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.

Demand for non-equity trading algorithms serving institutional asset managers and retail investors is expanding the prevalence of artificial intelligence in the world's financial markets. A recent report by Thomson Reuters estimates that algorithmic trading systems now handle 75 per cent of the volume of global trades worldwide and this figure is predicted, by those in the industry, to grow steadily. Firstly, while the institutional market has enjoyed a large variety of "algos" serving the equity markets to date, other areas such as futures are still witnessing huge product demand and innovation as a result. Secondly, regulations affecting the institutional investment market, such as the European Union Markets in Financial Instruments Directive II or MiFID II, are pushing for greater automation of trades in some asset classes which traditionally were not executed electronically. The fixed income market is a prime example and negotiations between industry groups are ongoing as to how practical a fully automated fixed income could really be, given the magnitude of the required shift from telephone to electronic trading.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

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. Researchers at Sony have been working on AI-generated music for years, and has previously used AI to create impressive jazz tracks. But this is the first time the Sony CSL Research Laboratory has released pop music composed by AI, and the results are impressive. The first song, "Daddy's Car," is a catchy, sunny tune reminiscent of The Beatles.