AI Learning and Human Learning Builds on What Came Before

In 2017, an AI system called Amper composed and produced original music for an album called I AM AI. The chord structures, instrumentation, and percussion were developed by Amper, which used initial parameters like genre, mood, and length to generate a full-length song in just a few minutes. Taryn Southern, a human artist, collaborated with Amper to create the album—and the result included a moody, soulful ballad called “Break Free” that counted more than 1.6 million YouTube views and was a hit on traditional radio. Before Amper could create that song, it had to first learn the qualitative elements of a big ballad, along with quantitative data, like how to calculate the value of notes and beats and how to recognize thousands of patterns in music (e.g., chord progressions, harmonic sequences, and rhythmic accents).

Creativity, the kind demonstrated by Amper, is the pinnacle of Bloom’s Taxonomy, but was it merely a learned mechanical process? Was it an example of humanistic creativity? Or creativity of an entirely different kind? Did Amper think about music, the same way that a human composer might? It could be argued that Amper’s “brain”—a neural network using algorithms and data inside a container —is maybe not that different from Beethoven’s brain, made up of organic neurons using data and recognizing patterns inside the container that is his head. Was Amper’s creative process truly different than Beethoven’s when he composed his Symphony no. 5, the one which famously begins dada-da-DUM, da-da-da-DUM before switching from a major to a minor key? Beethoven didn’t invent the entire symphony—it wasn’t completely original. Those first four notes are followed by a harmonic sequence, parts of scales, arpeggios, and other common raw ingredients that make up any composition. Listen closely to the scherzo, before the finale, and you’ll hear obvious patterns borrowed from Mozart’s 40th Symphony, written 20 years earlier, in 1788. Mozart was influenced by his rival Antonio Salieri and friend Franz Joseph Hayden, who were themselves influenced by the work of earlier composers like Johann Sebastian Bach, Antonio Vivaldi, and Henry Purcell, who were writing music from the mid-17th to the mid-18th centuries. You can hear threads of even earlier composers from the 1400s to the 1600s, like Jacques Arcadelt, Jean Mouton, and Johannes Ockeghem, in their music. They were influenced by the earliest medieval composers—and we could continue the pattern of influence all the way back to the very first written composition, called the “Seikilos epitaph,” which was engraved on a marble column to mark a Turkish gravesite in the first century. And we could keep going even further back in time, to when the first primitive flutes made out of bone and ivory were likely carved 43,000 years ago. Even before then, researchers believe that our earliest ancestors probably sang before they spoke.

Our human wiring is the result of millions of years of evolution. The wiring of modern AI is similarly based on a long evolutionary trail extending back to ancient mathematicians, philosophers, and scientists. While it may seem as though humanity and machinery have been traveling along disparate paths, our evolution has always been intertwined. Homo sapiens learned from their environments, passed down traits to future generations, diversified, and replicated because of the invention of advanced technologies, like agriculture, hunting tools, and penicillin. It took 11,000 years for the world’s 6 million inhabitants during the Neolithic period to propagate into a population of 7 billion today.2 The ecosystem inhabited by AI systems—the inputs for learning, data, algorithms, processors, machines, and neural networks—is improving and iterating at exponential rates. It will take only decades for AI systems to propagate and fuse into every facet of daily life.