Artificial Intelligence has transformative impact on precision medicine, structural health monitoring, and autonomous manufacturing systems, revolutionizing traditional approaches. This book chapter explores the challenges and ethical considerations inherent in AI implementation, addressing issues like data privacy, security, and human-AI collaboration. By scrutinizing real-world implementations and futuristic possibilities, this study paints a vivid picture of AI's profound influence on society. As Artificial Intelligence continues to evolve, its ethical integration into various sectors and the establishment of mutual trust between humans and machines emerge as paramount concerns. Amidst the rapid advancements, this research underscores the imperative of responsible AI development, ensuring a harmonious coexistence between artificial intelligence and humanity.
Top1. Introduction
Artificial intelligence methodologies have impacted not only individual human survival but also every project lifecycle. A decent quantity of consideration has been given to sectors such as engineering, product engineering, examination, surveillance, management, repairs and upkeep of infrastructure, and item evaluations and testing. Artificial intelligence can potentially be advantageous for power systems. Additionally, with the aid of artificial intelligence techniques, specialists' capacity to create, source, and oversee state-of-the-art products and technologies in the fields of health, insurance, power, petroleum, and natural resources gas, education, aviation, production, and automotive has significantly increased in recent years. Rationale computation techniques have greatly contributed to the creation of higher capacity, more fuel-efficient aircraft in the aviation industry, as well as reduced asset availability and repair costs and times, by promptly detecting, alerting, and fixing defects. The globe continues to be in the initial stages of an improved production revolt powered by AI, despite the broad usage of artificial intelligence in tackling various technical and production difficulties [3]. AI does, however, also provide significant difficulties and dangers for engineering, including moral, societal, legal, and security concerns. Engineers must therefore use AI properly and ethically by being aware of its advantages and drawbacks [2].
Figure 1.
Application of AI in phenomics
Figure 2. Components, types, and subfields of AI
Top2. Fundamentals Of Artificial Intelligence In Engineering
The topic “Fundamentals of Artificial Intelligence in Engineering” discusses the fundamental ideas and methods of Artificial Intelligence (AI) and how they relate to numerous engineering issues and fields. AI may be used to examine a lot of data and find patterns and insights.
Machines in this application of AI naturally learn from experience rather than being explicitly taught to carry out certain tasks. Artificial neural networks are the foundation of the subset of machine learning known as “Deep Learning” that is used for predictive analysis. Unsupervised learning, supervised learning, and reinforcement learning are only a few examples of the many machine learning algorithms [2]. The algorithm in unsupervised learning does not use classified information to make decisions on its own without any direction. A function is derived from the training data in supervised learning, which consists of a set of input objects and the intended output. Machines utilize reinforcement learning to determine the best possibility that needs to be considered and to take appropriate actions to increase the reward [4]. Computers that have been programmed to process natural languages do so when they interact with human language. With the help of machine learning, it is possible to extract meaning from human languages through natural language processing. NLP involves having a machine record audio of a person speaking. The communication from audio to text then takes place, and the text is subsequently processed to turn the data into audio. In order to communicate with people, the machine then uses audio. Applications of NLP can be found in IVR (Interactive Voice Response) systems used in contact centres, in language translation systems like Google Translate, and in word processors like Microsoft Word that assess the precision of grammar in text. But because of the rules that are required for communicating information using natural language, which are difficult for computers to understand, natural language processing is challenging due to the nature of human languages [3]. Therefore, NLP uses algorithms to recognize and abstract the rules of the natural languages so that the unstructured data from the human languages can be translated into a format that is recognized by the computer.