Changing security concerns following the dismantling of Islamic State and rising geopolitical tensions have caused European countries to shift defense efforts from far-away conflicts to homeland security, resulting in demand for drones that can be safely integrated into civilian airspace to, for example, monitor border crossings, Elbit officials said.

Last month, while tracking dark web marketplaces, threat intel team Insikt Group of the security firm Recorded Future discovered that someone was selling alleged US military documents. A hacker was asking for "$150 to $200" for non-classified yet sensitive materials on the US Air Force's Reaper drone, and posted an additional bundle of information on US Army vehicles and tactics for sale. According to Insikt's report, the team verified the documents after contacting the hacker. They learned that the intruder used an FTP vulnerability in Netgear routers that's been known for two years to break into a computer at the Creech Air Force Base in Nevada. The hacker took documents about the MQ-9 Reaper drone, including maintenance course books and a list of airmen assigned to fly it.

LONDON – The rise of robots in manufacturing in Southeast Asia is likely to fuel modern-day slavery as workers who end up unemployed due to automation face abuses competing for a shrinking pool of low-paid jobs in a "race to the bottom," analysts said Thursday. Drastic job losses due to the growth of automation in the region -- a hub for many manufacturing sectors from garments to vehicles -- could produce a spike in labor abuses and slavery in global supply chains, said risk consultancy Verisk Maplecroft. More than half of the workers in Cambodia, Indonesia, Thailand, Vietnam and the Philippines -- at least 137 million people -- risk losing their jobs to automation in the next two decades, the United Nations' International Labour Organization says. The risk of slavery tainting supply chains will spiral because workers who lose their jobs due to increased robot manufacturing will be more vulnerable to workplace abuses as they jostle for fewer jobs at lower wages, said Alexandra Channer of Maplecroft. "Displaced workers without the skills to adapt or the cushion of social security will have to compete for a diminishing supply of low-paid, low-skilled work in what will likely be an increasingly exploitative environment," she said.

HMRC's use of AI and big data to gather evidence in tax investigations has led to a 30% drop in property raids, according to law firm Pinsent Masons. The firm explained that HMRC has leveraged sophisticated algorithms and big data sources to gather evidence with greater ease and efficiency than costly and time-consuming property raids. Steven Porter, Partner at Pinsent Masons, said: "HMRC's big brother-style data collection on taxpayers is giving it the material it needs to ramp up its tax investigations and at the same time, is reducing the need for it to actually raid properties." The figure has dropped from 669 property raids in 2016/17 to 471 last year. In particular, tax inspectors have been using the state-of-the-art Connect database, an analytical system worth £80m and designed by BAE Systems, to carry out preliminary investigative work within seconds.

While America struggles to retain net neutrality protections on a piecemeal state-by-state level, India just adopted its own seemingly-strong set of rules that largely ban artificial slowing or blocking of content. The Telecom Regulatory Authority of India (TRAI) proposed the rules back in November, and the country's official Telecommunications Commission (TC) approved them today, resolving a movement to update internet protections that began in 2015. From today onward, ISPs cannot perform actions involving "blocking, degrading, slowing down or granting preferential speeds or treatment to any content," per The Wire's report. Providers are also prohibited from providing zero-rated content. These include remote surgery or autonomous vehicles, which Sundarajan compared to laws allowing ambulances to ignore traffic rules.

The cornerstone of collaboration is based on knowledge transfer; sharing of research tools, methodologies and findings; and sometimes combining mutual funding resources to meet shortfalls necessary to build prototypes and commercialize technologies. Collaborations often involve combinations of government, industry and academia who work together to meet difficult challenges and cultivate new ideas. A growing trend for many leading companies is creating technology specific innovation centers, labs, and foundries to accelerate collaboration and invention. As the development of new technologies continues to grow exponentially and globally, collaboration has more value as a resource for adapting to the rapidly emerging technologies landscape by establishing pivotal connections between companies, technologies and stakeholders. In the US Federal government, the National Labs (including: Lawrence Livermore, Oak Ridge, Argonne, Sandia, Idaho National Laboratory, Battelle, and Brookhaven, and Federally Funded Research and Development Centers (FFRDC's), and federally funded Centers For Excellence have been outlets for innovation and public/private cooperation.

You are free to share this article under the Attribution 4.0 International license. Researchers have created an artificial intelligence system for predicting, not simply tracking, potential side effects from drug combinations. Last month alone, 23 percent of Americans took two or more prescription drugs, according to one CDC estimate. Furthermore, 39 percent over age 65 take five or more, a number that's increased three-fold in the last several decades. And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy.

GDPR (The General Data Protection Regulation) is a very significant EU law that offers major new data and privacy protection for all individuals within the European Union (EU) and the European Economic Area (EEA). GDPR took effect on May 25, 2018. You probably have all received countless emails from companies updating their privacy policies to comply with GDPR. These were written prior to the introduction of the new regulations, when the ambiguity of the new regulations had everyone second-guessing as to what affect the machine learning field would feel. So, two months on, has it become clearer?

The hacker sought buyers for maintenance documents about the MQ-9 Reaper drone, a remotely controlled aerial vehicle used by the Pentagon and other parts of the government to conduct offensive strikes or reconnaissance and surveillance operations. Discovery of the attempted sale of the stolen documents comes amid heightened concern about how U.S. military secrets may be insufficiently protected from hackers. Military officials said last month that the Defense Department's inspector general was investigating a major security breach after Chinese hackers allegedly stole data pertaining to submarine warfare, including plans to build a supersonic antiship missile. There was no evidence that the hacker who acquired the Reaper drone documents was affiliated with a foreign country, or that he was intentionally seeking to obtain military documents, said Andrei Barysevich, a senior threat researcher at Recorded Future, the U.S.-based cybersecurity firm that spotted the attempted sale. Instead, the hacker scanned large parts of the internet for misconfigured Netgear routers and exploited a two-year-old known vulnerability, involving default login credentials, to steal files from compromised machines.

Scarcity of labeled data is one of the most frequent problems faced in machine learning. This is particularly true in relation extraction in text mining, where large corpora of texts exists in many application domains, while labeling of text data requires an expert to invest much time to read the documents. Overall, state-of-the art models, like the convolutional neural network used in this paper, achieve great results when trained on large enough amounts of labeled data. However, from a practical point of view the question arises whether this is the most efficient approach when one takes the manual effort of the expert into account. In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results. Distant supervision provides a mean of labeling data given known relations in a knowledge base, but it suffers from noisy labeling. We introduce an active learning based extension, that allows our neural network to incorporate expert feedback and report on first results on a complex data set.