While the default in amateur diagnostics has become a quick Google search, increasingly innovators are looking to curb potential health misinformation pitfalls. New research published last week by JAMA found that Buoy Health's free chatbot helped decrease the rate of uncertainty among patients. The study also found that patients using the platform were more likely to decrease their intended level of care after using the technology. "We're excited to have our results published -- clearing patient confusion and reducing unnecessary ER/urgent care visits have big implications as healthcare costs continue to rise," Dr. Andrew Le, CEO and cofounder of Buoy Health told MobiHealthNews in an email. "That said, we will be working on follow-on studies, always focused on proving our outcomes and safety. We believe in peer-reviewed science and will continue to pursue transparency."

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.

President Recep Tayyip Erdogan of Turkey has become the Tripoli government's last major patron, providing armed drones, armored vehicles and, in the past week, Turkish troops. Turkish officials say their troops will act mostly in an advisory role and avoid front-line combat. But there are indications, from American officials and from videos posted on the internet, that Ankara has deployed Syrian irregulars to Libya, drawn from units that fought the Kurds in northeastern Syria last year. The increasingly prominent foreign role drew an angry rebuke from the United Nations envoy to Libya, Ghassan Salamé, who told reporters on Monday that "probably thousands" of foreign mercenaries had arrived in Libya to participate in the fight. The battle has displaced 300,000 people and caused over 2,200 deaths.

DUBAI, UNITED ARAB EMIRATES – Iran's launching of more than a dozen missiles at American-led forces in Iraq on Wednesday came after years of preparing for a confrontation with its superpower foe, whose forces are vastly larger and more advanced. The Persian Gulf country has more than 500,000 active-duty personnel, including 125,000 members of its elite Revolutionary Guard, according to a report last year by the International Institute for Strategic Studies. But international sanctions and restrictions on arms imports have made it hard for Iran to develop or buy more sophisticated weaponry. To compensate for the imbalance, Iran has developed "asymmetric" responses -- ballistic missiles, deadly drones and a web of militia allies in Iraq, Syria, Lebanon and Yemen, among other things -- with the aim of being able to inflict pain while avoiding the traditional battlefield. "From a conventional military perspective, they would get absolutely hammered," said a British former military commander who asked not to be identified because of the sensitivity of the matter.

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

X, MONTH YEAR 1 Coupled Tensor Completion via Low-rank Tensor Ring Huyan Huang, Yipeng Liu, Senior Member, IEEE, Ce Zhu, Fellow, IEEE Abstract --The coupled tensor decomposition aims to reveal the latent data structure which may share common factors. Using the recently proposed tensor ring decomposition, in this paper we propose a non-convex method by alternately optimizing the latent factors. We provide an excess risk bound for the proposed alternating minimization model, which shows the improvement in completion performance. The proposed algorithm is validated on synthetic data. Index T erms--tensor ring, coupled tensor completion, alternating least squares, excess risk bound, permutational Rademacher complexity I. I NTRODUCTION Tensor is a multidimensional array and able to model the interaction between different modes in high-dimensional data.

To ensure the security of the general mass, crime prevention is one of the most higher priorities for any government. An accurate crime prediction model can help the government, law enforcement to prevent violence, detect the criminals in advance, allocate the government resources, and recognize problems causing crimes. To construct any future-oriented tools, examine and understand the crime patterns in the earliest possible time is essential. In this paper, I analyzed a real-world crime and accident dataset of Denver county, USA, from January 2014 to May 2019, which containing 478,578 incidents. This project aims to predict and highlights the trends of occurrence that will, in return, support the law enforcement agencies and government to discover the preventive measures from the prediction rates. At first, I apply several statistical analysis supported by several data visualization approaches. Then, I implement various classification algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Extra Tree Classifier, Linear Discriminant Analysis, K-Neighbors Classifiers, and 4 Ensemble Models to classify 15 different classes of crimes. The outcomes are captured using two popular test methods: train-test split, and k-fold cross-validation. Moreover, to evaluate the performance flawlessly, I also utilize precision, recall, F1-score, Mean Squared Error (MSE), ROC curve, and paired-T-test. Except for the AdaBoost classifier, most of the algorithms exhibit satisfactory accuracy. Random Forest, Decision Tree, Ensemble Model 1, 3, and 4 even produce me more than 90% accuracy. Among all the approaches, Ensemble Model 4 presented superior results for every evaluation basis. This study could be useful to raise the awareness of peoples regarding the occurrence locations and to assist security agencies to predict future outbreaks of violence in a specific area within a particular time.

The visit to Syria, a key ally of Iran, came amid soaring tensions in the Middle East between Iran and the United States. On Friday, a U.S. drone strike killed a top Iranian general who led forces supporting Assad in Syria's civil war. Iran has vowed revenge for the slaying, and with some 600 American troops deployed in Syria, the country is a potential site of conflict with Iran. While the official statements made no mention of the U.S. killing of Iran's Revolutionary Guard Gen. Qassem Soleimani on Friday, the timing was conspicuous. Soleimani had led Iran's armed support for Assad as armed rebels neared the capital, Damascus, and he helped seize key cities, preventing the collapse of Assad's state.

At the Last Futurist, we enjoy looking at AI Trends and digital transformation trends. In between those two are more broad technology trends. In fact these topics make up the mission statement of this new news site. However the last decade had a lot of technology and gadgets that didn't fare so well in the real world. The decade was mobile all the way, with mass adoption taking place the way we might expect the brain-computer interface (BCI) to achieve mass adoption in a future decade years from now. In the decade ahead the move to automated stores and electric vehicles are real trends, but it's important to differentiate the hype from the reality. Autonomous vehicles, quantum computing going mainstream, better self-learning AI, hang on a second! Even mass adoption of digital currencies is coming faster. From computers to the internet and smart phones, a few generations shows a lot of progress. But technology never stands still. Advertising has scaled a world of surveillance capitalism normalization and an AI-arms race is now taking place. Most technology trends and AI listicles only touch the surface of how humans are embedding technology increasingly into their lives. However looking at it from the perspectives of many industries and across technology and innovation stacks gives a more complete picture. The real world and customer experience are the real tests for new technological innovations and pivots. It will take decades for 3D printing, quantum computing and an AGI to even become mature, but an age of biotechnology and AI in healthcare, education and finance is inevitable. From Huawei, to ByteDance (TikTok), to Didi, China will wage major battles for global market share in 5G, consumer apps, E-commerce, mobile payments and ride sharing, among others. Chinese led tech companies -- with the support of the Chinese Government and venture funds such as Softbank Vision Fund -- can mean that in the 2020s China's ecosystem fully replaces Silicon Valley as the leader of innovation. In 2019, some believe this has already occurred.