SAN FRANCISCO - Residents here have been spotting a new type of Uber ride trolling the streets of this hilly city. Black Ford Fusion Hybrids with roofs loaded down with sensors and doors emblazoned with the ride-sharing company's logo are making the rounds in order to collect digital situational data to make the robotic cars drive smarter. Uber spokesperson Chelsea Kohler told USA TODAY that the vehicles, whose number remains unspecified, are "for internal use only, have a safety driver up front and are not part of our ride-sharing service in the city." A few weeks ago, Uber began not only testing similar self-driving vehicles in Pittsburgh, but also picking up passengers with the safety-driver equipped autonomous machines. It is the only U.S. city where riders can get picked up by an autonomous vehicle.

Autonomous cars are just beginning to edge their way onto the roads, but soon, they could replace human drivers altogether. And in some areas, it could happen just five years from now. Advocates for autonomous vehicles have proposed a shift toward'driver-free zones,' starting by banning human motorists from car-pool lanes on a 150-mile stretch of Interstate 5 between Seattle and Vancouver. Within a decade, researchers say self-driving cars could have full reign of these roads during peak hours. Autonomous cars are just beginning to edge their way onto the roads, but soon, they could replace human drivers altogether.

Apple is on a machine learning company buying spree. After buying Perceptio at the end of 2015 and Turi just a few months ago, Apple has now acquired an India/US-based machine learning team, Tuplejump. We'd been hearing rumors of another acquisition in this space by Apple for some time. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. That's okay -- as with most machine learning companies, they're not exactly a household name (unless you've got a data scientist in your house, I guess.)

Apple is continuing to add to its team of machine learning experts in Cupertino. TechCrunch reports that Apple has acquired Tuplejump, which describes itself as a service that "presents all your data in a familiar format" on their now-removed website. Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans. The report notes that Tuplejump is based in part in India as well as the United States and doesn't disclose the terms of the acquisition. We're hearing that Apple was particularly interested in "FiloDB", an opensource project that Tuplejump was building to efficiently apply machine learning concepts and analytics to massive amounts of complex data right as it streamed in.

Last summer, I was at a conference having lunch with Hal Daume III when we got to talking about how "Bayesian" can be a funny and ambiguous term. It seems like the definition should be straightforward: "following the work of English mathematician Rev. Thomas Bayes," perhaps, or even "uses Bayes' theorem." But many methods bearing the reverend's name or using his theorem aren't even considered "Bayesian" by his most religious followers. Why is it that Bayesian networks, for example, aren't considered… y'know… Bayesian? As I've read more outside the fields of machine learning and natural language processing -- from psychometrics and environmental biology to hackers who dabble in data science -- I've noticed three broad uses of the term "Bayesian."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

It's a good time to be an alien hunter. In August astronomers discovered Proxima b, a potentially life-supporting planet only 4.2 light-years away. Not long after, news surfaced of a possible signal coming from HD164595, a star in the Hercules constellation, first noticed by Russian scientists in 2015. Then there's KIC 8462852, better known as Tabby's Star, a body whose inexplicable light fluctuations have inspired a rash of speculation. Are we finally going to learn that we're not alone?

Apple's Siri analyzes thousands of movie showings and surfaces recommendations for the best times and theaters based on my location within seconds. Spotify knows my music preferences and curates personalized playlists for me. Facebook instantly recognizes my friends in photos and suggests tags with nearly 98 percent accuracy. All of this is made possible by artificial intelligence (AI)–complex and highly technical solutions such as natural language processing, deep learning and machine learning that when applied to everyday actions in our personal lives make us smarter and more productive. Today, Salesforce is delivering Salesforce Einstein–artificial intelligence for everyone.

It's a problem we can all relate to: sitting in traffic and waiting for a green light. While waiting, you may have even pondered how you would try to improve traffic efficiency--surely there's got to be some way for everyone to get to work on time. But ponder no longer, because a team of engineers from Tsinghua University in China has handed the problem over to machines. The team's recent study makes use of deep reinforcement learning algorithms to optimize traffic signaling, and its promising results suggest there may be a way to arrive on time after all. Let's be clear: traffic is a complex problem to solve, and traffic control engineers have long worked on improving efficiency.

This month Stanford launched a 100-year study of AI (AI100) with a report: Artificial Intelligence and Life in 2030. The 16 member study panel issuing the report sees increasingly useful applications of AI, with potentially profound positive impacts on our society and economy over the next decade. The study identifies eight domains where AI is already having or is projected to have the greatest impact: transportation, healthcare, education, low-resource communities, public safety and security, employment and workplace, home/service robots and entertainment. Let's start with public safety and a few emerging AI applications. Google's AI artificial company DeepMind announced an app that generates human-like speech.