The UK government is planning to introduce fresh rules for drone owners including a new requirement to sit safety tests and potentially no-fly zones. When you undergo the time-consuming and expensive process to gain a car license in the UK, you are expected to pass not only practical tests but sit an exam on the highway code and click your way through tedious hazard perception tests. Now, UK drone owners may be required to undergo a similar process to fly their beloved devices. As reported by the BBC, new legislation undergoing scrutiny by the UK government will require drone hobbyists to sit safety awareness tests to legally fly drones. Should drones be flown without this requirement in place, police will have new powers to confiscate drones which "may have been used in criminal activity," according to the publication, and this potentially could be extended to those flying illegally and without a license.

Germany's recent move to ban the sale of children's smartwatches with built-in voice recognition technology may lead privacy regulators to adopt a ban for other such devices, privacy professionals told Bloomberg Law. Inc.'s Alexa, become commonplace, German privacy officials may seek to ban them or restrict how they are used. The global voice and speech recognition market is valued at nearly $6.2 billion in 2017 and is likely to reach $18.3 billion by 2023, according to an August report by competitive market research company MarketsAndMarkets Research Private Ltd. "If something is said in your living room and that speech is recorded and passed along to data providers, it's exactly the same as surveillance, Marit Hansen, data protection officer for the German state of Schleswig-Holstein, told Bloomberg Law. As voice-controlled devices continue to grow, so too will data privacy concerns over devices that store voice commands and back up the data through a central server, Hansen said. German regulators are considering how to regulate such devices under German privacy law amendments set to take effect when the European Union's new privacy regime, the General Data Protection Regulation takes effect in May 2018, she said.

Cyber security companies are turning to artificial intelligence and machine learning tools to ward off growing number of attacks on networks, Finland-based internet security firm F-Secure said. As the world is fast moving towards Internet of Things and connected devices, deployment of artificial intelligence (AI) has become inevitable for cyber security firms to analyse huge amount of data to save networks from infiltration attempts, F-Secure's Security Advisor Sean Sullivan said. Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks. On a daily basis, F-Secure Labs on an average receives sample data of 500,000 files from its customers that include 10,000 malware variants and 60,000 malicious URLs for analysis and protection, Sullivan said. For humans, it is a big task to go through such huge amount of data and machine learning tools and AI are lending a helping hand at this stage, he said.

WASHINGTON (Reuters) - A coalition of rights groups launched an online petition on Thursday urging IBM Corp to declare that it will not develop technology to help the Trump administration carry out a proposal to identify people for visa denial and deportation from the United States. IBM and several other technology companies and contractors, including Booz Allen Hamilton, LexisNexis and Deloitte [DLTE.UL], attended a July informational session hosted by immigration enforcement officials that discussed developing technology for vetting immigrants, said Steven Renderos, organizing director at petitioner the Center for Media Justice. President Donald Trump has pledged to harden screening procedures for people looking to enter the country, and also called for "extreme vetting" of certain immigrants to ensure they are contributing to society, saying such steps are necessary to protect national security and curtail illegal immigration. The rights group said the proposals run counter to IBM's stated goals of protecting so-called "Dreamer" immigrants from deportation. Asked about the petition and whether it planned to work to help vet and deport immigrants, an IBM spokeswoman said the company "would not work on any project that runs counter to our company's values, including our long-standing opposition to discrimination against anyone on the basis of race, gender, sexual orientation or religion."

Scientists have developed the world's first artificial intelligence politician, that can answer a person's queries regarding local issues such as policies around housing, education and immigration. The virtual politician, called SAM, was created by Nick Gerritsen, a 49-year-old entrepreneur in New Zealand. "There is a lot of bias in the'analogue' practice of politics right now," said Gerritsen. "There seems to be so much existing bias that countries around the world seem unable to address fundamental and multiple complex issues like climate change and equality," he said. The AI politician is constantly learning to respond to people through Facebook Messenger as well as a survey on its homepage.

The government's plan to boost UK industry ahead of the country leaving the EU is due to be unveiled later. The industrial strategy is aimed at lifting growth, which official forecasts suggest will slow due to the UK's poor productivity performance. Business Secretary Greg Clark said the UK's decision to leave the EU meant the strategy was "even more important". A deal with US healthcare giant MSD to open a UK research centre has been announced as part of the strategy. The investment by MSD, known as Merck in the US, is worth up to £1bn and is expected to create 950 jobs.

Complex architectures of biological neural circuits, such as parallel processing pathways, has been behaviorally implicated in many cognitive studies. However, the theoretical consequences of circuit complexity on neural computation have only been explored in limited cases. Here, we introduce a mechanism by which direct and indirect pathways from cortex to the CA3 region of the hippocampus can balance both contextual gating of memory formation and driving network activity. We implement this concept in a deep artificial neural network by enabling a context-sensitive bias. The motivation for this is to improve performance of a size-constrained network. Using direct knowledge of the superclass information in the CIFAR-100 and Fashion-MNIST datasets, we show a dramatic increase in performance without an increase in network size.

We address the problem of prescribing an optimal decision in a framework where its cost depends on uncertain problem parameters $Y$ that need to be learned from data. Earlier work by Bertsimas and Kallus (2014) transforms classical machine learning methods that merely predict $Y$ from supervised training data $[(x_1, y_1), \dots, (x_n, y_n)]$ into prescriptive methods taking optimal decisions specific to a particular covariate context $X=\bar x$. Their prescriptive methods factor in additional observed contextual information on a potentially large number of covariates $X=\bar x$ to take context specific actions $z(\bar x)$ which are superior to any static decision $z$. Any naive use of limited training data may, however, lead to gullible decisions over-calibrated to one particular data set. In this paper, we borrow ideas from distributionally robust optimization and the statistical bootstrap of Efron (1982) to propose two novel prescriptive methods based on (nw) Nadaraya-Watson and (nn) nearest-neighbors learning which safeguard against overfitting and lead to improved out-of-sample performance. Both resulting robust prescriptive methods reduce to tractable convex optimization problems and enjoy a limited disappointment on bootstrap data. We illustrate the data-driven decision-making framework and our novel robustness notion on a small news vendor problem as well as a small portfolio allocation problem.

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.

SANAA, Yemen – Yemeni security and tribal officials say a suspected U.S. drone strike has killed three alleged al-Qaida fighters in the country's central Bayda province. They say the Sunday strike was the third of its kind in a week in the province, a stronghold for the group. They spoke on condition of anonymity for fear of reprisals. Yemen fell into chaos following its 2011 Arab Spring uprising that deposed longtime autocrat Ali Abdullah Saleh, now allied with Shiite rebels from the north who have occupied much of the country and are fighting his successor. A Saudi-led coalition has been battling the rebels and Saleh's forces since March 2015.