Sally Jones, a former punk rocker from Kent, United Kingdom, who gained notoriety as "Mrs Terror" after joining the Islamic State group (also called ISIS), was reportedly killed in a United States drone strike along with her 12-year old son Jojo in Syria as she tried to escape Raqqa, the Sun reported. Though Whitehall sources confirmed reports that Jones was killed, according to the Guardian, the Pentagon was unable to confirm the news. Maj Adrian Rankine-Galloway, a Pentagon spokesman, told the Guardian, "I do not have any information that would substantiate that report but that could change and we are looking into this." Rukmini Callimachi, a correspondent for the New York Times, also said two senior U.S. officials denied that Jones was dead. Fifty-years-old Jones was born in Greenwich, southeast London, and later moved to Kent.

How do you try and become one of the biggest and most influential technology companies in the world? Just move the $15 billion you have stashed away into the R&D business, suggests Telecoms.com This might not be realistic for most companies, but apparently it is a goer for Alibaba. This is a company which has serious ambitions to emulate Huawei on the global stage. By 2036, the company wants to serve two billion consumers, create 100 million jobs around the world and serve ten million companies through its various platforms and services.

Quoting a British intelligence source who had been briefed by U.S. counterparts, The Sun reported that Jones and her son had been killed in June close to Syria's border with Iraq, as she was attempting to flee the IS stronghold of Raqqa.

Exactly a year ago, Cyngn (formerly Cyanogen) announced that it will discontinue its own flavor of Android, which powered phones from numerous international brands, and focus on a Modular OS that's easier for manufacturers to integrate. As it turns out, the company has now shifted gears and moved in a completely different direction: autonomous vehicle technology. Axios noted that the company's site and job listings point to its new interests in developing self-driving tech: it's hiring people to create and run autonomous system software, as well as mapping and perception systems across facilities in Singapore and Palo Alto. It also counts among its ranks former Mercedes-Benz talent. That signals the end of Cyanogen's Android efforts once and for all.

Elon Musk: Google's AI Camera Doesn't Even Pretend to be Innocent Musk, who thinks AI could trigger World War III and poses a far greater threat than North Korea, has now tweeted against "Clips" and its prowess. Elon Musk: Google's AI Camera Doesn't Even Pretend to be Innocent Google's artificial intelligence (AI)-based "Clips" camera has not impressed Tesla founder Elon Musk, a famed critic of AI. "Clips" does image recognition and AI processing on-device, deploying machine learning to automatically click best pictures for you. Musk, who thinks AI could trigger World War III and poses a far greater threat than North Korea, has now tweeted against "Clips" and its prowess. Musk took to Twitter with reference to a video of "Clips" posted by The Verge. "This doesn't even'seem' innocent," he tweeted.

BMW and Fiat Chrysler Automobiles are seeking one more automaker to join their autonomous driving partnership. "The road is open by the end of the year, and we have some good discussions with different other OEMs," Elmar Frickenstein, senior vice president of autonomous driving for BMW Group, told Automotive News. "The confidence level is high that we will do it by the end of the year." In order to keep BMW's iNEXT project on track by 2021, a new partner is needed this year, Frickenstein said. BMW formed the partnership in 2016 with technology companies Intel and Mobileye.

Chinese researchers have developed a method to measure the intelligence quotient (IQ) of AI applications and found that Google's technology scored nearly as well as a human six-year old. The researchers also measured applications developed by Baidu, Microsoft and Apple, all of which fared less well. The study was written up by Liu Feng of Beijing Jiaotong University; Yong Shi, the Director of the Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science; and Ying Liu of the School of Economic Management, UCAS. In the study, they attempt to quantify the capabilities of a number of well-known AI technologies. They present the problem thusly: "Quantitative evaluation of artificial intelligence currently in fact faces two important challenges: there is no unified model of an artificially intelligent system, and there is no unified model for comparing artificially intelligent systems with human beings." In the paper they published, the authors propose to solve that by developing a "standard intelligence model" that attempts to encompass AI systems and humans, and which categories them across a seven-level taxonomy of knowledge capability.

Alibaba Group announced today that it plans to invest more than $15 billion over the next three years into a global research and development initiative called Alibaba DAMO Academy. The Chinese tech giant said the program, which is currently recruiting 100 researchers, will help it reach its goal of serving two billion customers and creating 100 million jobs by 2036, while also "increasing technological collaboration worldwide." DAMO Academy (the initials stand for "discovery, adventure, momentum and outlook") will be led by Alibaba Group chief technology officer Jeff Zhang and start by opening labs in seven cities around the world: Beijing and Hangzhou in China; San Mateo and Bellevue in the U.S.; Moscow, Russia; Tel Aviv, Israel; and Singapore. Alibaba's researchers will collaborate closely with university programs such as U.C. Berkeley's RISE Lab, which is developing technologies that enable computers to make secure decisions based on real-time data. DAMO Academy's current advisory board also includes professors from Princeton, Harvard, MIT, the University of Washington, Columbia University, Beijing Institute of Technology, Peking University and Zhejiang University.

We connect high-dimensional subset selection and submodular maximization. Our results extend the work of Das and Kempe (2011) from the setting of linear regression to arbitrary objective functions. For greedy feature selection, this connection allows us to obtain strong multiplicative performance bounds on several methods without statistical modeling assumptions. We also derive recovery guarantees of this form under standard assumptions. Our work shows that greedy algorithms perform within a constant factor from the best possible subset-selection solution for a broad class of general objective functions. Our methods allow a direct control over the number of obtained features as opposed to regularization parameters that only implicitly control sparsity. Our proof technique uses the concept of weak submodularity initially defined by Das and Kempe. We draw a connection between convex analysis and submodular set function theory which may be of independent interest for other statistical learning applications that have combinatorial structure.

In this paper, a new approach for classification of target task using limited labeled target data as well as enormous unlabeled source data is proposed which is called self-taught learning. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is considered where the marginal distributions p(x) change over domains and the conditional distributions p(y|x) remain the same. In our approach, we propose a new objective function which simultaneously learns a common space T(.) where the conditional distributions over domains p(T(x)|y) remain the same and learns robust SVM classifiers for target task using both source and target data in the new representation. Hence, in the proposed objective function, the hidden label of the source data is also incorporated. We applied the proposed approach on Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to the available competing methods. Our method has a superior performance to the successful existing algorithms.