→Machine Learning (ML)
paradigm called a neural network, which consists of parameter-processing units (“neurons”), input/ output, and control modules. Such system can be programmed to refine the procedure for solving a particular problem by dynamically modifying parameters based on the referential data.

→Deep Learning (DL)
a subset of ML methods in which the optimization of network performance and accuracy relies on complex statistical rules applied to multiple layers of neurons

Metaphorical use of the word algorithm which ignores these contextual basics, although colloquially economical, actually obscures both the intricacy and unpredictability of AI development and application.

→Generative Art (GA)
GA includes heterogeneous approaches (not necessarily involving AI) based upon consciously and intentionally interfacing the predefined systems with different factors of unpredictability in preparing, producing or presenting the artwork

→Algorithm
Within a conceptual framework of computer science and technology, algorithm design involves defining a problem or task, finding its solution, creating and testing algorithms for this solution, translating the best algorithm candidate into software and (often custom-built) hardware systems, running, testing and debugging.