This fall, Gabon was facing an odd and tenuous political situation. President Ali Bongo had been out of the country since October receiving medical treatment in Saudi Arabia and London and had not been seen in public. People in Gabon and observers outside the country were growing suspicious about the president's well being, and the government's lack of answers only fueled doubts; some even said he was dead. After months of little information, on December 9th, the country's vice president announced that Bongo had suffered a stroke in the autumn, but remained in good shape. Despite such assurances, civil society groups and many members of the public wondered why Bongo, if he was well, had not made any public appearances, save for a few pictures of him released by the government along with a silent video.

In new video game'The Division 2,' players explore Washington, D.C., which has been decimated by a virus. Draining the swamp pales in comparison to the problems facing the nation's capital in new video game "Tom Clancy's The Division 2." The White House is under attack. An enemy force occupies the Lincoln Memorial and a wrecked Air Force One has crashed at the foot of the U.S. Capitol. Chaos reigns as the result of a pandemic, which has left Washington decimated. In the role of an agent for the Strategic Homeland Division, the player is tasked with protecting the division's makeshift headquarters at the White House and assisting survivors to improve their ragtag existence.

US Senators Roy Blunt and Brian Schatz want to protect people's facial recognition data and make it much harder to sell now that information is treated as currency. The lawmakers have introduced the bipartisan Commercial Facial Recognition Privacy Act of 2019, which prohibits companies from collecting and resharing face data for identifying or tracking purposes without people's consent. The Senators have conjured up the bill because while facial recognition has been used for security and surveillance for decades, it's "now being developed at increasing rates for commercial applications." They argue that a lot of people aren't aware that the technology is being used in public spaces and that companies can collect identifiable info to share or sell to third parties -- similar to how carriers have been selling location data to bounty hunters for years. In addition to prohibiting companies from redistributing or disseminating data, the bill would also require them to notify customers whenever facial recognition is in use. FR technologies also need to undergo third-party testing prior to implementation to address accuracy and bias issues, seeing as they tend to have higher error rates when it comes to women and people of color.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

In July 2017, China's government published an ambitious policy paper, outlining how the country would become the world leader in AI by the year 2030. But by some measures China has already succeeded in this goal -- a decade ahead of schedule. A new study shows that China's output of influential AI research papers will soon overtake that of the US, the world's current number one in AI research. The finding suggests that China's plan to expand its AI capabilities with the help of generous government investment in both educational facilities and private industry is paying off. In terms of sheer volume of AI papers published each year, China surpassed America back in 2006, but critics have pointed out that quantity does not necessarily equal quality.

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A PwC report estimates that by 2030, 70 percent of the profits generated by artificial intelligence (AI) technologies will be shared between the U.S. and China. While the two countries compete to develop the most advanced AI applications, there are also many opportunities for cooperation to mitigate the technology's potential risks. On March 12, The Center for Technology Innovation hosted a panel discussion where Brookings scholars Darrell West, Nicol Turner-Lee, and Ryan Haas were joined by Robb Gordon, the chief counsel and director of Intel's China legal team. The panel examined how the two nations have deployed artificial intelligence technologies so far and how they plan to use them in the future. China hopes to become a global leader in AI in the next decade, and has committed to investing $150 billion to achieve this goal.

The gender pay gap has always been a topic of debate but never has it ever been able to bring the much-required change in the system. Time and again, feminists have raised their voices against such inequalities. Infact, they have been very right in stating that the women are sharing responsibilities equally then why not authority? Besides, many compensation guidelines and policies have also been articulated by the government but little did all of that benefit. Otherwise, the rate at which the global economy is embracing the removal of the gender pay gap can take the next 202 years to hit the equilibrium. In this blog, I will take you through the ways in which AI can be the most practical method to remove the gender pay gap.

As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot's behavior. These approaches, however, fail to consider the cognitive effort needed to understand the received explanation. In particular, the human teammate is expected to understand any explanation provided before the task execution, no matter how much information is presented in the explanation. In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps to spread out the information to be explained and thus reducing the cognitive load of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented. We base our explanation generation method in a model reconciliation setting introduced in our prior work. Our approach is evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with four different problems.

In the present scenario of domestic flights in USA, there have been numerous instances of flight delays and cancellations. In the United States, the American Airlines, Inc. have been one of the most entrusted and the world's largest airline in terms of number of destinations served. But when it comes to domestic flights, AA has not lived up to the expectations in terms of punctuality or on-time performance. Flight Delays also result in airline companies operating commercial flights to incur huge losses. So, they are trying their best to prevent or avoid Flight Delays and Cancellations by taking certain measures. This study aims at analyzing flight information of US domestic flights operated by American Airlines, covering top 5 busiest airports of US and predicting possible arrival delay of the flight using Data Mining and Machine Learning Approaches. The Gradient Boosting Classifier Model is deployed by training and hyper-parameter tuning it, achieving a maximum accuracy of 85.73%. Such an Intelligent System is very essential in foretelling flights'on-time performance.