Artificial Intelligence (AI) can be broadly defined as a field of computer science focused on creating systems capable of performing tasks that normally require human intelligence. This includes cognitive abilities such as learning, reasoning, decision-making, and adaptation. AI encompasses various techniques like machine learning, neural networks, and deep learning that enable machines to recognize patterns, interpret data, and make autonomous decisions.

AI is often characterized by its ability to mimic human cognitive functions through algorithms and computational models. It is not confined to single applications but spans many domains, including medicine, manufacturing, education, and others, where it helps improve efficiency, personalize experiences, and assist decision-making. For example, in nephrology, AI aids in clinical care and patient management by analyzing complex data patterns to predict disease progression. In education, AI enhances instruction by providing personalized learning paths and automating administrative tasks. In manufacturing, AI optimizes product design by enabling smart, adaptive systems (Niel, 2019), (Chen, 2020), (Aphirakmethawong, 2022).

On a technical level, AI involves creating models that learn from data (machine learning) and improve performance over time. Deep learning, a subset of AI, uses neural networks inspired by the human brain to process large datasets and identify complex features (Niel, 2019). The field continues to evolve rapidly with expanding applications and raising new challenges, including ethical considerations related to decision-making, privacy, and accountability.

In summary, AI refers to the development of computational systems that simulate intelligent behavior to perform tasks autonomously, leveraging data-driven learning approaches across multiple disciplines and industries.