Toward Artificial General Intelligence: Deep Learning, Neural Networks, Generative AI

Toward Artificial General Intelligence: Deep Learning, Neural Networks, Generative AI
Author: Victor Hugo C. de Albuquerque (Author), Pethuru Raj (Author), Satya Prakash Yadav (Author)
Publisher finelybook 出版社: De Gruyter
Publication Date 出版日期: 2023-12-04
Language 语言: English
Print Length 页数: 440 pages
ISBN-10: 3111323560
ISBN-13: 9783111323565

Artifi cial Intelligence (AI) has been an exciting fi eld of study and research in educational institutions and research labs across the globe. Technology giants and IT organizations invest heavily on AI technologies and tools with the aim of preciselyautomating a variety of simple as well as complicated business operations acrossindustry verticals. This book covers the latest trends and transitions happening in thefuturistic AI domain. The book also focuses on machine and deep learning (ML/DL)algorithms, which are, undoubtedly, the mainstream implementation technologies ofstate-of-the-art AI systems and services. Also, there are chapters on computer vision(CV) and natural language processing (NLP), the primary use cases and applicationsof AI. The book has well-written chapters for demystifying AI model engineeringmethods. Further on, our esteemed readers can fi nd details on AI model evaluation, optimization, deployment and observability. Finally, the book deals and describesgenerative AI, the latest buzzword in the IT industry.

The book

presents the recent ground-breaking changes taking place in the aspects of AI model building, hosting, running and maintaining in cloud environments,

articulates and accentuates the most recent developments taking place in the

domain of Artifi cial Intelligence,

covers the noteworthy innovations and disruptions towards Generative Artifi cial

Intelligence (Generative AI),

explains the breakthrough innovations and disruptions towards Artifi cial General

Intelligence (AGI)

and delineates an engaging discussion of Natural Language Processing, Neuromorphic Systems and Biometrics.