With increasing technology development we can see many industrial leaders and technologies taking interest in artificial intelligence. Many industries are assimilating artificial intelligence into their workflows and productions. Disrupting the industries with AI applications has become a trend in today’s world. We often think about how industries are applying AI to their products and workflows. On that note let’s go through the below list presenting top books on the use of AI in industries.
Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI
Distinguished industry thought leader, Randy Bean, has introduced Fail Fast, Learn Faster. The author discussed the story of the rise of Big Data and its impact, its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI.
Big Data is indeed crossing the void that isolates early adopters from the mainstream of enterprise customers. As Randy Bean makes abundantly clear in Fail Fast, Learn Faster, this crossing is by no means smooth. Through a wonderful hoard of business anecdotes, he shows over and over again that the challenge of becoming a data-driven business has little to do with the Big Data itself, and is only marginally about mastering the technology needed to harness it. Rather, it is primarily about leadership teams finding forcing functions that can drive massive change in the roles, processes, and systems that make up their enterprise. For some, the forcing function might be regulatory demands, for others a global market demanding extreme cost reductions, an emerging competitive threat from digital disrupters, and a mission to solve customer problems that cannot be addressed by conventional means.
Industrial AI: Applications with Sustainable Performance
Prof. Jay Lee an Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, introduced the book Industrial AI.
This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline that focuses on developing, validating, and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication, and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in its infancy stage and may encompass the collective use of technologies such as the Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects, and the physical environment interact with each other to reach intended goals. A broad range of industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further Use of AI in industries that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remaining Use of AI in industries life and further optimize maintenance tasks to avoid disruption to operations.
Artificial Intelligence and Industrial Applications: Artificial Intelligence Techniques for Cyber-Physical, Digital Twin Systems, and Engineering Applications
The editors of the book are Masrour, Tawfik, El Hassani, Ibtissam, Cherrafi, Anass.
This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA’2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. It addresses various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization, and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
Artificial Intelligence and Machine Learning in Industry
The book is introduced by David Beyer. The growth of businesses centered on artificial intelligence and machine learning makes it clear that automation will fundamentally reshape industry and society. But this will only happen after a broader sweep and scrutiny Use of AI in industries and its economic, social, and political influence, from scholars and policymakers alike. This report adds to this discussion through interviews with the entrepreneurs and executives on the front lines of AI, machine learning, and industry.
Industrial Machine Learning: Using Artificial Intelligence as a Transformational Disruptor
Introduced by Andreas François Vermeulen who is the Chief Data Scientist and Solutions Delivery Manager at Sopra-Steria and he serves as a part-time doctoral researcher and senior research project advisor at the University of St. Andrews on future concepts in health care systems, Internet of Things (IoT) sensors, massive distributed computing, mechatronics, at-scale data lake technology, data science, business intelligence (BI), and deep machine learning in health informatics.
This book helps in understanding the industrialization of machine learning (ML) and taking the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). you can learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science.
The book covers supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors.