Opinions expressed are solely those of the author and do not reflect the views of Rolling Stone editors or publishers. Artificial intelligence-powered tools can now provide insight into many of the questions that previously confounded stakeholders across the music industry. Analytics and predictive models allow labels to make smarter decisions concerning their investments -- decisions that are now informed by a more comprehensive understanding of the competitive environment and audiences' preferences. Clearly, AI technology is here to stay. Yet it's being applied in new and innovative ways beyond understanding listeners and their preferences: It has become the latest method for creating music.

Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading, robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of music. Nevertheless, artificial intelligence and music (AIM) has, for a long time, been a common subject in several conferences and workshops, including the International Computer Music Conference, the Computing Society Conference and the International Joint Conference on Artificial Intelligence. In fact, the first International Computer Music Conference was the ICMC 1974, Michigan State University, East Lansing, USA Current research includes the application of AI in music composition, performance, theory and digital sound processing. Several music software applications have been developed that use AI to produce music. A few examples are included below.

Reynolds has often compared his SPLITZ and SPIRLZ algorithms to a very traditional type of algorithm used in music, the canon. The process of canon is simply to combine a melody (the input) with one or more imitations of itself (possibly transposed, possibly slightly modified), each of which has been delayed by a certain time interval. The result is a contrapuntal output: the original melody in counterpoint with its delayed imitation(s). That is the explicit definition of the algorithm of the canon, and Reynolds maintains that his algorithms are similar in that they act upon the input in a predictable, well-defined way to produce a predictable output. However, implicit in the canon of tonal music is a whole set of explicit classical rules of harmony and voice-leading to which the output must conform.