We think of art as the most human of endeavors, but in recent years we’ve learned that machines can understand creativity too. There are algorithms that evaluate songs and movies for record companies and movie studios. One music professor even created a program that wrote compositions which drew critical acclaim.
Paradoxically, developing algorithms that can create artistic works pushes the bounds of human capability. Unlike machines that, say, dig holes or build cars, algorithms that produce creative work need to understand things that even humans find difficult to articulate. That’s the idea behind #Google ’s #Magenta project, which is developing machine learning tools for art and music
Magenta is built on top of #TensorFlow, the library of machine learning tools that the company recently released as an open source technology, allowing anyone who wants to download the source code. To get a sense of why Google would open up its most advanced technology, which is at the heart of its most important products, I asked company executives about it.
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