DERA ISMAIL KHAN, Pakistan (Reuters) - The leader of Pakistani militant group Jamaat-ul-Ahrar, who planned some of the deadliest suicide bombings in Pakistan in the last year, died on Thursday after being wounded in a U.S. drone strike in Afghanistan, a spokesman said.

With the revolution of machine learning, a new trend has come to town and it is called face recognition. When Apple announced its FaceID feature, everyone started talking and thinking about implementing face recognition everywhere – business, mobile apps, medicine, retail, and whatnot. But how can you be sure this technology is what you need without its thorough understanding? We'll tell you today what face recognition is, how it works, and what are the different use cases for this technology. Let's just say, after reading this article, you'll become a real Jedi of face recognition. Face (or facial) recognition is a biometric identification system that is developed for identifying or verifying a person by comparing and analyzing patterns based on the person's stored records of facial contours in real time.

The UAE on Thursday appointed Omar Bin Sultan as the country's first Minister of State for Artificial Intelligence as part of a cabinet reshuffle. Aged just 27, Sultan's appointment is part of the UAE's ambition to be at the forefront of the global technological revolution which sees it planning to be build homes on the planet Mars by 2117. The position was announced in a tweet by Sheikh Mohammed bin Rashid Al Maktoum, UAE Prime Minister and Vice President and ruler of Dubai who said: "The new Government is a Government for the new Emirati percentage. The move comes just days after Sheikh Mohammed announced the UAE Strategy for Artificial Intelligence (AI), a major part of the UAE Centennial 2070 objectives. The initiative aims to improve government performance and create an innovative and highly-productive environment by means of investing in AI. Other new positions created in the reshuffle include a Minister for Advanced Sciences and another for Food Security, according to a series of tweets, written in Arabic. Sheikh Mohammed said: "The new Government is a Government for the new Emirati percentage.

Are you ready for the WWE of giant robot fighting? Don't worry, we weren't either. Anyway, it's Thursday, which means it's time to make a plan for cleaning up dead satellites. Chainsaws aren't against the rules, FYI.USA vs. Japan giant robot battle was a slow, brilliant mess On Tuesday, Team USA's mechs scrapped it out with Japan's Kuratas in an abandoned steel mill for the world to watch. There could only be one victor, and it proved to be -- well, click here if you'd like to watch without a spoiler.

Google's artificial intelligence group, DeepMind, has unveiled the latest incarnation of its Go-playing program, AlphaGo – an AI so powerful that it derived thousands of years of human knowledge of the game before inventing better moves of its own, all in the space of three days. Named AlphaGo Zero, the AI program has been hailed as a major advance because it mastered the ancient Chinese board game from scratch, and with no human help beyond being told the rules. In games against the 2015 version, which famously beat Lee Sedol, the South Korean grandmaster, AlphaGo Zero won 100 to 0. The feat marks a milestone on the road to general-purpose AIs that can do more than thrash humans at board games. Because AlphaGo Zero learns on its own from a blank slate, its talents can now be turned to a host of real-world problems. At DeepMind, which is based in London, AlphaGo Zero is working out how proteins fold, a massive scientific challenge that could give drug discovery a sorely needed shot in the arm.

The emergence of big data has been transformational in many areas in science and engineering – biology, health sciences, material science, physics, and so on. At the heart of this transformation is statistical machine learning, subfield of computer science that aims at studying and developing algorithms that can analyze large volumes of data. The goal of this workshop is to bring together researchers, both from USA and Armenia, who work on machine learning (ML) and other scientific disciplines that are poised to benefit from the recent advances in ML. During the Soviet times, Armenia was one of the main hubs of cybernetics research in ex-Soviet Union, where centers such as the Mergelyan Institute (one of the three major producers of computer equipment in former USSR), and the Computing Center of the National Armenian Academy of Science (currently the Institute for Informatics and Automation Problems) conducted cutting edge research on topics ranging from robotics to algorithmic game theory to automated machine translation. Currently, Armenia has a small but vibrant research community in machine learning and data science, some members of which have participated in past and present DoD-sponsored research projects in collaboration with US colleagues.

The Global Service Robotics Market is a strategy report from Berg Insight analysing the latest developments on this market covering floor cleaning robots, robot lawn mowers, milking robots, humanoid robots, telepresence robots, powered human exoskeletons, surgical robots, AGVs, AMRs and UAVs. This strategic research report from Berg Insight provides you with 240 pages of unique business intelligence including 5-year industry forecasts and expert commentary on which to base your business decisions. The future is here: Service robotics will change our lives Robots are now being increasingly adopted for service applications, both by consumers and professionals. The service robot market comprises many different types of robots, most of which can be used for applications in multiple industries. At a consumer level service robots are commonly used for tedious and repetitive tasks such as domestic chores, or for leisure and entertainment purposes.

Watch: This may be the first footage of a blue whale "heat run." Blue whales are the largest animals on Earth, but we know surprisingly little about their complex social interactions--and they've rarely been recorded on camera. But new footage filmed off the coast of Sri Lanka by pro whale photographer Patrick Dykstra, in conjunction with blue whale scientist Howard Martenstyn, may be a first. Their video shows what they believe is the first known clip of a blue whale "heat run." Heat runs have been well documented in humpback whales, but no known footage exists of the behavior in blue whales (or at least that Dykstra or National Geographic could find).

Bayesian image processing [1] based on a probabilistic graphical model has a long and rich history [2]. In Bayesian image processing, one constructs a posterior distribution and then infers restored images based on the posterior distribution. The posterior distribution is derived from a prior distribution that captures the statistical properties of the images. One of the major challenges of Bayesian image processing is the construction of an effective prior for the images. For this purpose, a Gaussian Markov random field (GMRF) model (or Gaussian graphical model) is a possible choice.

Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three sub-tasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations, that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped Switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of WER over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency and the sequence level linguistic knowledge integration.