We already know that artificial intelligence systems can work in law firms and beat the world champion at a game of Go. Now it turns out that AI can write some pretty good pop songs, too.

Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.

However, Oren Etzioni, professor of Computer Science at the University of Washington and CEO of the Allen Institute for Artificial Intelligence, argues that such headlines are in fact strongly influenced by the work of one man: professor Nick Bostrom of the Faculty of Philosophy at Oxford University, author of the bestselling treatise Superintelligence: Paths, Dangers, and Strategies. Essentially, Bostrom claims that if machine brains surpass human brains in general intelligence, the resultant new'superintelligence' could replace humans as the dominant lifeform on Earth. Furthermore, according to his findings, there's a 10-percent probability that human-level AI will be attained by 2022, a 50-percent probability that this feat will be achieved by 2040, and 90-percent probability that such an entity will be created by 2075. However, in his article published in the MIT Technology Review magazine Etzioni points out that Bostrom's main source of data is an aggregate of four different surveys of groups, including participants of the Philosophy and Theory of AI conference that was held in 2011 in Thessaloniki, and members of the Greek Association for Artificial Intelligence. Furthermore, it appears that Bostrom didn't provide the response rates or the phrasing of questions used during those surveys, and neither did he account for the reliance on data collected in Greece.

Self-driving cars are already rolling along in Pittsburgh, thanks to Uber (albeit on a small scale with humans onboard, ready to intervene), and a Wired writer gave it a shot. A bevy of companies are working to put autonomous cars on the streets, but a new announcement by Udacity at TechCrunch Disrupt SF could and should send shockwaves into the nascent industry. Udacity is best known as a titan of online education, specializing in "nanodegrees" for people interested in working in the tech sector. For 2400 and a 9-month commitment, Udacity can turn prospective students into viable experts on self-driving vehicle technology, capable enough to work with the likes of Google, Uber, and other firms working on this next step forward. Of course, new students will need a background in programming, but the course will offer the chance to master deep learning, sensor fusion, vehicle kinematics, and more subjects to enable your new Tesla drive on its own accord.

Maluuba launched its first Siri-like personal assistant at TC Disrupt San Francisco four years ago. Since then, the company has raised 11 million and has licensed its technology to a number of handset manufacturers that now use it to power their own personal-assistant features. As Maluuba's head of product Mo Musbah told me, the company spent the last two years doubling down on how it could utilize deep learning in the context of natural language processing. To do so, it recently opened an R&D office in Montreal, for example. As Musbah told me, "our vision there is to build one of the largest deep learning labs in the world," so the company is definitely not lacking in ambition.

"We are in a new era, one in which we are building systems that can't be grasped in their totality or held in the mind of a single person." In his book, Arbesman writes we're entering the entanglement age, a phrase coined by Danny Hillis, "in which we are building systems that can't be grasped in their totality or held in the mind of a single person." In the case of driverless cars, machine learning systems build their own algorithms to teach themselves -- and in the process become too complex to reverse engineer. My country has, because of huge digital divide a huge technological divide a huge internet of things analphabetism.

"We are in a new era, one in which we are building systems that can't be grasped in their totality or held in the mind of a single person." In his book, Arbesman writes we're entering the entanglement age, a phrase coined by Danny Hillis, "in which we are building systems that can't be grasped in their totality or held in the mind of a single person." In the case of driverless cars, machine learning systems build their own algorithms to teach themselves -- and in the process become too complex to reverse engineer. My country has, because of huge digital divide a huge technological divide a huge internet of things analphabetism.

K-nearest neighbors, or KNN, is a supervised learning algorithm for either classification or regression. It's super intuitive and has been applied to many types of problems. To make a personalized offer to one customer, you might employ KNN to find similar customers and base your offer on their purchase behaviors. KNN has also been applied to medical diagnosis and credit scoring. This is a post about the K-nearest neighbors algorithm and Python.

James Cameron's seminal summer blockbuster Terminator 2: Judgment Day turned 25 earlier this year. But in the big technological questions it raises, the film remains almost frighteningly relevant. This episode of Popcorn Politics, The A.V. Club's collaboration with Scrappers Film Group, explores what T2 had to say about the ethics, dangers, and possible future of artificial intelligence--and how those issues continue to inspire debate in the scientific community.

Automation and artificial intelligence are profoundly transforming trading and markets. Many scientists and futurists agree that the effects of artificial intelligence and automation on society are difficult to predict. While many predicted that the tip of the spear for such technology would play out in areas such as medicine or general computer system markets, the AI revolution is already underway in the financial markets. Many of the predicted challenges and solutions are occurring now -- in real time. Financial technology becoming possibly the first major component of society to be completely AI enabled is not surprising if you consider that financial markets are inherently big-data intensive, attract bright minds and high-quality capital, and often have a short investment-to-profit time horizon.