Named entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document. It is difficult to link a mention when it appears multiple times in a document, since there are conflicts by the contexts around the appearances of the mention. In addition, it is difficult since the given training dataset is small due to the reason that it is done manually to link a mention to its mapping entity. In the literature, there are many reported studies among which the recent embedding methods learn vectors of entities from the training dataset at document level. To address these issues, we focus on how to link entity for mentions at a sentence level, which reduces the noises introduced by different appearances of the same mention in a document at the expense of insufficient information to be used. We propose a new unified embedding method by maximizing the relationships learned from knowledge graphs. We confirm the effectiveness of our method in our experimental studies.

Researchers at the University of Buffalo have received a $316,000 grant from the Defense Advanced Research Projects Agency (DARPA), an agency funded by the US Department of Defense, to develop an artificial intelligence capable of controlling swarms of up to 250 drones. To created the experimental AI, scientists from the university's Artificial Intelligence Institute will study video game players as they pilot autonomous swarms of digital military units in real time strategy games like StarCraft, Stellaris, and Company of Heroes. The team will collect data on how the players react to a wide variety of different tactical challenges as well as watching how they react to unexpected changes in the terrain or terms of battle. Researchers at the University of Buffalo's Artificial Intelligence Institute will study the way video game players make choices in real time strategy games like StarCraft and Company of Heroes to develop an AI that can control swarms of up to 250 drones'We don't want the AI system just to mimic human behavior; we want it to form a deeper understanding of what motivates human actions,' University of Buffalo's Souma Chowdhury told the school's news site. 'That's what will lead to more advanced AI.' The team will also collect a range of biometric data from the players, through eyetracking software and electroencephalograms, which monitors brain activity while they play.

According to a new scientific study, a nuclear attack of 100 bombs could harm the entire planet including the aggressor nation. Since the creation of the atom bomb, the threat of nuclear war has loomed. Endless films and books have dealt with the nuclear apocalypse and its aftermath, but what would a nuclear apocalypse really look like? Rutgers University Professor Alan Robock spoke with Fox News about the Armageddon and his team's new study regarding a nuclear war's effects on ocean life. If you live in a major city when a nuke hits, needless to say, you're in big trouble.

And as the injury toll has mounted, veterans groups and others have levied criticism at the White House, in part because, in January, President Trump dismissed the injuries as "not very serious." "I heard that they had headaches and a couple of other things," Mr. Trump said at a news conference Jan. 22 in Davos, Switzerland. "I don't consider them very serious injuries relative to other injuries I have seen." At least a dozen missiles were fired during the attack, which was a retaliation for the killing of a top Iranian general, Qassim Suleimani, by an American drone strike in Baghdad on Jan. 3. The Trump administration at first said there were no injuries, but a week later said several service members were evaluated for possible concussions.

As the deadly 2019-nCov coronavirus spreads, raising fears of a worldwide pandemic, researchers and startups are using artificial intelligence and other technologies to predict where the virus might appear next -- and even potentially sound the alarm before other new, potentially threatening viruses become public health crises. "What we're doing currently with Coronavirus is really trying to get an understanding of what's happening on the ground through as many sources as we can get our hands on," says John Brownstein, chief innovation officer at Boston Children's Hospital and a professor at Harvard Medical School. After SARS killed 774 people around the world in the mid-2000s, his team built a tool called Healthmap, which scrapes information about new outbreaks from online news reports, chatrooms and more. Healthmap then organizes that previously disparate data, generating visualizations that show how and where communicable diseases like the coronavirus are spreading. Healthmap's output supplements more traditional data-gathering techniques used by organizations like the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).

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Prior to 2018, regulators resisted recommending the use of Machine Learning (ML) based Artificial Intelligence (AI) for AML compliance. There was a mindset shift in mid 2018 indicating that proceeding with caution in implementing AI approaches for AML is appropriate. Regulators realize the adoption of recent innovation, such as the use of AI-ML and robotic process automation (RPA) techniques, enables AML compliance improvements not otherwise attainable. A risk-based approach to compliance, underpinned by AI/Machine Learning, creates opportunities for governance and process refinement as well as identifying potential untapped revenues. Reliance on box-ticking approaches familiar to users of legacy rules-based compliance systems is no longer sufficient.

On January 1, 2020, Illinois' new Artificial Intelligence Video Interview Act (AIVIA) went into effect, meaning Illinois employers must now comply with the law if they use artificial intelligence (AI) to analyze video interviews by job candidates. As we outlined in a prior post, the AIVIA imposes duties of transparency, consent and data destruction on organizations using AI to evaluate interviewees for jobs that are "based" in Illinois. While these concepts may be clear in the abstract, the Illinois law is a lesson in brevity and leaves several key terms undefined (including, for example, the term "artificial intelligence"). Nor is it clear what it means for a position to be "based" in Illinois. As a result, employers using AI-enabled analytics in interview videos must sort through these questions and take other affirmative steps to ensure compliance with the new law.

The Army has many data problems. But when it comes to the data that underlies facial recognition, one sticks out: Enemies want to poison the well. Adversaries are becoming more sophisticated at providing "poisoned," or subtly altered, data that will mistrain artificial intelligence and machine learning algorithms. To try and safeguard facial recognition databases from these so-called backdoor attacks, the Army is funding research to build defensive software to mine through its databases. Since deep learning algorithms are only as good as the data they rely on, adversaries can use backdoor attacks to leave the Army with untrustworthy AI or even bake-in the ability to kill an algorithm when it sees a particular image, or "trigger." "People tend to modify the input data very slightly so it is not so obvious to a human eye, but can fool the model," said Helen Li, a Duke University faculty member whose research team received $60,000 from the Army Research Office for work on an AI database defensive software.

Studies have shown that AI can outperform human doctors at identifying breast cancer from ... [ ] mammograms. Here, a clinician interprets a mammogram in a hospital in France. There has been increased hand-wringing across the AI community in recent months about the limitations of deep learning. It was a dominant theme a few months ago at NeurIPS, the world's premier AI conference. In December, deep learning pioneer Yoshua Bengio and AI researcher Gary Marcus engaged in a high-profile televised debate about whether deep learning was the right path forward for AI.