British workers are being shut out of decisions over the rising use of robots in the UK economy, according to a report. According to the commission on workers and technology, run by the Fabian Society and the Community trade union, almost six in 10 employees across Britain in a poll said their employers did not give them a say on the use of new technologies. Risking a future where workers' jobs get worse and people's voices go unheard over changes in the workplace, the findings come as a separate report finds the use of robots in poorer regions triggers the loss of almost twice as many jobs as in wealthier ones. In a study by the consultancy firm Oxford Economics, the rapidly growing use of robots is expected to have a profound impact on jobs across the world, resulting in up to 20m manufacturing job losses by 2030. Around 1.7m manufacturing jobs have already been lost to robots since 2000, according to the study, including as many as 400,000 in Europe, 260,000 in the US and 550,000 in China.

BANGKOK - An order for mobile phone users in Thailand's restive south to submit a photo of themselves for facial recognition purposes is causing uproar from opponents who see it as further curtailing the rights of the Muslim-majority population. But an army spokesman on Wednesday defended the move, saying the facial identification scheme is needed to root out insurgents deploying mobile phone-detonated home-made bombs. Thailand's three southernmost states -- Yala, Pattani and Narathiwat -- have since 2004 been rife with conflict between Malay-Muslim rebels and the Buddhist-majority Thai state, which annexed the region around a century ago. The tit-for-tat violence has claimed around 7,000 lives, mostly civilians of both faiths, and security forces have detained individuals suspected of being separatist rebels without warrants in the past. Now telecoms companies are requiring all users of the region's 1.5 million mobile numbers to submit a photo of themselves for facial recognition purposes following orders from the army -- a move that is drawing anger from rights groups as the deadline to register photos nears.

Sophisticated threat actors can often maintain a long-term presence in their target environments for months at a time, without being detected. They move slowly and with caution, to evade traditional security controls and are often targeted to specific individuals and organizations. AI will also be able to learn the dominant communication channels and the best ports and protocols to use to move around a system, discretely blending in with routine activity. This ability to disguise itself amid the noise will mean that it is able to expertly spread within a digital environment, and stealthily compromise more devices than ever before. AI malware will also be able to analyse vast volumes of data at machine speed, rapidly identifying which data sets are valuable and which are not. This will save the (human) attacker a great deal of time and effort.

The Ethics Guidelines for Trustworthy AI provide an assessment list that operationalises the key requirements and offers guidance to implement them in practice. This assessment list will undergo a piloting process: all stakeholders are invited to test the assessment list and provide practical feedback on how it can be improved. This feedback will allow for a better understanding of how the assessment list, which is aimed to offer guidance for all AI applications, can be implemented within an organisation. It will also indicate where specific tailoring of the assessment list is needed given AI's context-specificity. All interested stakeholders can participate to the piloting process and start testing out the assessment list. An open survey or "quantitative analysis" which will be sent to all those who register to the piloting; The piloting phase will run from the 26th of June until the 1st of December 2019.

Technology progresses daily and with this progression come threats and risks to social, financial and economic life. Today, cyber-attackers have resorted to the use of automation to launch more frequent attacks on different businesses and corporations. While cyber-attackers are expending a lot of resources to launch more sophisticated attacks, many organizations still rely on manual efforts to gather internal security findings and contextualize them with external threat information. It was reported that carelessness of employee was the reason behind the ransomware attack in 51 percent of the cases. However, such outdated methods and strategies need to part way for AI because they use up a lot of time, in which cyber-attackers can successfully take advantage of vulnerabilities to breach systems and steal data.

The widespread shortage of skilled security operations and threat intelligence resources in security operations centers (SOCs) leaves many organizations open to the increased risk of a security incident. That's because they are unable to effectively investigate all discovered, potentially malicious behaviors in their environment in a thorough and repeatable way. According to ESG, two-thirds of security professionals believe the cybersecurity skills gap has led to an increased workload for existing staff. "Since organizations don't have enough people, they simply pile more work onto those that they have," wrote ESG Senior Principal Analyst Jon Oltsik. "This leads to human error, misalignment of tasks to skills, and employee burnout."

This document was written by the High-Level Expert Group on AI (AI HLEG). It is the second deliverable of the AI HLEG and follows the publication of the group's first deliverable, Ethics Guidelines for Trustworthy AI, published on 8 April 2019. The AI HLEG is an independent expert group that was set up by the European Commission in June 2018.

TEHRAN - Iran said Tuesday it will further free itself from the 2015 nuclear deal in defiance of new American sanctions as U.S. President Donald Trump warned the Islamic republic of "overwhelming" retaliation for any attacks. Tensions between Iran and the U.S. have spiraled since last year when Trump withdrew the United States from the deal under which Tehran was to curb its nuclear program in exchange for relief from economic sanctions. The two arch-rivals have been locked in an escalating war of words since Iran shot down a U.S. surveillance drone in what it said was its own airspace, a claim the US vehemently denies. On Monday, Washington stepped up pressure by blacklisting Iran's supreme leader Ayatollah Ali Khamenei and top military chiefs, saying it would also sanction Foreign Minister Mohammad Javad Zarif later in the week. Tehran was defiant on Tuesday, saying the new US sanctions against Iran showed Washington was "lying" about an offer of talks.

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate this issue from a new perspective, which purely relies on logits, the class scores before softmax, to detect and defend adversarial attacks. Our defender is a two-layer network trained on a mixed set of clean and perturbed logits, with the goal being recovering the original prediction. Upon a wide range of adversarial attacks, our simple approach shows promising results with relatively high accuracy in defense, and the defender can transfer across attackers with similar properties. More importantly, our defender can work in the scenarios that image data are unavailable, and enjoys high interpretability especially at the semantic level.

The rise of artificial intelligence (A.I.) based systems has the potential to benefit adopters and society as a whole. However, these systems may also enclose potential conflicts and unintended consequences. Notably, people will only adopt an A.I. system if it confers them an advantage, at which point non-adopters might push for a strong regulation if that advantage for adopters is at a cost for them. Here we propose a stochastic game theoretical model for these conflicts. We frame our results under the current discussion on ethical A.I. and the conflict between individual and societal gains, the societal value alignment problem. We test the arising equilibria in the adoption of A.I. technology under different norms followed by artificial agents, their ensuing benefits, and the emergent levels of wealth inequality. We show that without any regulation, purely selfish A.I. systems will have the strongest advantage, even when a utilitarian A.I. provides a more significant benefit for the individual and the society. Nevertheless, we show that it is possible to develop human conscious A.I. systems that reach an equilibrium where the gains for the adopters are not at a cost for non-adopters while increasing the overall fitness and lowering inequality. However, as shown, a self-organized adoption of such policies would require external regulation.