DERA ISMAIL KHAN, Pakistan – Pakistani Taliban militants chose a religious scholar as their new chief in place of Mullah Fazlullah, the insurgent leader who ordered the assassination of Nobel Peace Prize winner Malala Yousafzai and was killed earlier this month in a U.S. drone strike. Mohammad Khurasani, a spokesman for the Pakistani Taliban, said Saturday that the executive council of Tehrik-e-Taliban Pakistan appointed Mufti Noor Wali Mahsud as its new chief and Mufti Mazhim, aka Mufti Hafzullah, as his deputy. Khurasani conceded for the first time that Mullah Fazlullah was killed in the drone attack in Afghanistan's Kunar province. He did not say when and where the TTP executive council met to choose the new leader. A ruthless leader, Fazlullah ordered the beheading of dozens of opponents when his band of insurgents controlled Pakistan's picturesque Swat Valley from 2007 until a massive military operation routed them out in 2009. Fazlullah rose to prominence through his radio broadcasts in Swat demanding the imposition of Islamic law, earning him the nickname "Mullah Radio."

Asset managers are spending tens of millions on data science. The idea is to use lots of data and machine-learning tools in search of trading ideas and blind spots, the Journal reports. "Most of it is at an early stage, and I don't think it's matured yet," says Onur Erzan, a senior partner at consultant McKinsey & Co. "There will be new sources of data and it will help with investment decisions, but the real question is can an asset manager sustain an edge on those kinds of insights." This is how it starts. "Petter Stensland's foray into data began in late 2012 when, as a junk-bond analyst at AllianceBernstein Holding LP, he needed to analyze the prospects of the many oil-and-gas companies emerging in the nation's energy boom," the Journal says.

The AML/KYC regulatory landscape continues to impose greater costs to financial institutions as they begin to collect, refresh, and analyze more and more customer data. New Customer Due Diligence (CDD) requirements set forth by the Financial Crimes Enforcement Network (FinCen) include the Final Rule for beneficial ownership and control, and with effect from May 11, 2018, and the EU 5th AML Directive, which also mandates beneficial ownership collection for legal entity customers. Fines for AML and KYC deficiencies have topped billions of USD for both US and EU banks for lack of sufficient AML/KYC programs, failure to file Suspicious Activity Reports (SARs), CDD deficiencies, and other violations. The implications for financial institutions go well beyond additional documentation for customers to complete. The current AML/KYC regulatory framework calls for the implementation of a substantial framework to collect and analyze customer data on both a retroactive and ongoing basis.

In 2015, a worried father asked Rhema Vaithianathan a question that still weighs on her mind. A small crowd had gathered in a basement room in Pittsburgh, Pennsylvania, to hear her explain how software might tackle child abuse. Each day, the area's hotline receives dozens of calls from people who suspect that a child is in danger; some of these are then flagged by call-centre staff for investigation. But the system does not catch all cases of abuse. Vaithianathan and her colleagues had just won a half-million-dollar contract to build an algorithm to help. Vaithianathan, a health economist who co-directs the Centre for Social Data Analytics at the Auckland University of Technology in New Zealand, told the crowd how the algorithm might work. For example, a tool trained on reams of data -- including family backgrounds and criminal records -- could generate risk scores when calls come in. That could help call screeners to flag which families to investigate.

Cylance, a cybersecurity startup that leverages artificial intelligence and machine learning to combat online attacks, has raised $120 million in a series E round of funding led by Blackstone Tactical Opportunities, with participation from other unnamed investors. Founded in 2012 by Stuart McClure, an entrepreneur who sold an Internet security firm to McAfee for $86 million in 2004, Cylance is an endpoint protection platform designed to thwart malware, ransomware, and other forms of advanced threats using AI. Its suite of algorithm-based security protocols essentially inspect networks for weaknesses and shuts them down if any are detected. Cylance claims in excess of 4,000 customers, and said that it has revenues of $130 million for the 2018 fiscal year, representing a year-on-year growth of 90 percent. Prior to now, Cylance had raised around $177 million, including a $100 million tranche two years ago, and with another $120 million in the bank it said that it plans to double down on its global expansion efforts, with a particular focus on Europe, the Middle East, and Asia Pacific, and extend its product range.

Cylance, a cybersecurity startup that leverages artificial intelligence and machine learning to combat online attacks, has raised $120 million in a series E round of funding led by Blackstone Tactical Opportunities, with participation from other unnamed investors. Founded in 2012 by Stuart McClure, an entrepreneur who sold an Internet security firm to McAfee for $86 million in 2004, Cylance is an endpoint protection platform designed to thwart malware, ransomware, and other forms of advanced threats using AI. Its suite of algorithm-based security protocols essentially inspect networks for weaknesses and shuts them down if any are detected. Cylance claims in excess of 4,000 customers, and said that it has revenues of $130 million for the 2018 fiscal year, representing a year-on-year growth of 90 percent. Prior to now, Cylance had raised around $177 million, including a $100 million tranche two years ago, and with another $120 million in the bank it said that it plans to double down on its global expansion efforts, with a particular focus on Europe, the Middle East, and Asia Pacific, and extend its product range.

This translation by Jeffrey Ding, edited by Paul Triolo, covers some of the most interesting parts of the Standards Administration of China's 2018 White Paper on Artificial Intelligence Standardization, a joint effort by more than 30 academic and industry organizations overseen by the Chinese Electronics Standards Institute. Ding, Triolo, and Samm Sacks describe the importance of this white paper and other Chinese government efforts to influence global AI development and policy formulation in their companion piece, "Chinese Interests Take a Big Seat at the AI Governance Table." Historical experience demonstrates that new technologies can often improve productivity and promote societal progress. But at the same time, as artificial intelligence (AI) is still in the early phrase of development, the policies, laws, and standards for safety, ethics, and privacy in this area are worthy of attention. In the case of AI technology, issues of safety, ethics, and privacy have a direct impact on people's trust in AI technology in their interaction experience with AI tools.

How likely is it that Artificial Intelligence (AI) will, in the coming decades, be used to automate millions, tens of millions or even hundreds of millions of jobs? And will that have an impact on the diamond trade? The possible future impact of AI is being hotly debated by high-tech and other experts. Fundamentally, on one side you have billionaire Elon Musk, the founder of electric car maker Tesla, and on the other side many of the biggest stars of the tech firmament, such as Facebook's Mark Zuckerberg, among others, who believe that Musk is overestimating the power of AI and the speed of its likely development. The debate becomes even more interesting, especially to politicians and economists, when Musk adds in the argument that a universal basic income (UBI) – which really means government allowances to people who will not be able to find jobs due to AI replacing them.

Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples. We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes. We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT. We demonstrate that white-box adversarial examples are significantly stronger than their black-box counterparts in different attack scenarios, which show more serious vulnerabilities than previously known. In addition, after performing adversarial training, which takes only 3 times longer than regular training, we can improve the model's robustness significantly.

Employees at Amazon.com are calling on chief executive Jeffrey P. Bezos to end the sale of facial-recognition technology to law enforcement agencies and to discontinue partnerships with companies that work with U.S. Immigration and Customs Enforcement (ICE). In a letter, a group of Amazon workers said they are also troubled by a recent report from the American Civil Liberties Union revealing the company's sale and marketing of Rekognition, its facial-recognition technology, to police departments and government agencies. Workers at Amazon are protesting the recently halted Trump administration policy of separating migrant children from their parents at the U.S.-Mexico border. "We don't have to wait to find out how these technologies will be used. We already know that in the midst of historic militarization of police, renewed targeting of Black activists, and the growth of a federal deportation force currently engaged in human rights abuses -- this will be another powerful tool for the surveillance state, and ultimately serve to harm the most marginalized," the letter states.