Imagine for a moment, if you will, that you were a "dude" from the 1970s. And when I mean "The Dude" I really mean the "Dude". I mean the dude who is so much like the dude that it will make the protagonist of Big Lebowski shiver. And now think, if you had a friend called "Walter". And he says, "Dude, there is like this big company in like… 10 years. It'll sell computers and stuff and will revolutionize the tech industry."

Multicolored lights illuminate a rack of computer server units. "Legal confidentiality is a shield for citizens." These are the words of Shami Chakrabarti, the one-time director of the U.K.-based human rights group Liberty, speaking in 2018. Well, it seems that this shield has just been broken, because researchers at the University of Zurich in Switzerland have published a study in which they were able to identify the participants in confidential legal cases, even though such participants had been anonymized. By harnessing these technologies in tandem, the study's authors could mine over 120,000 public legal records and then use an algorithm to identify connections between them.

Finance Minister Asad Umar recently spoke in the National Assembly about amendments to the Finance Bill and hinted at using data and AI to identify tax evaders. "Algorithms are now very advanced. Global tax collection agencies don't rely solely on traditional methods for collection, they use modern technology," he said. "We want to use anonymised data to run algorithms which will help us identify tax evaders." But what algorithms was Umar referring to and how do they work?

Yesterday I attended Spotify's Big Data & Machine Learning panel at Galvanize in NYC. Going in I had a few select questions, mainly why my #1 song on my 2016 Year in Review was "Venus" by Lady Gaga, despite all the times I set it to Private Mode for my ritualistic guilty pleasure listening sessions. More importantly than that, I came to learn how big data and machine learning are used by the engineers over at Spotify. With over 100M registered users worldwide, Spotify has many unique data and AI challenges. There's 25 billion data points within Spotify's database, 100 million music preferences to catalog and cater to, over 30M songs, and 2 billion playlists across 60 countries.