Nearly 70 years later, following a number of boom-and-bust cycles in the field, we now have AI models that more or less follow that recipe. While large language models that generate text have exploded in the last three years, a different type of AI, based on what are called diffusion models, is having an unprecedented impact on creative domains. By transforming random noise into coherent patterns, diffusion models can generate new images, videos, or speech, guided by text prompts or other input data. The best ones can create outputs indistinguishable from the work of people, as well as bizarre, surreal results that feel distinctly nonhuman.
Now these models are marching into a creative field that is arguably more vulnerable to disruption than any other: music. AI-generated creative works -- from orchestra performances to heavy metal -- are poised to suffuse our lives more thoroughly than any other product of AI has done yet. The songs are likely to blend into our streaming platforms, party and wedding playlists, soundtracks, and more, whether or not we notice who (or what) made them.
For years, diffusion models have stirred debate in the visual-art world about whether what they produce reflects true creation or mere replication. Now this debate has come for music, an art form that is deeply embedded in our experiences, memories, and social lives. Music models can now create songs capable of eliciting real emotional responses, presenting a stark example of how difficult it's becoming to define authorship and originality in the age of AI.
The courts are actively grappling with this murky territory. Major record labels are suing the top AI music generators, alleging that diffusion models do little more than replicate human art without compensation to artists. The model makers counter that their tools are made to assist in human creation.
In deciding who is right, we're forced to think hard about our own human creativity. Is creativity, whether in artificial neural networks or biological ones, merely the result of vast statistical learning and drawn connections, with a sprinkling of randomness? If so, then authorship is a slippery concept. If not -- if there is some distinctly human element to creativity -- what is it? What does it mean to be moved by something without a human creator? I had to wrestle with these questions the first time I heard an AI-generated song that was genuinely fantastic -- it was unsettling to know that someone merely wrote a prompt and clicked "Generate." That predicament is coming soon for you, too.
After the Dartmouth conference, its participants went off in different research directions to create the foundational technologies of AI. At the same time, cognitive scientists were following a 1950 call from J.P. Guilford, president of the American Psychological Association, to tackle the question of creativity in human beings. They came to a definition, first formalized in 1953 by the psychologist Morris Stein in the Journal of Psychology: Creative works are both novel, meaning they present something new, and useful, meaning they serve some purpose to someone. Some have called for "useful" to be replaced by "satisfying," and others have pushed for a third criterion: that creative things are also surprising.
Later, in the 1990s, the rise of functional magnetic resonance imaging made it possible to study more of the neural mechanisms underlying creativity in many fields, including music. Computational methods in the past few years have also made it easier to map out the role that memory and associative thinking play in creative decisions.
