MyBucks, the German listed FinTech company that holds three brands - GetBucks, GetSure and GetBanked, says its groundbreaking partnership with NGO (non-governmental organization) Opportunity International continues to take strides forward in their vision of bringing financial inclusion to the unbanked and underbanked in emerging markets - most specifically in Africa. In a bold move that is contrary to the current trend worldwide where banks are acquiring FinTech companies to add value and expand services, the partnership marks the first time a FinTech company has acquired banks to bridge the gap between the virtual and traditional worlds of banking. This is ultimately to enable faster, more efficient and less expensive access to financial services for clients. The conclusion of the acquisition of four banks and two microfinance institutions from Opportunity International will add Ghana, Tanzania and Mozambique to MyBucks country portfolio and regulatory approval has already been granted in Kenya, Tanzania and Mozambique. This will increase its customer base to 1.5 million, and will also more than double its assets.

There'll be a new civilization, the sum of human intelligence and artificial intelligence, where artificial intelligence will be able to analyze a problem, identify the resources needed to deal with it, plan a strategy, adapt and improve itself to resolve the problem." It will do this not only for specific, planned objectives – like the IBM computer that beat chess champion Garry Kasparov in 1996 or the vision system that recognizes obstacles in today's self-driving cars – but "in a generalized way, with any problem". This will happen soon, "within the next two or three decades". A valuable time "for us to get ready for a world that will see huge changes, and make sure the effects are positive and compatible with people's lives and aspirations". David Orban is a lecturer at the Singularity University, a Silicon Valley think tank set up six years ago in the NASA Research Park in California, and founder and manager of Network Society Venture, a fund that invests in hi-tech start-ups. He spoke to "la Lettura" in the run-up to the Turin Book Fair, where he will be a guest speaker on Saturday 14 May. Con che velocità arriverà il futuro [How quickly will the future arrive] is the subject of the debate, and also the subtitle of Orban's book, Singolarità (Hoepli). Specifically, "technological singularity", the hypothetical moment in time when, with the support of increasingly powerful computers, the world will move from so-called "restricted" artificial intelligence (such as the vision system in a driverless car) to "strong" artificial intelligence (able to adapt itself to achieve generalized goals). "In strong artificial intelligence, space colonization systems will play a leading role," says Orban. "We have already conquered Mars, but we've never set foot on the planet, our robots have done that.

VIENTIANE – Southeast Asian nations are in unparalleled disarray over Beijing's saber-rattling in the South China Sea, analysts and insiders say, with the fractures set to deepen as staunch China ally Laos hosts top regional diplomats this weekend. U.S. Secretary of State John Kerry and Chinese Foreign Minister Wang Yi are among the delegates due to fly in from Sunday for two days of meetings in Vientiane, the capital of the communist nation. The South China Sea is set to cast a long shadow over the summit that is hosted by the 10-member Association of Southeast Asian Nations (ASEAN). Earlier this month a U.N.-backed tribunal found there was no legal basis for China's claims to most of the strategic and resource-rich seas -- a ruling rejected as "waste paper" by Beijing. ASEAN prides itself on consensus diplomacy but divisions have never been starker with Beijing blamed for driving a wedge between members. The Philippines brought the international arbitration case, while fellow ASEAN members Vietnam, Malaysia and Brunei also have competing claims to parts of the sea.

In the previous parts of this series, we looked at an overview of some popular tricks for feature engineering, and examined those tricks in greater detail. In this part, we continue our closer examination of these approaches with a deeper dive into the final techniques described in Part 1. The examples discussed in this article can be reproduced with the source code and datasets available here. As an analyst, you savor the scenario in which you have a lot of data. But, with a lot of data comes the added complexity of analyzing and making better sense of that data.

How many windows were in the house or apartment in which you lived when you were ten? Can you name all 50 states? What was served at your birthday party when you were 13? When you came back from your first trip abroad, how did you describe the experience to your friends? What was the most difficult interaction you ever had with a teacher and what did you learn from that experience?

Inside a secretive AI nonprofit backed by Elon Musk and other Silicon Valley figures, a handful of robots designed to help out in warehouses are gradually learning how to do useful household chores. OpenAI, which was created to do basic AI research, is reprogramming robots developed by Fetch Robotics, a company that supplies warehouse automation hardware. Researchers at OpenAI are equipping the robots with software that lets them train themselves through trial and error. The effort reflects a bet that innovations in software and machine learning, rather than breakthroughs in hardware, are the way to give robotics remarkable new capabilities. Fetch makes a range of robots for warehouses, including systems that follow workers around a building, carrying items dropped into a basket.

Testing systems that don't always return the same answers require new approaches. This is especially true when testing systems whose responses adapt to what they have learned from previous transactions. Software testing, in theory, is a fairly straightforward activity. For every input, there should be a defined and known output. We enter values, make selections, or navigate an application and compare the actual result with the expected one.

With the unemployment rate falling to 5.3 percent, the lowest in seven years, policy makers are heaving a sigh of relief. Indeed, with the technology boom in progress, there is a lot to be optimistic about. Manufacturing will be returning to U.S. shores with robots doing the job of Chinese workers; American carmakers will be mass-producing self-driving electric vehicles; technology companies will develop medical devices that greatly improve health and longevity; we will have unlimited clean energy and 3D print our daily needs. The cost of all of these things will plummet and make it possible to provide for the basic needs of every human being. I am talking about technology advances that are happening now, which will bear fruit in the 2020s. But policy makers will have a big new problem to deal with: the disappearance of human jobs. Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers.

Real-time systems differ in the way they perform analytics. Specifically, Real-time systems perform analytics on short time windows for Data Streams. Hence, the scope of Real Time analytics is a'window' which typically comprises of the last few time slots. Making Predictions on Real Time Data streams involves building an Offline model and applying it to a stream. Models incorporate one or more machine learning algorithms which are trained using the training Data.

The blockish and slightly dorky computer game Minecraft may turn out to be a great place for humans and AI to learn how to work together. An experimental new version of the game, released by Microsoft researchers this month, can be used to train an AI to perform all sorts of tasks, from crossing bridges to building complex objects. The new platform, called Project Malmo, makes it possible for a learning algorithm to control a Minecraft character that's normally operated by a human player. But it also provides ways for human players and AI agents to work together, and a chat window through which a person can talk with a nascent AI. "In the long run I want to work toward AI that can be taught by any user to help them achieve their goals," says Katja Hofmann, a researcher at Microsoft Cambridge in the U.K. who leads the project. Hofmann, who gave a demo of the software to AI researchers at an academic conference in New York last week, says that human-AI collaboration is a key goal for the project: "We've built in all the capabilities that a researcher would need in order to work toward collaborative AI." Malmo is geared toward testing reinforcement-learning algorithms, a way of training a computer to perform a task by providing simulated rewards.