How easy is it to deceive a deep neural network? Does gender of leaders affect team cohesion? Can music be classified by looking at entropy alone?
In this month’s reading round-up I look at businesses in Africa, category theory and Scala, fancy copy-pasting of code, neuromorphic microchips, machine learning, philosophy, supercomputers, topology, and of course data.
Below I have collected an initial batch of recent research articles and posts on various topics, such as deep learning, graphs, music, and Scala, that may be of interest to readers of Databaseline.
There are two crucial pieces of information for each song: who performed it (performing rights) and who owns the rights to it (copyrights). As of today, there is no central system that maintains this information. Is this really such a problem? Well, Spotify had to settle a $30m lawsuit a while ago because they had no idea whom to pay. There is also an infamous case in which 107% of the rights to a single song were sold. So, yes, it’s a pretty big deal. That’s why I want to take a look at blockchains as a possible solution.
If you need to read a JSON file from a resources directory and have these contents available as a basic
String or an
RDD and/or even
Dataset in Spark 2.1 (with Scala 2.11), you’ve come to the right place.
2016 was a good year for the music business: revenue from streaming and digital downloads exceeded that of physical sales for the first time in the history of the industry. On the one hand, companies such as Spotify and Apple have made digital music easily accessible and legal. On the other hand, each stream only pays artists fractions of a cent, which means that all but the most popular artists make very little money from streaming. Fair compensation for artists in a digital world is a recurring topic in media and industry. In this article I want to look at the ways professional musicians make money now.