KABUL – Afghan Taliban leader Mullah Mansour, who according to U.S. officials was probably killed in a drone strike, took over as head of the insurgent movement last July following the revelation that the group's founder, Mullah Omar, had been dead for two years. He was initially thought to favor peace talks with the government, but after becoming leader he repeatedly refused to come to the negotiating table. For some Mansour was the obvious choice to succeed Mullah Omar, the one-eyed warrior-cleric who led the Taliban from its rise in the chaos of the Afghan civil war of the 1990s. Born in the same southern province, Kandahar, some time in the early 1960s, Mansour was part of the movement from the start and effectively in charge since 2013, according to Taliban sources. Mansour spent part of his life in Pakistan, like millions of Afghans who fled the Soviet occupation.

A senior commander with the Afghan Taliban says the militant group's leader Mullah Akhtar Mansour has been killed in a U.S. drone strike. Mullah Abdul Rauf told The Associated Press Sunday that Mansour died in the strike late Friday night. He says the strike took place "in the Afghanistan-Pakistan border area." The office of Afghan President Ashraf Ghani confirmed the strike but could not confirm Mansour's death. Chief Executive Abdullah Abdullah, however, says that Mansour is "more than likely" dead.

This week on TechRepublic's Business Technology Weekly podcast, hosts Dan Patterson and Bill Detwiler discuss how swarm AI won the Kentucky derby, and the real world, practical impact of artificial intelligence. Headlines: Swarm AI predicts the 2016 Kentucky Derby Hope Reese Big news in the AI world this week! HOME NEWS Baidu to Shift to AI After Government Probe Baidu is planning to switch toward developing artificial intelligence after a government probe that affected its core business. BERLIN, GERMANY - SEPTEMBER 04: Visitors look at smartphones at the Lenovo stand at the 2015 IFA consumer electronics and appliances trade fair on September 4, 2015 in Berlin, Germany. The PC maker posted its first loss in six years in 2015.

Data Science Middle East (#DSME) in partnership with PAPIs are excited to announce this 2-day hands-on Machine Learning Workshop. Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We'll go all the way from data preparation to the integration of predictive models in applications and their deployment in production. "Just like development where you don't need to know a thing about computability or big-O notation to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra."

It's hard to make a right turn down San Francisco's crowded streets these days without running into a story about bots, conversational commerce, or conversational commerce bots. Bots have reached kale levels of hype. But amidst the noise, there's something real going on here -- we just have to decipher it. You may have heard of conversational commerce -- it's a catchall term for a future of technology driven by messaging (and voice) interactions that transcend current communications modalities. It's a convenient moniker but also confusing because there isn't one trend to follow.

One of the major issues in stochastic gradient descent (SGD) methods is how to choose an appropriate step size while running the algorithm. Since the traditional line search technique does not apply for stochastic optimization algorithms, the common practice in SGD is either to use a diminishing step size, or to tune a fixed step size by hand, which can be time consuming in practice. In this paper, we propose to use the Barzilai-Borwein (BB) method to automatically compute step sizes for SGD and its variant: stochastic variance reduced gradient (SVRG) method, which leads to two algorithms: SGD-BB and SVRG-BB. We prove that SVRG-BB converges linearly for strongly convex objective functions. As a by-product, we prove the linear convergence result of SVRG with Option I proposed in [10], whose convergence result is missing in the literature. Numerical experiments on standard data sets show that the performance of SGD-BB and SVRG-BB is comparable to and sometimes even better than SGD and SVRG with best-tuned step sizes, and is superior to some advanced SGD variants.

To design an intelligent and human-centered control system [1] that adaptively adjusts relevant parameters in time to meet the human driver's needs and to provide a basic control law for the advanced vehicle dynamics control system [2][3] or driver assistance system [4][5], driver behaviors, driving styles or characteristics should be recognized and predicted. For example, to improve vehicle's fuel economy and reduce the emission, we can design different control strategies for driving styles. To achieve these goals, recognition and prediction of driving styles and characteristics precisely is the primary work. Drivers and their factors have been discussed from the viewpoint of application in vehicle dynamics [6][7], physical attributes of human drivers, and modeling driver [8][9]. For the recognition and prediction of driving characteristics or driver types, including physical characteristics/states (e.g., fatigue, drunk, and drowsiness), psychical characteristics (e.g., nervous, relaxed) and driving styles (e.g., aggressive, moderate), a lot of investigations have been conducted in recent years. In general, the basic idea to identify and predict driving behaviors or styles is based on driver model, called indirect or model-based method. The model-based method, firstly, requires to establish a driver model that can describe driver's

Anomaly detection is a method used to detect outliers in a dataset and take some action. Example use cases can be detection of fraud in financial transactions, monitoring machines in a large server network, or finding faulty products in manufacturing. This blog post explains the fundamentals of this Machine Learning algorithm and applies the logic on the Spark framework, in order to allow for large scale data processing. Indeed, this was a real SMS I received from my bank after trying to deposit some money to an online payment system I had never used before. If Spark is new to you, it is an top-level Apache project for large-scale data processing.

The next time you ask the room service at your hotel for a toothbrush or extra towels, you might find a surprise at your door. Its name is Relay and is the first room service robot, outside Japan. Thanks to its 3D cameras and the built-in Wi-Fi, the Savioke's Relay accommodation robot can help with […]

Japan's Sony said it plans to build up its artificial intelligence (AI) business and eventually turn it into a major revenue source, beginning with an investment in a U.S. startup. The electronics maker has invested an undisclosed sum in California-based Cogitai. The year-old firm, founded by three researchers, focuses on technology that allows machines to learn continually and autonomously from interaction in the real world. The move comes a time when major technology companies such as Facebook, Apple, and Alphabet's Google are spending aggressively on AI ventures. "From an objective perspective, we are lagging behind," Hiroaki Kitano, chief executive of Sony Computer Science Laboratories, said in an interview.