There are more mobile phones than humans on earth. That presents a unique opportunity for big data and, more importantly, the insights from the data to be applied to greater social good. At this week's PAPIs Connect--a predictive application programming interface (API) conference in Valencia, Spain--Nuria Oliver, the scientific director of Telefonica's R&D program, spoke about how to adapt this data via machine learning. Today, we touch on two of the situations she presented where big data and machine learning gave insight into how governments can better address crises, whether it's a natural disaster or a disease outbreak. In this piece we aren't talking about personalized data or even that which we're offering via our social media accounts.

"Our goal is to create applications that can see, hear and understand," says Jeff Dean, senior fellow at Google and one of leaders of the firm's machine learning strategy. Artificial intelligence is a clear priority for Google. Last week, its AlphaGo program defeated world Go champion Lee Sedol, while its DeepMind arm is considered to be at the forefront of deep learning research. And now Google is increasingly opening its technology up to other businesses as it attempts to grow its cloud operations. At its Cloud Next conference Google unveiled the Cloud ML service which helps machine learning engineers build "sophisticated, large" models based on its TensorFlow deep learning library, which was opened sourced last year.

In winter, snowfall can rapidly disrupt daily life and impact on Japan's economy. Snowplowing is a considerable annual expense, and methods for co-ordinating plowing activity are needed to ensure an efficient, cost-effective service. Clever computer models are needed to manage such complex activities, which involve many agents and interactions. Now, Satoshi Takahashi at the University of Electro-Communications, and Tokuro Matsuo at the Advanced Institute for Industrial Technology in Tokyo have devised a computational method that combines task allocation and scheduling of individual snow-plows to maximize efficiency. The researchers aimed to identify the best routes for multiple snow-plows to take without replicating route paths, meaning their computer model had to allocate and schedule tasks simultaneously.

Artificial intelligence is changing the world of work and education, with some groups saying AI could eliminate the need for standardized tests. AI trends raise questions, including what skills should be taught to students for future jobs and how best to prepare educators for the classroom.

The most common approach to address'Machine Learning' these days is to think of it as just another arduous subject, one which young computer scientists choose in their last two years of college. A huge percentage of people are not able to realise that this'subject' is actually one of the most important strategies that humans use throughout their lifetime. Let me start with a simple example. During childhood, every kid is curious about how the surroundings around him or her work – anyone with any experience of young children will know you have to be very careful about their activities! You can't just let it go near a burning flame, near sharp knives or let it eat just anything off the floor.

Today on In Case You Missed It: Autodesk built a 3D printing assembly line that gives different jobs to several print heads, allowing them to work collaboratively and quickly spit out a finished project. Clemson University used a Korean robot to graft up to 3,000 plants an hour. And a Kickstarter project for an augmented reality shirt is designed to teach kids and interested adults all about the internal organs. The Milky Way is looking stunning in newly released photos from the APEX telescope. As always, please share any great tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

Russian Defence Minister Sergei Shoigu announced Friday that Moscow will deploy a range of missile systems on the Kuril islands, claimed by Japan, as part of its military build-up in the far-eastern region, Agence France-Presse (AFP) reported. The islands have been a reason of tense relations between Moscow and Tokyo. "The planned rearmament of contingents and military bases on Kuril islands is under way. Already this year they will get Bal and Bastion coastal missile systems as well as new-generation Eleron-3 unmanned aerial vehicles," Shoigu said during a ministry meeting, AFP reported. Russia has been investing in military infrastructure on the Kuril islands, which Japan considers its territory, leading to strained relations between the two nations.

One dating app is on a mission to end "catfishing," the term for someone having a fictional persona online and trying to get into a relationship. Badoo is a name many Americans have never heard of, but the dating service is trying to grow its presence in the U.S. It's part of the reason why the brand acquired LuLu, a dating app originally for anonymous ranking men and gained popularity in U.S. colleges, and brought on its founder Alexandra Chong as president last month. Soon, she and Badoo will be opening an office in New York, adding to the locations in Moscow and London. "They're not known as the biggest," Chong told International Business Times while sitting on the rooftop of the Soho House earlier this week. "Tinder was the one that caught the millennials."

Here's what happened yesterday at the first meeting. Rachel started by going through the syllabus. So, what is data science? This is an ongoing discussion, but Michael Driscoll's answer is pretty good: Data science, as it's practiced, is a blend of Red-Bull-fueled hacking and espresso-inspired statistics. But data science is not merely hacking, because when hackers finish debugging their Bash one-liners and Pig scripts, few care about non-Euclidean distance metrics.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.