SHANGHAI (Reuters) - A popular Chinese video game, which involves battling other players with a variety of weapons, is showing off its red credentials after recent criticism by regulators of violent games in the world's biggest gaming market.

This sleek robotic dog may still be a mystery, but it clearly knows far more tricks than fetch, roll over and sit. Waltham-based Boston Dynamics has the tech world buzzing over a 24-second teaser video featuring its stylish new dog-like robot, SpotMini, which can climb stairs, handle objects and operate outdoors or in an office. The not-quite two-and-a-half foot tall, 60-pound robot is the smaller, younger brother of the company's Spot model, which was also unveiled with a YouTube video in February 2015. The SpotMini is battery powered and has a state-of-the-art 3D vision system. "SpotMini is a small four-legged robot that comfortably fits in an office or home," the company wrote on its website. The teaser video posted online has been viewed more than 2.5 million times.

Belong, a Bengaluru-based startup, is creating a "Google for people," as co-founder Rishabh Kaul describes it. The three-year-old recruitment firm is part of a new crop of companies using technology to simplify hiring processes, from sorting resumés to scheduling interviews. Belong, however, goes well beyond all that. "Earlier, a resume was just like a balance sheet--what you declare about yourself at a given point of time," explained Kaul. "Now, you have a rich stream of constant data."

Chatbots, which began as a fairly unknown niche concept in technology, are now turning into a necessity for mainstream businesses. Globally, the chatbot market has seen explosive growth since 2015, growing at 35 percent CAGR. Reports estimate $8 billion in annual savings from chatbots, with 80 percent of businesses considering implementing them. Big advances being made by the big five -- Facebook, Google, Microsoft, Apple, and Amazon -- today jostle for headlines with the innovations being led by nimble startups. However, one of the questions we ask ourselves regularly is where does India lie in this journey?

Talks on lethal autonomous weapons systems began at the United Nations November 13, amid calls for an international ban on independent "killer robots" that could revolutionize warfare -- discussions are scheduled to last all week, under the banner of the Convention on Certain Conventional Weapons. CCW Group of Governmental Experts on Lethal Autonomous Weapons Systems to meet next week https://t.co/cawvBkiuAR The summit in Geneva comes after over 100 major figures in technology and science co-signed a letter warning such weapons systems could lead to a "third revolution in warfare" in July. "Once developed, they will permit armed conflict to be fought at a scale greater than ever, and at timescales faster than humans can comprehend. The deadly consequence of this is machines -- not people -- will determine who lives and dies," the letter read.

Russia's Ministry of Defence has posted what it called "irrefutable proof" of the US aiding so-called Islamic State - but one of the images was actually taken from a video game. The ministry claimed the image showed an IS convoy leaving a Syrian town last week aided by US forces. Instead, it came from the smartphone game AC-130 Gunship Simulator: Special Ops Squadron. The ministry said an employee had mistakenly attached the photo. The Conflict Intelligence Team fact-checking group said the other four provided were also errors, taken from a June 2016 video which showed the Iraqi Air Force attacking IS in Iraq.

We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations that are similar to continuous optimization methods. Our theoretical contribution reveals that VAN is a second-order method that unifies existing methods in distinct fields of continuous optimization, variational inference, and evolution strategies. Our experimental results show that VAN performs well on a wide-variety of learning tasks. This work presents a general-purpose explorative-learning method that has the potential to improve learning in areas such as active learning and reinforcement learning.

We develop an approximate formula for evaluating a cross-validation estimator of predictive likelihood for multinomial logistic regression regularized by an $\ell_1$-norm. This allows us to avoid repeated optimizations required for literally conducting cross-validation; hence, the computational time can be significantly reduced. The formula is derived through a perturbative approach employing the largeness of the data size and the model dimensionality. Its usefulness is demonstrated on simulated data and the ISOLET dataset from the UCI machine learning repository.

This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.

The so called "rise of the machines" has started, and it looks like obtaining citizenship is the first step. A robot named "Sophia" has made history, as it became the first ever to be granted a full Saudi Arabian citizenship. Developed by AI specialist David Hanson of Hanson Robotics, Sophia's appointment was made public during the Future Investment Initiative held in the Saudi Arabian capital of Riyadh.