge artificial intelligence (AI) is a disruptive technique that combines the capabilities of Internet of Things (IoT) devices with the power of machine learning (ML). As a result of this paradigm shift away from conventional cloud-centric approaches, latency is reduced, privacy is improved, and operational efficiency is increased. Information is processed locally on devices. The Internet of Things (IoT) is experiencing exponential expansion, which presents a problem for centralized cloud processing due to the sheer amount of data created by sensors, cameras, and linked equipment of all kinds. By putting artificial intelligence closer to the source of the data, Edge AI makes it possible to make decisions more quickly and reduces the need for continual data transmission to the cloud, which in turn reduces the expenses associated with bandwidth and cloud storage. Innovation is fostered across a variety of sectors, including healthcare, smart cities, autonomous cars, and industrial automation, via the integration of the Internet of Things (IoT) and machine learning at the edge. Real-time analytics makes it possible to identify trends and irregularities, which in turn leads to improvements in accessibility and efficiency in areas such as tailored services, increased security, and predictive maintenance. Utilizing on-device machine learning models enables quick insights, which is essential in applications that are time-sensitive. This is also true as Internet of Things devices grow more sophisticated. Furthermore, the infrastructure for edge computing is capable of supporting dispersed systems, which not only ensures increased system resilience but also reduces the likelihood of downtime. Nevertheless, putting Edge AI into practice is not without its difficulties. The management of the computational needs of machine learning models on devices with limited resources, the maintenance of scalability, and the guarantee of security across dispersed nodes are all key concerns that need to be addressed. The development of lightweight machine learning models, hardware that has been optimized, and security mechanisms that have been improved are all essential components in promoting the widespread use of this technology. Furthermore, the continuing developments in 5G networks and edge computing frameworks promise to push the frontiers of edge artificial intelligence, which will offer up new opportunities for real-time, decentralized intelligence. In conclusion, Edge AI is able to bridge the gap between the increasing needs of Internet of Things ecosystems and the requirement for real-time insights that can be put into action. With the ability to facilitate decision-making processes that are quicker, more intelligent, and more secure, it has the potential to completely transform whole sectors. Artificial intelligence at the edge of the network will play a crucial part in determining the future of intelligent systems as technology continues to advance