To simultaneously capture syntax and global semantics from a text corpus, we propose a new larger-context recurrent neural network (RNN) based language model, which extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation. Moving beyond a conventional RNN based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences. For inference, we develop a hybrid of stochastic-gradient MCMC and recurrent autoencoding variational Bayes. Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms state-of-the-art larger-context RNN-based language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.

Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data. However, robustness of graph neural networks is not yet well-understood. In this work, we focus on node structural identity predictions, where a representative GNN model is able to achieve near-perfect accuracy. We also show that the same GNN model is not robust to addition of structural noise, through a controlled dataset and set of experiments. Finally, we show that under the right conditions, graph-augmented training is capable of significantly improving robustness to structural noise.

President Trump and Defense Secretary Mark Esper visit the Army-Navy locker rooms to deliver words of encouragement before the 120th Army-Navy football game in Philadelphia. A probe into hand gestures flashed by West Point cadets and Naval Academy midshipmen at last weekend's televised Army-Navy college football in Philidelphia game were not racist, separate military investigations conducted by the military academies found. Clips of the "OK" hand gestures by the service-academy students during a Dec. 14, ESPN College GameDay broadcast game went viral and raised concerns over whether the signs were associated with white nationalism. The gesture, which features the thumb and forefinger that touch in a circle with the other fingers outstretched, has been appropriated as a signal for white supremacy in recent years. The Naval Academy found that two of its midshipmen were participating in a "sophomoric game" and had no racist intent behind the hand signs.

A U.S. government study released this week found that 189 facial recognition algorithms from 99 developers "falsely identified African-American and Asian faces 10 to 100 times more often than Caucasian faces." This should be the last such study. We are long overdue for federal governments to regulate or outright ban facial recognition. This year, the NYPD ran a picture of actor Woody Harrelson through a facial recognition system because officers thought the suspect seen in drug store camera footage resembled the actor. This year China used facial recognition to track its Uighur Muslim population.

The city of Chicago is using algorithms to try to prevent crimes before they happen. In Pittsburgh, traffic lights that use artificial intelligence (AI) have helped cut traffic times by 25 percent and idling times by 40 percent.1 Meanwhile, the European Union's real-time early detection and alert system (RED) employs AI to counter terrorism, using natural language processing (NLP) to monitor and analyze social media conversations.2 Such examples illustrate how AI can improve government services. As it continues to be enhanced and deployed, AI can truly transform this arena, generating new insights and predictions, increasing speed and productivity, and creating entirely new approaches to citizen interactions. AI in all its forms can generate powerful new abilities in areas as diverse as national security, food safety, regulation, and health care. But to fully realize these benefits, leaders must look at AI strategically and holistically. Many government organizations have only begun planning how to incorporate AI into their missions and technology. The decisions they make in the next three years could determine their success or failure well into the next decade, as AI technologies continue to evolve. It will be a challenging period.

Time is of the essence when it comes to treating cancer, the second leading cause of death in the U.S. according to the Centers for Disease Control and Prevention. Between diagnosis and the first day of treatment, days and even weeks may tick by as doctors convene to discuss treatment plans and order testing to gather as much information as possible. But as a new decade dawns, artificial intelligence may buy more time for those who need it most. Both President Donald Trump and former Vice President Joe Biden have promised to prioritize curing cancer should they win the 2020 election. But because of its complex biology, cancer has been historically difficult to cure with a pill or injection. As new treatments like immunotherapy undergo further research, health systems are starting to harness data-sharing and artificial intelligence to better predict a patient's prognosis, and determine the most effective treatment plan for their cancer based on other patients with similar medical histories.

"The main thing is to keep the main thing the main thing." These wise words of world-renowned business author Stephen Covey challenge each of us as we stand on the precipice of a new decade. But what's the'main thing' when navigating technology as we enter 2020? The simple answer is… Cybersecurity. As innovation explodes into every area of our lives, cybersecurity is providing the glue that can enable the good and disable the bad for implementing cutting-edge innovation as well as reducing risk from older vulnerabilities. We also see cybersecurity continue as the top priority for chief information officers (CIOs) in 2020, just as it has been for most of the past decade, with groups like the National Association of State CIOs (NASCIO). But even as cybersecurity solutions offer a way forward to ensure privacy protections are workable and effective, most people see the data breaches, ransomware, identity theft, denial-of-service attacks and other cyberattacks as proof that cybersecurity has become the Achilles Heel, not the savior, for new innovation. Even as exciting advances occur in artificial intelligence (AI), autonomous vehicles, 5G networks cloud computing, mobile devices and the Internet of Things (IoT), these same developments seem to cause negative societal disruptions that make daily news headlines. So what will happen next with cybersecurity? That's what this annual security prediction roundup will cover, from the perspective of the top cybersecurity industry companies, thought leaders, executives and journalists. Every year we catalogue the evaluators to see who has made a New Year's security prediction list and checked it twice.

A New York City based large volume private practice radiology group conducted a quality assurance review that included an 18 month software evaluation in the breast center comprised of nine (9) specialist radiologists using an FDA cleared artificial intelligence software by Koios Medical, Inc as a second opinion for analyzing and assessing lesions found during breast ultrasound examinations. Over the evaluation period, radiologists analyzed over 6,000 diagnostic breast ultrasound exams. Radiologists used Koios DS Breast decision support software (Koios Medical, Inc.) to assist in lesion classification and risk assessment. As part of the normal diagnostic workflow, radiologists would activate Koios DS and review the software findings with clinical details to formulate the best management. Analysis was then performed comparing the physicians' diagnostic performance to the 18-month period prior to the introduction of the AI enabled software.

Civil liberties experts, however, warn that the technology -- which can be used to track people at a distance without their knowledge -- has the potential to lead to ubiquitous surveillance, chilling freedom of movement and speech. This year, San Francisco, Oakland and Berkeley in California and the Massachusetts communities Somerville and Brookline banned government use of the technology. "One false match can lead to missed flights, lengthy interrogations, watch list placements, tense police encounters, false arrests or worse," Jay Stanley, a policy analyst at the American Civil Liberties Union, said in a statement. "Government agencies including the F.B.I., Customs and Border Protection and local law enforcement must immediately halt the deployment of this dystopian technology." The federal report is one of the largest studies of its kind.

Samuel Holmes Doten of Plymouth, Mass., was born June 5, 1812, so after the Civil War ended in 1865, he would joke that he "served in the infantry in the war of that date." William Kendall Crossfield, a Peterborough, N.H. native, was having a rest during the battle of Fredericksburg when he was shot in the neck while turning over. The blanket he had pulled up to his chin miraculously cushioned the bullet, but he passed out from the shock of the blow. Vermonter Almeron C. Inman was recommended for the Medal of Honor of Feb. 9, 1887, "for intelligent coolness and bravery" in two 1864 engagements. After going missing for three months in 1895, he was found dead, thought to have killed himself.