Over the last several years, artificial intelligence (AI) has shifted from being an esoteric branch of computer science to an everyday technology that most of us carry in a pocket or purse--AI is what drives Apple's Siri, Facebook's photo-tagging, Spotify playlists and Google's auto-complete, just for starters. But can we also expect that someday soon AI will report and write the important news of the day--and technology stories like this one? Well, guess what: It already has. First, a bit of background: Many of the most exciting AI advances are driven by research in cognitive computing and natural language generation (NLG) processing, which allow computers to analyze massive quantities of data and generate a plain English document that highlights the most important insights. Those advances are made stronger through deep learning, a field of AI that uses neural networks to teach computers to sift through massive amounts of data to find their own patterns.

We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Here we discuss clustering and density estimation on the grid. The grid can be seen as an 2-dimensional or 3-dimensional array.

Students at Lebanese American University (LAU) participate in a hardware design workshop that leverages the tools of the fourth Industrial Revolution. We are in the midst of the greatest industrial revolution in human history. The Fourth Industrial Revolution (4ID) is an economic transformation a thousand-times wider and deeper than anything that has come before it. "The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril," according to Professor Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF). The 4ID is characterized by the confluence of next generation technologies like: quantum computing, artificial intelligence and machine learning, autonomous transportation and robotics, the Internet of Things, additive manufacturing including 3D printing, biotechnology, and more generally; the merging of the digital and physical worlds.

AlphaGo, the computer system Google engineers trained to master the ancient game of Go, needed only one move to make it abundantly clear it has left humans in its dust. The move came Thursday, in the second game of AlphaGo's 4-1 landmark victory over South Korean Lee Sedol, one of the world's best Go players. About an hour into Thursday's match, AlphaGo placed one of its stones in a nontraditional spot on the board that surprised those watching. "I don't really know if it's a good or bad move," said Michael Redmond, a commentator on a live English broadcast. Redmond, one of the Western world's best Go players, could only crack a bemused smile.

Britain is on the brink of a robotics revolution. Advances in technology are unleashing a new age where computers handle many tasks previously carried out by humans. From automated manufacturing to software that does complex legal work, business is adapting to the robot economy. Some worry that this will lead to a jobs apocalypse as "thinking machines" replace workers. Others are optimistic that robots will free workers from mundane tasks and allow them to concentrate on higher-level creative and strategic work.

It could have taken months for the systems administrators at a large bank in Rome to figure out that one of their servers was talking to Facebook, a red flag given that networks in banks don't need to know how many "likes" they've received. And they might not have noticed the streams of data the server then sent to an array of unknown computers. This kind of threat--coming from inside the network, not from outside its firewall--is difficult to detect. According to IT researcher Gartner, it can take an average 229 days for a business to figure out it's been compromised this way. What tipped off the bank's IT department was a little black box containing software from Darktrace, a U.K. startup founded in 2013 by a group of former British spooks and Cambridge University Ph.D.s.

The radio spectrum is a mess: it's congested, expensive and there's no room for expansion. But DARPA has a plan to change that, by building a system where radio waves can work together using artificial intelligence, rather than fighting for space. DARPA launched its latest Grand Challenge last week, and it plans to encourage researchers around the world to develop "smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment... to maximize the flow of radio frequency". That sounds exciting, because making radio frequency flow more easily means -- theoretically, at least -- faster data rates, fewer dropped signals, and cheaper connections. How does it plan to do it?

"If you've created a conscious machine," says Caleb to Nathan toward the beginning of Ex Machina, when Caleb discovers Nathan is on the verge of creating an artificial intelligence indistinguishable from human intelligence, "it's not the history of man. Ex Machina, written and directed by Alex Garland, is an intriguing film about the wonders and dangers of artificial intelligence (AI). Garland's tale is stylishly told, beautifully photographed, and aided by a clever script that subverts standard cinematic clichés. It is also suffused with religious themes and theological motifs--unsurprisingly, because ever since Mary Shelley's Frankenstein, the prospect of human beings creating human-like beings of their own has almost invariably raised the issue of "playing God." In Ex Machina, Caleb is a computer coder brought to Nathan's secret research facility to apply the Turing Test to Nathan's AI--that is, to test whether a human interacting with the robot would be able to tell that the AI is ...

From burglar alarms that identify intruders, to robotic guards and even'gun' turrets, homeowners of the future will have far more imaginative options to protect their property than today. A report about the future of home security predicts a future that's a cross between the Minority Report and The Crystal Maze in which burglars would have to run a high-tech gauntlet to make off with goods. For example, burglar alarms could shout at intruders by name, while indelible sprays could mark them out in case of escape, and drones could give chase. The report, by future trends experts Futurizon, was commissioned by security company ADT. In a survey, the Florida-based firm found only four out of ten people feel safe being at home, while one in ten believe their home is at greater risk today than it was only five years ago.

Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results. These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). Such applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally. As machine learning algorithms are communication-intensive, the cost of centralizing the data is thought to be offset by the lower cost of intra-data center communication during training. In this work, we show that the current centralized practice can be far from optimal, and propose a system for doing geo-distributed training. Furthermore, we argue that the geo-distributed approach is structurally more amenable to dealing with regulatory constraints, as raw data never leaves the source data center. Our empirical evaluation on three real datasets confirms the general validity of our approach, and shows that GDML is not only possible but also advisable in many scenarios.