The text of this article is usable as a set of slides for a virtual workshop aimed at introducing the topic of artificial intelligence (AI) in a way that is accessible to a general reader and student, without having a popularizing purpose or nature. The first part introduces the necessary basic concepts, such as those relating to the leap from behaviorism to cognitive science, which allows us to conceive behavior as a result of information processing and not only as a series of reactions, enabling modeling and simulations. It also covers the difference between automatism and autonomy based on the ability to learn, the concept of systemic emergence that leads to the continuous acquisition of new properties that are not only possessed, and the concepts of procedure and algorithm, which are inadequate for modeling properties of complex systems such as the ability to learn. This can be simulated through the computational emergence, for example, of artificial neural networks (ANN) and cellular automata (CA). The second part introduces notes on the theoretical shift from computing as the calculation of results to physical systems with emergent collective computational abilities to acquire properties, such as ANNs and CA. We then focus on the technical aspects underlying AI (fuzzy sets and connectionism) and on variations and forms of AI such as machine learning, ensemble learning, deep learning, chatbots, and generative AI. We conclude by considering the intrinsic limitations of AI and its social consequences.