On the day U.S.-backed forces made a major breakthrough in the battle for Islamic State group's (ISIS) operation capital Raqqa, reports said the terror group's poster girl Sally Jones, also known as Mrs. Terror, is desperate to return to the U.K. This was revealed by the wife of a former ISIS militant who is now living in a refugee camp in Syria, in an interview to Sky News. The woman, known as Aisha, said of Jones: "She was crying and wants to get back to Britain. She told me she wish [sic] to go to her country. Aisha said Umma Hussain al Britani, the name adopted by Jones, was distraught and crying as her plea had been denied by ISIS leaders on the basis she considered a military wife. READ: Who Is Sally Jones? Jones is originally from from Chatham, Kent, became the leading female recruitment officer for ISIS after moving to Raqqa and marrying a now-dead jihadist in 2004. She is now the most wanted woman in the world after climbing to the top of the CIA assassination list, the Sun reported. Jones, 47, has remained at large since her husband, ISIS recruit Junaid Hussain, was killed in a U.S. drone strike in 2015. The couple are thought to have been behind at least a dozen murderous attacks both in the Middle East and abroad. On September 28, 2015, the United Nations sanctioned Jones as an agent operating on behalf of a terrorist organization, the Mirror reported. Jones is believed to have enticed scores of would-be European jihadis to join the self-declared caliphate through her influential recruitment network called the "Raqqa 12," the Sun reported. Jones has been attributed with the recruitment and training of young girls in Syria. The Express quoted a counter extremism website as saying Jones's activity online was in line with her role as leader of the secret "Anwar al-Awlaki" battalion's female wing. "In this role, Jones is responsible for training all European female recruits, or'muhajirat', in the use of weapons and tactics.

The cyber-security industry will grow from $102 billion in 2015 to $155 billion in 2020, with a compound annual growth rate of 52 percent, according to Frost & Sullivan. But in its report, "How Machine Learning Will Strengthen the Web Application Security Testing Market," the think tank also points to a different trend when it comes to web application attacks: Insecure web applications cause the most data breaches. Quoting Verizon's "Data Breach Investigation Report (DBIR) for 2016," Frost and Sullivan noted that "Although attacks on web applications account for only 8 percent of overall reported incidents (whether they were successful or not), attacks on web applications accounted for over 40 percent of incidents resulting in a data breach, and were the single-biggest source of data loss." Furthermore, the percentage of data breaches that leveraged web application attacks increased rapidly--from 7 percent in 2015 to 40 percent in 2016. In the face of this trend, Frost and Sullivan's report recommends machine learning technology for web application security testing.

This book is a discussion about machine learning. It is the best book for those with little or no knowledge about machine learning. The book begins by helping you understand what machine learning is. You will also learn the areas in which machine learning is applicable. In machine learning, training is very essential, as it is what helps the machine learning algorithms to learn and show an improvement next time from their experience.

Machine learning and predictive behavioral analysis are two practices having a decisive impact on a number of industries, as this article published by The Business Times points out. In the public safety sector, the analysis and correlation of massive amounts of data allows for the identification of behavioral trends, which in turn enables law enforcement agencies to predict and anticipate crises. Meanwhile, in the healthcare industry, genetic information and big data are used to provide customized treatment for patients. As for FinTech companies, they are harnessing disruptive technologies in order to develop innovative solutions that build a previously unattainable level of connection with customers. These industry-specific examples all have one thing in common: they are cases in which data is used to analyze and improve on past activity.

The world of Artificial Intelligence (AI) is growing at an unprecedented rate. This report provides a broad look at how enterprises leverage AI in meaningful ways. This report includes data from Gigaom's recent AI survey, insights from our recent AI Conference, and personal experience working with corporate enterprises on their AI journey.

At one point in our history, the most impressive example of artificial intelligence was a computer that was really, really good at chess. Today, various pieces of software can do everything from chat with us on Facebook Messenger to guiding the Mars rover Curiosity while its human engineers catch a nap. Now, a team of scientists from Carnegie Mellon University have developed an AI that can do something once thought impossible: read the human mind. The group's new software takes a novel approach to guessing what is going on inside a human brain, using data gathered from brain scans via fMRI to predict human thoughts by seeing how the pattern of brain activity that produces them, then detecting it in reverse. "One of the big advances of the human brain was the ability to combine individual concepts into complex thoughts," lead researcher Marcel Just explains.

Overview: Artificial Intelligence (AI) and Cognitive Computing are increasingly integrated in many areas including Internet search, entertainment, commerce applications, content optimization, and robotics. The long-term prospect for these technologies is that they will become embedded in many different other technologies and provide autonomous decision making on behalf of humans, both directly, and indirectly through many processes, products, and services. AI will anticipated to have an ever increasing role in ICT including both traditional telecommunications as well as many communications enabled applications and digital commerce. Fast growing AI technologies for consumer facing industries include chat bots and Virtual Personal Assistants (VPA) and smart advisors. These technologies leverage autonomous agents to enable an ambient user experience for applications, services, and enhanced commerce.

Continuous latent time series models are prevalent in Bayesian modeling; examples include the Kalman filter, dynamic collaborative filtering, or dynamic topic models. These models often benefit from structured, non mean field variational approximations that capture correlations between time steps. Black box variational inference with reparameterization gradients (BBVI) allows us to explore a rich new class of Bayesian non-conjugate latent time series models; however, a naive application of BBVI to such structured variational models would scale quadratically in the number of time steps. We describe a BBVI algorithm analogous to the forward-backward algorithm which instead scales linearly in time. It allows us to efficiently sample from the variational distribution and estimate the gradients of the ELBO. Finally, we show results on the recently proposed dynamic word embedding model, which was trained using our method.

Feature selection is an important problem in statistical machine learning, and is a common method for dimensionality reduction that encourages model interpretability. With large data sets becoming ever more prevalent, feature selection has seen widespread usage across a variety of real-world tasks in recent years, including text classification, gene selection from microarray data, and face recognition [3, 13, 17]. In this work, we consider the supervised variant of feature selection, which entails finding a subset of the input features that explains the output well. This practice can reduce the computational expense of downstream learning by removing features that are redundant or noisy, while simultaneously providing insight into the data through the features that remain. Feature selection algorithms can generally be divided into three main categories: filter methods, wrapper methods, and embedded methods [13].

We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns $\alpha$-approximate solutions for distributions over objectives, we compute a distribution over solutions that is $\alpha$-approximate in the worst case. We show that de-randomizing this solution is NP-hard in general, but can be done for a broad class of statistical learning tasks. We apply our results to robust neural network training and submodular optimization. We evaluate our approach experimentally on corrupted character classification, and robust influence maximization in networks.