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Recent Innovations in Artificial Intelligence and Smart Applications
Recent Innovations in Artificial Intelligence and Smart ApplicationsНазвание: Recent Innovations in Artificial Intelligence and Smart Applications
Автор: Mostafa Al-Emran, Khaled Shaalan
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2022
Страниц: 387
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book tackles the recent research trends on the role of AI in advancing automotive manufacturing, augmented reality, sustainable development in smart cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical Machine Learning, Deep Learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, Big data, and natural language processing (NLP). The edited book covers empirical and reviews studies that primarily concentrate on the aforementioned issues, which would assist scholars in pursuing future research in the domain and identifying the possible future developments of AI applications.

The rapid emergence of Deep Learning (DL) algorithms has paved the way for bringing Artificial Intelligence (AI) services to end users. The intersection between edge computing and AI has brought an exciting area of research called edge artificial intelligence (Edge AI). Edge AI has enabled a paradigm shift in many application areas such as precision medicine, wearable sensors, intelligent robotics,industry, and agriculture. The training and inference of DL algorithms are migrating from the cloud to the edge. Computationally expensive, memory and power-hungry DL algorithms are optimized to leverage the full potential of Edge AI. Embedding intelligence on the edge devices such as the internet of things (IoT), smartphones, and cyber-physical systems (CPS) can ensure user privacy and data security.

Edge AI eliminates the need for cloud transmission through processing near the source of data and significantly reduces the latency; enabling real-time, learned, and automatic decision-making. However, the computing resources at the edge suffer from power and memory constraints. Various compression and optimization techniques have been developed in both the algorithm and the hardware to overcome the resource constraints of edge. In addition, algorithm-hardware codesign has emerged as a crucial element to realize the efficient Edge AI. This chapter focuses on each component of integrating DL into Edge AI such as model compression, algorithm hardware codesign, available edge hardware platforms, and challenges and future

Chatbots that Use AI. People who use a “live chat” option for customer service can be identified and helped by AI. AI chatbots use Machine Learning and can be used on various websites and apps. A database of responses and data can be built by AI chatbots. They can also get data from a set of integrated responses. Customers can be sure that these chatbots will be able to answer their questions and help them around the clock as AI improves. Customers may be able to use these AI chat-bots to their advantage.

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