A very, very small quadcopter, one inch in diameter can carry a one- or two-gram shaped charge. You can order them from a drone manufacturer in China. You can program the code to say: "Here are thousands of photographs of the kinds of things I want to target." A one-gram shaped charge can punch a hole in nine millimeters of steel, so presumably you can also punch a hole in someone's head. You can fit about three million of those in a semi-tractor-trailer. You can drive up I-95 with three trucks and have 10 million weapons attacking New York City. They don't have to be very effective, only 5 or 10% of them have to find the target. There will be manufacturers producing millions of these weapons that people will be able to buy just like you can buy guns now, except millions of guns don't matter unless you have a million soldiers. You need only three guys to write the program and launch them. So you can just imagine that in many parts of the world humans will be hunted. They will be cowering underground in shelters and devising techniques so that they don't get detected. This is the ever-present cloud of lethal autonomous weapons. Mary Wareham laughs a lot. It usually sounds the same regardless of the circumstance -- like a mirthful giggle the blonde New Zealander can't suppress -- but it bubbles up at the most varied moments. Wareham laughs when things are funny, she laughs when things are awkward, she laughs when she disagrees with you. And she laughs when things are truly unpleasant, like when you're talking to her about how humanity might soon be annihilated by killer robots and the world is doing nothing to stop it. One afternoon this spring at the United Nations in Geneva, I sat behind Wareham in a large wood-paneled, beige-carpeted assembly room that hosted the Convention on Certain Conventional Weapons (CCW), a group of 121 countries that have signed the agreement to restrict weapons that "are considered to cause unnecessary or unjustifiable suffering to combatants or to affect civilians indiscriminately"-- in other words, weapons humanity deems too cruel to use in war. The UN moves at a glacial pace, but the CCW is even worse.

Data Natives Speaker Dr. Stefan Kühn is Lead Data Scientist at codecentric. Together with his team he is developing robust, fast and intelligent algorithms and is applying modern machine learning methods for analyzing large datasets. Before codecentric he was a researcher in the Scientific Computing Group at the Max Planck Institute for Mathematics in the Sciences Leipzig with a focus on Tensor Approximation and Higher-Order Singular Value Decomposition. He earned a Diploma in Applied Mathematics, with a focus on Mathematical Optimization, at the University of Hamburg. He went on to get PhD in Applied Mathematics with a focus on Numerics, Tensor Approximation and Higher-Order Singular Value Decomposition, at the Max Planck Insitute for Mathematics in the Sciences Leipzig in 2012. High-dimensional data is very hard to analyze and understand.

In 2002, when the Oakland A's replaced their MVP first baseman Jason Giambi with 32-year-old Scott Hatteberg, a washed-up catcher with a bum arm, longtime baseball scouts figured the unpredictable A's had finally gone completely around the bend. As journalist Michael Lewis recounted in his book, Moneyball, even "Hatteberg hadn't the slightest idea why the Oakland A's were so interested in him." As everybody who read Lewis' celebrated 2003 book knows, the A's signed Hatteberg with the encouragement of the team's bright young assistant general manager, Paul DePodesta. Schooled in economics at Harvard, DePodesta was developing a new way to interpret player statistics, finding value where nobody else was looking. With players like Hatteberg, the A's, led by general manager Billy Beane, took an operating budget that was a fraction of that of the Yankees or Red Sox, and assembled teams that didn't cost a fortune but won games like baseball royalty. We interviewed DePodesta for our 2013 issue "The Unlikely" because we were anxious to learn his methods for uncovering unlikely skills in baseball players through statistics and probability theory. He was forthcoming with his insights, if not all his secrets. At the time, DePodesta was vice president of scouting and player development for the New York Mets. Although he built his reputation in baseball, he told us he really wanted to be a football coach. Earlier this year he got his wish to be in football when he joined the Cleveland Browns as "Chief Strategy Officer," where he has put his skills to work to help the losing Browns reverse their fortunes.

How much can a 17-year-old boy really know about women? For starters, that thousands face heartbreaking and costly misdiagnosed breast cancer scares every year. Abu Qader, who's about to start his final year in the Chicago public school system's Lane Technical College Prep High School, was born in war-scarred Afghanistan but has spent most of his 17 years on Earth in the U.S. Qader and European business partner Vedad Mesanovic, who focuses on young and under-resourced scientists, created a company they call GliaLab (named after the cells that support and protect neurons). They are now courting venture capitalists for a targeted 600,000 to help finance the breast cancer-focused artificial intelligence that Qader first created for a 10th-grade class project. Qader believes his technology can help women (and men, too) take on potentially deadly breast tumors and non-cancerous growths by using the convenience of their own mobile phone or tablet to aid in diagnosis and classification, reduce human error and save the expense of false-positive readings.

Dr. Kanade and DENSO have worked together from 2002-2009 on a joint research of image recognition technology. In addition, he has been a lecturer of DENSO's high talent program organized by DENSO E&TS Training Center. DENSO expects to use artificial intelligence technology in more areas of its business. Currently, it uses machine learning in its sensing technologies and applies them to its sensing products. DENSO has developed technologies and products to help create a society free from road traffic accidents.

We derive fundamental sample complexity bounds for recovering sparse and structured signals for linear and nonlinear observation models including sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of the following Markovian property. We are given a set of $N$ variables $X_1,X_2,\ldots,X_N$, and there is an unknown subset of variables $S \subset \{1,\ldots,N\}$ that are relevant for predicting outcomes $Y$. More specifically, when $Y$ is conditioned on $\{X_n\}_{n\in S}$ it is conditionally independent of the other variables, $\{X_n\}_{n \not \in S}$. Our goal is to identify the set $S$ from samples of the variables $X$ and the associated outcomes $Y$. We characterize this problem as a version of the noisy channel coding problem. Using asymptotic information theoretic analyses, we establish mutual information formulas that provide sufficient and necessary conditions on the number of samples required to successfully recover the salient variables. These mutual information expressions unify conditions for both linear and nonlinear observations. We then compute sample complexity bounds for the aforementioned models, based on the mutual information expressions in order to demonstrate the applicability and flexibility of our results in general sparse signal processing models.

Robot" Season 2 concerns Tyrell's (Martin Wallstrom) whereabouts. While Mr. Robot, also known as Edward (Christian Slater), already told Elliot (Rami Malek) in the previous episode that he put an end to Tyrell's life, he might just be covering up for something else. Members of the cast remain mum about details of the show, particularly that aspect, but Carly Chaikin (Darlene) shared her thought about the arc during an interview with Den of Geek. "Well, we're all just convinced that he's going to kill all of us," Chaikin said. "Are we all going to die?

"AI ...surely will be a trend at least on the size of big data. It almost certainly will be a trend on the size of mobile. It might be a trend on the size of the internet. And maybe, just maybe, it'll be a trend on the size of software; that the software before machine intelligence and after will be two worlds that are very different from each other." It's undeniable that artificial intelligence (AI) is one of tech's hottest topics and a trend that is permeating every part of our world.

I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Now, let's come back to Flowcast. Can you share more information and shed more light on how Flowcast came about?

The winners of the Hugo Awards were announced at a gala ceremony in Kansas City, Mo., on Saturday, marking a good night for women and authors of color, and a very bad one for the "Puppies." Writers N.K. Jemisin and Nnedi Okorafor, both of whom are African American women, won the novel and novella awards, respectively. It was a defeat for the groups the Sad Puppies and the Rabid Puppies, who for two years have semi-successfully gamed the nominations for the Hugos -- which along with the Nebula Awards are generally considered the preeminent awards in science fiction and fantasy -- in an attempt to advance their anti-diversity agendas. Jemisin, who won for her novel "The Fifth Season," referenced the Puppies in her acceptance speech, io9 reports. "Only a small number of ideologues have attempted to game the Hugo Awards," Jemisin said.