In The Artist in the Machine, Arthur I. Miller provides a thorough examination of the world of AI-powered creativity. “Twenty-first century computers equipped with neural networks can learn the rules of baroque music from Bach’s scores and work out for themselves the probability of one note following another — which was precisely how Project Magenta set its computer to work to produce its little melody.”

Here are some of the insights he provides with regard to one of Google’s most exciting initiatives: “As head of Google’s Project Magenta, Douglas Eck [right] has found a way to combine his twio loves — AI amd music — in his work. An open and outspoken man, he first took an interest in computers at the age of thirteen. ‘I was from the floppy disc era,’ he says, ‘but never thought of myself as a computer person.’ Nevertheless, he became proficient at coding.”

To Eck, “the engineers at Magenta are more Les Paul and the artists more Jimi Hendrix. The essence of Magenta’s existence is to be a creative feedback loop, a push and pull, between us and AI, increasing our human creativity. At present, fully autonomous creative machines are not on Eck’s radar. He doesn’t want to step back and ‘watch a machine create art,’ he says. Rather his aim is to ‘increase our creativity as people [with] a cool piece of technology to work with.'”

As head of Project Magenta, creativity is always on Eck’s mind. “I ask him about volition, whether machines can create works of art on their own. His first response is, ‘I predicted this question on my bike ride in this morning. It’s the right question. This is the sort of thing I think about when I’m falling asleep or when I have a scotch’…Eck believes that imposing boundaries on computers when they compose music will help ensure they create works we can appreciate and enjoy.”

Here’s how Miller concludes Chapter 17 in The Artist in the Machine: “Another example Eck gives of the necessity of imposing limitations, or constraints, on creativity is the way he formed Project Magenta. ‘When I created Magenta, it was clear there had to be some  constraining factors. Otherwise you’re all over the place. For me the constraining factor was that I believed that deep learning, explicitly, has been a very productive and interesting revolution in machine learning, that would tie deep neural networks in with RNNs [recurrent neural networks] and reinforcement learning,’ he tells me.”

In other words, “he had a definite plan in mind, rather than simply taking account of everything in AI. Thus inspired, he worked with engineers from Google Brain to create Magenta and tied it in with TensorFlow, Google’s software library and machine learning framework. He also chose to utliize end-to-end learning, in which the computer essentially teaches itself by being fed data, with no further outside input. This is the basis of Google’s very successful translation process, which took twenty years to work out.  ‘Finally we got it right,’ says Eck. ‘That’s what we’re trying to do with music.'”

Rather than approaching the beginning of the end of that creative process, Miller suggests, Eck seems to be reaching the end of its beginning.”

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As indicated, he leads Magenta, a Google Brain project working to generate music, video, images, and text using deep learning and reinforcement learning. A main goal of Magenta is to better understand how AI can enable artists and musicians to express themselves in innovative new ways. To learn more about his life and work, please click here.

The Artist in the Machine was published by The MIT Press (November 2020).