Machine learning systems are pretty good at finding hidden correlations in data and using them to infer potentially compromising information about the people who generate that data: for example, researchers fed an ML system a bunch of Google Play reviews by reviewers whose locations were explicitly given in their Google Plus reviews; based on this, the model was able to predict the locations of other Google Play reviewers with about 44% accuracy. This has grave implications for privacy, as it can be used to de-anonymize or re-identify anonymized data-sets (for example, some of these statistical methods are used to make very accurate guesses about which pages are being transited through encrypted Tor connections). But machine learning systems are also plagued by adversarial examples: tiny preturbations in data that would not confuse humans but which sharply decrease the accuracy of machine learning systems (from fake roadsigns projected for an eyeblink to changes to a single pixel in an image. Wired's Andy Greenberg reports on two scholarly efforts to systematize a method for exploiting adversarial examples to make re-identification and de-anonymization attacks less effective. The first is Attrigard, which introduces small amounts of random noise into user activity such that "an attacker's accuracy is reduced back to that baseline."

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for instance, enable highly accurate facial recognition, see through the pixelation in photos, and even--as Facebook's Cambridge Analytica scandal showed--use public social media data to predict more sensitive traits like someone's political orientation. Those same machine-learning applications, however, also suffer from a strange sort of blind spot that humans don't--an inherent bug that can make an image classifier mistake a rifle for a helicopter, or make an autonomous vehicle blow through a stop sign. Those misclassifications, known as adversarial examples, have long been seen as a nagging weakness in machine-learning models. Just a few small tweaks to an image or a few additions of decoy data to a database can fool a system into coming to entirely wrong conclusions.

Five days from now Google will publish open-source tools that will focus its machine-learning engine on music and art. But one London startup, named Jukedeck, has been working on getting machines to automatically generate original music for years. You can even generate your own ditty, composed by artificial intelligence, right now on Jukedeck's website. Jukedeck lets anyone use its machine-learning engine to generate tunes hosted on its website. The engine then produces an original piece of music.