This chapter has introduced the field of Artificial Intelligence, including definitions, types, taxonomy, examples, and applications. The different techniques, such as ML, DL, supervised learning, backpropagation, unsupervised learning, and reinforcement learning, are briefly explored. AI systems (and examples thereof) driven by LLMs are presented, while the key aspects of the AI semiconductor industry, which produces the chips that power AI systems, are reviewed. AI’s transformative potential comes with complexities and risks that must be managed carefully. AI offers powerful tools for solving global challenges, from predicting climate patterns and optimising renewable energy use to improving healthcare access and educational outcomes. It is essential to craft enabling legislation and regulations and ensure effective AI development and deployment governance. Mitigation and prevention of the adverse effects of AI require strategic thinking, foresight and planning. The 4IR is bigger than AI. Other transformative technologies include 3D Printing, Data Science, Cryptocurrency (Bitcoin), Blockchain, BESS, Quantum Computing, Nanotechnology, and the Internet of Things. However, AI is the DNA of the 4IR. Nevertheless, the deployment of AI to achieve the SDGs must be conducted within the context of embracing all these related and mutually reinforcing 4IR technologies.