LONDON--(BUSINESS WIRE)--Deep Instinct, the first and only cybersecurity company to apply end-to-end deep learning to predict, identify, and prevent cyberattacks, is continuing its strategic expansion into EMEA, contracting with T-Systems (Poland), one of the region's largest IT services providers, to utilize and distribute Deep Instinct's protection to its customers. Deep Instinct also signed strategic partnership agreements with Cyber Monks and Spinnakar to distribute Deep Instinct's deep learning-based solution across the region. Leading Deep Instincts' EMEA expansion is Brooks Wallace, VP Sales EMEA, a veteran cybersecurity sales leader with over 20 years of experience in building sales teams. Wallace will oversee the newly opened sales and support office in the UK and forge additional strategic partnerships with MSSPs across the region. "Our expansion into EMEA comes at a critical time for the region, and contracting with T-Systems Poland attests to the unique value of our deep learning-based cyber-attack prevention solution," said Guy Caspi, CEO and Co-founder of Deep Instinct.

Army researchers developed a reinforcement learning approach that will allow swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing performance uncertainty.Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions.Army researchers said future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats targeting U.S. forces.The Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks, said Dr. Jemin George of the U.S. Army Combat Capabilities Development Command's Army Research Laboratory."Finding optimal guidance policies for these swarming vehicles in real-time is a key requirement for enhancing warfighters' tactical situational awareness, allowing the U.S. Army to dominate in a contested environment," George said.Reinforcement learning ...

It has been more than two months since the killing of George Floyd at the hands of police in the United States. And as protests continue - the message is no longer just about specific incidents of violence, but about what demonstrators say is systemic racism in policing. One of the most obvious examples is the widespread use of facial recognition systems that have been proven to misidentify people of colour.

It sounds like a sci-fi movie: pitting an artificial intelligence against human pilots. Sadly, DARPA will no longer hold an in-person event for its third and final AlphaDogfight Trial. It'll happen virtually, instead, with participants and viewers watching online as AI algorithms control simulated F-16 fighter planes in aerial combat. By the end of the three-day event, viewers will witness a matchup between the top AI and an experienced Air Force fighter pilot, who'll also be controlling a virtual F-16. If you're interested, you need to register beforehand to tune in.

Whether we realize it or not, our digital lives and what we see on the internet are controlled and determined by algorithms and analytics. Through them, businesses learn what our preferences are and what we're drawn to in order to target us with information. The idea is to present us with information that is most relevant to us. In the same way, cybersecurity professionals are constantly faced with an enormous amount of threat data to sift through and prioritize on a daily basis. In fact, "too much data to analyze" is the number one obstacle inhibiting companies from defending against cyber threats according to the 2019 Cyberthreat Defense Report by CyberEdge.

We face great challenges in a globalized and modern world that we, humanity, have built. The fulfillment of the 17 United Nations Sustainable Development Goals (SDGs) in 2030 is essential to make a planet with a viable future. Achieving them not only depends on the will of governments, institutions or people. The application of technologies that, with their multiplying effect, allow achieving the goals is extremely important. Technological innovation plays a decisive role in the evolution of changes towards a new model that involves improving development, without leaving anyone behind, and with the focus on avoiding inequality and injustice, ensuring better protection of the environment.

It's no secret that the legal field is a competitive environment. Junior lawyers are undeterred by (and perhaps even attracted to) the cutthroat nature of the business, and one-upping the competitor is necessary to get a job in the legal field. Firms turn to the latest and greatest tech development to compete with each other and "keep up with the [legal] Joneses." In 2019 alone, investments in B2B legal tech soared past $1 billion. Still, some legal professionals fear that cutting-edge technology, such as artificial intelligence (AI), will eliminate the role of junior lawyers in the future. It's clear to many, however, that law firms must incorporate new legal tech developments in order to attract top talent, remain a top competitor, and mold their junior lawyers to be better than the next.

Three friends were having morning tea on a farm in the Northern Rivers region in New South Wales (NSW), Australia, when they noticed a drilling rig setting up in a neighbor's property on the opposite side of the valley. They had never heard of the coal seam gas (CSG) industry, nor had they previously considered activism. That drilling rig, however, was enough to push them into action. The group soon became instrumental in establishing the anti-CSG movement, a movement whose activism resulted in the NSW government suspending gas exploration licenses in the area in 2014.2 By 2015, the government had bought back a petroleum exploration license covering 500,000 hectares across the region.3 Mining companies, like companies in many industries, have been struggling with the difference between having a legal license to operate and a moral4 one. The colloquial version of this is the distinction between what one could do and what one should do--just because something is technically possible and economically feasible doesn't mean that the people it affects will find it morally acceptable. Without the acceptance of the community, firms find themselves dealing with "never-ending demands" from "local troublemakers" hearing that "the company has done nothing for us"--all resulting in costs, financial and nonfinancial,5 that weigh projects down. A company can have the best intentions, investing in (what it thought were) all the right things, and still experience opposition from within the community. It may work to understand local mores and invest in the community's social infrastructure--improving access to health care and education, upgrading roads and electricity services, and fostering economic activity in the region resulting in bustling local businesses and a healthy employment market--to no avail. Without the community's acceptance, without a moral license, the mining companies in NSW found themselves struggling. This moral license is commonly called a social license, a phrase coined in the '90s, and represents the ongoing acceptance and approval of a mining development by a local community. Since then, it has become increasingly recognized within the mining industry that firms must work with local communities to obtain, and then maintain, a social license to operate (SLO).6 The concept of a social license to operate has developed over time and been adopted by a range of industries that affect the physical environment they operate in, such as logging or pulp and paper mills. What has any of this to do with artificial intelligence (AI)?

We study the average-case complexity of the classic Minesweeper game in which players deduce the locations of mines on a two-dimensional lattice. Playing Minesweeper is known to be co-NP-complete. We show empirically that Minesweeper exhibits a phase transition analogous to the well-studied SAT phase transition. Above the critical mine density it becomes almost impossible to play Minesweeper by logical inference. We use a reduction to Boolean unsatisfiability to characterize the hardness of Minesweeper instances, and show that the hardness peaks at the phase transition. Furthermore, we demonstrate algorithmic barriers at the phase transition for polynomial-time approaches to Minesweeper inference. Finally, we comment on expectations for the asymptotic behavior of the phase transition.

In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems.