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Algorithms curb the discovery process. Amazon tries to recommend you books. Pandora examines your listening behavior to recommend music. Art.sy tries to introduce you to new art based on your preferences.

Algorithmic predictions feel a bit like Google, crowdsourced information that displays results for what the masses are also looking for in the aggregate.

The information, art, and music DJs that really know their stuff ignore algorithms altogether. They have trusted sources and spend the time to find new and emerging sources to pluck gems from. These curators master the art of showing people what they know people will like and what they think people will like.

I believe everyone should research at least one category of art and dig into it as much as they can. That means scouring the Internet for niche blogs, listening to obscure podcasts, seeing what the DJs are recommending, and following influencers on forums and on Twitter.

Discovery is an active process, not a passive one. Turn off mainstream radio and find something new or rediscover something old. The real gems lie in the nooks and crannies. Predict what’s next, not what’s now.

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Online, a Genome Project for the World of Art

Internet recommendation engines are now the way we discover things, whether it’s music, photography, or art.

At first we thought the best recommendation engines were our friends. Then it turned out those we follow on Twitter, Tumblr, and Pinterest were sharing far more interesting stuff.

On top of the social engine though are experts. These experts huddle in a room and break down a song or piece of art. Their analysis is “then fed into an algorithm” which seeds to users based on their interests. In Pandora terms, this is called Music Genome Project.

Art.sy is mimicking the same model, leveraging the brain power of curators to grade works of art that it can then recommend to users based on their interests.

This expert recommendation engine is now being used across industries to help people discover new stuff. The process is time consuming but the human element is sometimes greater than the smartest algorithm based on category generalizations. It pays to be more specific.