The Roadmap to AI and Digital Twin Adoption

The Roadmap to AI and Digital Twin Adoption

Elakkiya Elango, Gnanasankaran Natarajan, Ahamed Lebbe Hanees, Shirley Chellathurai Pon Anna Bai
Copyright: © 2024 |Pages: 22
DOI: 10.4018/979-8-3693-1818-8.ch017
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Abstract

Organizations are quickly realizing the transformative possibilities of digital twins and artificial intelligence (AI) in this era of fast technical advancement. This chapter provides a brief synopsis of “The Roadmap to AI and Digital Twin Adoption,” a comprehensive resource that delves into the key elements and techniques necessary for the successful integration of AI and digital twins across a range of sectors. This roadmap explores the mutually beneficial relationship between artificial intelligence (AI) and digital twins, emphasizing how each may enhance overall performance, decision-making, and operational efficiency. It covers the fundamental concepts of artificial intelligence (AI), such as natural language processing, machine learning, and deep learning, and how important they are in relation to digital twins. The guide's emphasis extends to the practical use of AI and digital twins, offering guidance on data collection and management, model training, and algorithm choice.
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1. A Brief Overview About Artificial Intelligence

The diverse and revolutionary branch of computer science and technology known as artificial intelligence, or AI, focuses on building intelligent computers that are able to carry out tasks that normally require human intelligence. Artificial intelligence (AI) is defined by its ability to simulate cognitive processes like language comprehension, learning, problem-solving, perception, and decision-making. The ultimate aim of artificial intelligence is to create hardware and software which can carry out activities on their own, adjust to changing conditions, and get better over time—just like people do—by continuously learning and evolving. Two major categories can be used to group AI systems as shown in Figure 1.

Figure 1.

Major categories of AI systems

979-8-3693-1818-8.ch017.f01
  • a.

    Narrow or Weak AI: These AI systems are made for particular purposes or fields. They are excellent at carrying out clearly defined tasks and are unable to apply their knowledge to unrelated ones. Examples include picture recognition software, recommendation engines on e-commerce websites, and virtual personal assistants like Alexa and Siri.

  • b.

    General or Strong AI: The goal of this area of AI is to build machines that are as intelligent as humans. Like humans, generalized artificial intelligence (AI) systems would be able to comprehend, acquire, and utilize knowledge to a variety of jobs. Building such sophisticated AI is still primarily theoretical and is a huge hurdle.

1.1. Key Components and Techniques in the Field of AI Include

  • •

    Machine Learning (ML): A branch of artificial intelligence called “machine learning” is concerned with creating models and algorithms that let computers utilize data for learning from and predict the future. Reinforcement learning, supervised learning, and unsupervised learning are examples of machine learning techniques.

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    Neural Networks: Computational models that mimic the architecture and operations of the human brain are called neural networks. A subclass of neural networks called deep learning has shown impressive results on tasks like voice and picture recognition.

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    Natural Language Processing (NLP): NLP is the branch of AI that studies how language is used by computers to communicate with humans. It makes it possible for computers to comprehend, translate, and produce human language, opening up new applications like chatbots and language translation.

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    Computer Vision: The goal of computer vision is to provide computers the ability to analyze and comprehend visual data from the outside environment. This is essential for applications such as object identification, facial recognition, and driverless cars.

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    Robotics: AI-powered robots are designed to perform physical tasks in the real world. They can range from factory automation robots to advanced humanoid robots that can interact with humans.

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    Expert Systems: These artificial intelligence (AI) systems imitate human decision-making in particular fields. To deliver suggestions at the expert level, they leverage inference engines and knowledge bases.

A dynamic, virtual depiction of a real thing or system is called a digital twin. It's a strong idea that is becoming more and more significant across a range of sectors, from infrastructure management and urban planning to manufacturing and healthcare. With the use of digital twins, businesses may keep an eye on, examine, and replicate physical items or procedures in a virtual setting. I'll go into a great deal on digital twins here, which includes their uses, advantages, and applications.

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