The American drone attack that killed Iranian Gen. Qassem Soleimani last week is the latest move in a low-level war between Iran and the U.S. that has been waged with varying degrees of intensity for over 40 years – and is likely to continue long into the future. Some people fear that recent events will escalate the long conflict into a costly all-out war between the two countries. Others may welcome what they see as the necessary and inevitable showdown leading ultimately to regime change in Tehran. The killing of Soleimani – the most prominent military figure in Iran and close to Supreme Leader Ayatollah Ali Khamenei – can be seen as an escalation and will almost certainly provoke Iranian retaliation. President Trump's boast of ordering the killing of Soleimani may further increase pressure on Iran to respond.

The major objective of the Cognitive Systems & Artificial Intelligence In BFSI market report is to help the user understand the market in terms of its definition, segmentation, market potential, influential trends, and the challenges that the market is facing. This research is conducted to understand the current landscape of the market, especially in 2019 up-to the forecast year 2025. The readers will find this report very helpful in understanding the Cognitive Systems & Artificial Intelligence In BFSI market in depth. The data and the information regarding the market are taken from reliable sources such as websites, annual reports of the companies, journals, and others and were checked and validated by the industry experts. The facts and data are represented in the report using diagrams, graphs, pie charts, and other pictorial representations.

Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requires considerable time and experimentation. On the other hand, the number of optimization problems in the industry continues to grow. In recent years, machine learning techniques have been explored to address this gap. We propose a framework for leveraging machine learning techniques to scale-up exact combinatorial optimization algorithms. In contrast to the existing approaches based on deep-learning, reinforcement learning and restricted Boltzmann machines that attempt to directly learn the output of the optimization problem from its input (with limited success), our framework learns the relatively simpler task of pruning the elements in order to reduce the size of the problem instances. In addition, our framework uses only interpretable learning models based on intuitive features and thus the learning process provides deeper insights into the optimization problem and the instance class, that can be used for designing better heuristics. For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques. This results in several fold speedups of state-of-the-art algorithms. Furthermore, the model used in our framework highlights that the chi-squared value of neighborhood degree has a statistically significant correlation with the presence of a node in a maximum clique, particularly in dense graphs which constitute a significant challenge for modern solvers. We leverage this insight to design a novel heuristic for this problem outperforming the state-of-the-art. Our heuristic is also of independent interest for maximum clique detection and enumeration.

We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the negation, disjunction and conjunction of a set of base tasks. We then show that by learning goal-oriented value functions and restricting the transition dynamics of the tasks, an agent can solve these new tasks with no further learning. We prove that by composing these value functions in specific ways, we immediately recover the optimal policies for all tasks expressible under the Boolean algebra. We verify our approach in two domains, including a high-dimensional video game environment requiring function approximation, where an agent first learns a set of base skills, and then composes them to solve a super-exponential number of new tasks.

WhiteFox Defense Strategic Advisor and drone expert Brett Velicovich discusses the operation and mission of the airstrike that hit General Qassem Soleimani. You only get "one shot" while taking down a target like Iranian General Qassem Soleimani, drone expert Brett Velicovich said Saturday. Appearing on "America's News HQ: Weekend" with host Ed Henry, Velicovich -- who once tracked Soleimani's movements -- said that the drone strike on Soleimani was a " forceful reminder that the Iranians can no longer attack Americans with impunity, [as well as] that the U.S. government can retaliate with a wide variety of options that are both devastating actions that are short of war." "Thanks to President Trump's decisive action, we are able to use one of the tools within the government's arsenal to strike and to strike Soleimani with precision," he added. The MQ-9 Reaper drone was used to strike Soleimani early Friday at the Baghdad International Airport. With a range of 1,150 miles and the ability to fly at altitudes of 50,000 feet, the Reaper weighs almost 5,000 pounds.

Iran vows retaliation; Lt. Col. Daniel Davis, Walid Phares, and Rep. Mark Green react. A giant question mark hangs over the Middle East as the world waits to see what action Iran will take to retaliate for the long-overdue killing Friday morning of Iranian Gen. Qassem Soleimani in a drone strike ordered by President Trump. President Trump made the right decision in ordering Soleimani killed in Iraq. I've been arguing for four years that we ought to take out this dangerous enemy of the United States, who was responsible for the deaths of hundreds of Americans and wanted to kill many more. Thankfully, his killing days are over.

BAGHDAD – A fresh airstrike hit pro-Iran fighters in Iraq early Saturday, as fears grew of a proxy war erupting between Washington and Tehran a day after an American drone strike killed a top Iranian general. The killing of Quds Force commander Gen. Qassem Soleimani in Baghdad on Friday was the most dramatic escalation yet in spiralling tensions between Iran and the United States, which pledged to send more troops to the region -- even as President Donald Trump insisted he did not want war. Iran's ambassador to the United Nations, Majid Takht Ravanchi, told CNN that the killing was an "act of war on the part of the United States." A new strike on Saturday targeted a convoy belonging to the Hashed al-Shaabi, an Iraqi paramilitary network dominated by Shiite factions with close ties to Iran. The Hashed did not say who it held responsible but Iraqi state television reported it was a U.S. airstrike.

The Artificial Intelligence market has witnessed growth from USD XX million to USD XX million from 2014 to 2019. With the CAGR of X.X%, this market is estimated to reach USD XX million in 2026. The report mainly studies the size, recent trends and development status of the Artificial Intelligence market, as well as investment opportunities, government policy, market dynamics (drivers, restraints, opportunities), supply chain and competitive landscape. Technological innovation and advancement will further optimize the performance of the product, making it more widely used in downstream applications. Moreover, Porter's Five Forces Analysis (potential entrants, suppliers, substitutes, buyers, industry competitors) provides crucial information for knowing the Artificial Intelligence market.

Radiology artificial intelligence (AI) was again the hottest topic at the 2019 Radiological Society of North America (RSNA) annual meeting in December. AI was a primary theme in the larger booths in the north and south expo floors, as well as on the new third expo floor dedicated AI showcase. The separate AI show floor did not make many AI vendors happy. Many wanted the artificial intelligence showcase on the same level as the other expo halls to reduce the shuttling between the floors for meetings. RSNA organizers pointed out to one startup that due to the sheer number of AI exhibitors, they had to give the showcase its own space.

Artificial intelligence (AI) is now receiving unprecedented global attention as it finds widespread practical application in multiple spheres of activity. But what are the human rights, social justice and development implications of AI when used in areas such as health, education and social services, or in building "smart cities"? How does algorithmic decision making impact on marginalised people and the poor? This edition of Global Information Society Watch (GISWatch) provides a perspective from the global South on the application of AI to our everyday lives. It includes 40 country reports from countries as diverse as Benin, Argentina, India, Russia and Ukraine, as well as three regional reports.