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How Artificial Intelligence is reshaping art and music

Smart art:A handout image of an artwork created using the computer vision program DeepDream, which researchers at Google developed in 2015.NYT

Smart art:A handout image of an artwork created using the computer vision program DeepDream, which researchers at Google developed in 2015.NYT  

In the mid-1990s, Douglas Eck worked as a database programmer in Albuquerque, New Mexico, while moonlighting as a musician. After a day spent writing computer code inside a lab run by the Department of Energy, he would take the stage at a local juke joint, playing what he calls “punk-influenced bluegrass” — “Johnny Rotten crossed with Johnny Cash.” But what he really wanted to do was combine his days and nights, and build machines that could make their own songs. “My only goal in life was to mix AI and music,” Mr. Eck said.

It was a naive ambition. Enrolling as a graduate student at Indiana University, in Bloomington, not far from where he grew up, he pitched the idea to Douglas Hofstadter, the cognitive scientist who wrote the Pulitzer Prize-winning book on minds and machines, Gödel, Escher, Bach: An Eternal Golden Braid . Mr. Hofstadter turned him down, adamant that even the latest artificial intelligence techniques were much too primitive.

But during the next two decades, working on the fringe of academia, Mr. Eck kept chasing the idea, and eventually, the AI caught up with his ambition.

Last spring, a few years after taking a research job at Google, Mr. Eck pitched the same idea he pitched to Mr. Hofstadter all those years ago. The result is Project Magenta, a team of Google researchers who are teaching machines to create not only their own music but also to make so many other forms of art, including sketches, videos and jokes.

With its empire of smartphones, apps and internet services, Google is in the business of communication, and Mr. Eck sees Magenta as a natural extension of this work. “It’s about creating new ways for people to communicate,” he said during a recent interview inside the small two-story building here that serves as headquarters for Google AI research.

Growing effort

The project is part of a growing effort to generate art through a set of AI techniques that have only recently come of age. Called deep neural networks, these complex mathematical systems allow machines to learn specific behaviour by analysing vast amounts of data.

By looking for common patterns in millions of bicycle photos, for instance, a neural network can learn to recognise a bike. This is how Facebook identifies faces in online photos, how Android phones recognise commands spoken into phones, and how Microsoft Skype translates one language into another. But these complex systems can also create art. By analysing a set of songs, for instance, they can learn to build similar sounds.

As Mr. Eck says, these systems are at least approaching the point — still many, many years away — when a machine can instantly build a new Beatles song or perhaps trillions of new Beatles songs, each sounding a lot like the music the Beatles themselves recorded, but also a little different.

Tools for artists

But that end game is not what he is after. There are so many other paths to explore beyond mere mimicry. The ultimate idea is not to replace artists but to give them tools that allow them to create in entirely new ways.

In the 1990s, at that juke joint in New Mexico, Mr. Eck combined Johnny Rotten and Johnny Cash. Now, he is building a software that does much the same thing. Using neural networks, he and his team are cross-breeding sounds from very different instruments — say, a bassoon and a clavichord — creating instruments capable of producing sounds no one has ever heard.

Much as a neural network can learn to identify a cat by analysing hundreds of cat photos, it can learn the musical characteristics of a bassoon by analysing hundreds of notes. It creates a mathematical representation, or vector, that identifies a bassoon. So, Mr. Eck and his team have fed notes from hundreds of instruments into a neural network, building a vector for each one.

Now, simply by moving a button across a screen, they can combine these vectors to create new instruments. One may be 47% bassoon and 53% clavichord. Another might switch the percentages. And so on.

For centuries, orchestral conductors have layered sounds from instruments atop one other. But this is different. Rather than layering sounds, Mr. Eck and his team combine them to form something that did not exist before, creating new ways that artists can work.NYT

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