A drone footage of the Niger ambush that killed four U.S. and five Nigerian soldiers that surfaced recently shows the service personnel desperately trying to escape and fighting for their lives after friendly Nigerien forces mistook them for the enemy. The video shows the harrowing hours of troops holding off their enemy and waiting for rescue. It shows how the soldiers set up a defensive location on the edge of a marsh and wrote letters to their loved ones thinking they were going to die. Pentagon released the video with explanatory narration and it contains more than 10 minutes of drone footage, animation and file tape that was not made public last week when the military released a portion of the final report on the October attack, the Guardian reported. In a failed attempt to target a local ISIS leader, 46 U.S. and Nigerien troops were involved in the initial mission in the West African nation.

As Hurricane Harvey ripped through Texas and neighboring gulf states in August 2017, leaving a record-breaking 30 million gallons of quickly-dirtied water in its wake, the Federal Emergency Management Agency, more commonly known as FEMA, moved into position. Among the personnel from federal agencies as varied as the Department of Health and Human Services and the Coast Guard were numerous Urban Search and Rescue teams--experts in finding people in the midst of a large-scale crisis, whether they're stranded on a roof, or trapped deep beneath the rubble. They're equipped with listening devices, heat detection equipment, and, most importantly, some loyal sniffers. "We use the dogs [as] locating tools," says Scott Mateyaschuk of the New York Police Department's K9 unit. "The dogs will locate live human scent under structural collapse."

Google announced a new AI-powered set of products and services at its I/O conference for developers, including one called Duplex that makes phone calls for you and sounds just like a real person, which freaked everyone right out. The Trump administration held some sort of AI summit with representatives from Amazon, Facebook, Microsoft, Nvidia, and buttermaker Land O'Lakes, presumably because the White House has so much churn. Add to this the public reveal that the musician Grimes and Elon Musk are dating, after the two shared a joke about AI. And yet when people ask what the software company I run is doing with machine learning, I say, calmly, "Nothing." Because at some level there's just nothing to do.

According to a press statement [insert link] published by the university, the four-year project will draw on a fleet of autonomous vehicles of sizes ranging from four to 12 places, and aims gather data reflecting the economic, logistic, and social implications of a self-driving network. Run in collaboration with local authorities and the Geneva public transport services (TPG), the trial hopes to collect information around three major axes: "autonomous driving" (the security and adaptability of vehicles), "optimization of itineraries" (the user experience), and "in- and out-of-vehicle services" (catering to reduced mobility passengers, for example). It will be conducted in suburban areas less frequently or conveniently serviced by existing routes. "Autonomous vehicles will not go downtown," said Denis Berdoz, CEO of TPG. Geneva, Switzerland's second-largest city, is particularly suited to such a trial because of the complex traffic situations it offers (a combination of jams, pedestrianized zones and bicycle lanes), as well as the well-mapped and GPS-ready nature of the region. The project has a budget of some €22 million (CHF26.28 million), €16 million of which are funded by the European Union's Horizon 2020 research financing mechanism. The following content is sourced from external partners. We cannot guarantee that it is suitable for the visually or hearing impaired.

Machine learning is critical to the future of cybersecurity and helping security teams overcome the challenges of modern cybersecurity attacks. Indeed, its ability to'outthink' humans can boost return on investment (ROI), drastically improve productivity and minimise resource expenditure. However, machine learning is also not just a'set and forget' solution. In fact, companies need to treat machine like an intern on their first day. Security teams should not assume a machine learning programme can hit the ground running – there needs to be an onboarding process where you check in on the models frequently and spend time getting them started in the right direction.

With promises of crunching mounds of data into bite-sized nuggets of actionable information, machine learning could be a breakthrough for the intelligence community. However, vulnerabilities within such systems could open them up to cyber attacks. Jason Matheny, director of the Intelligence Advanced Research Projects Activity, said his organization funds research at over 500 universities, colleges, businesses and labs. A third of his portfolio focuses on machine learning, speech recognition and video analytics. "For us, machine learning is an approach to dealing with this deluge of data that the intelligence community is confronted with," he said during a panel discussion at a Defense One event focusing on artificial intelligence.

Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given baseline covariates) targets a nonparametrically nonidentifiable parameter. Consistent estimation of propensity score functions is an important component of various causal inference estimators, including double robust machine learning and inverse probability weighted estimators. We propose the use of the probability of exposure from the source population in observation-weighted stacking algorithms to produce consistent estimators of propensity score functions. Simulation studies and a hypothetical health policy intervention data analysis demonstrate low empirical bias and variance for these stacked propensity score functions with observation weights.

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naive Bayes (MNB) and a support vector machine (SVM). All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score. We measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses, the LSTM classifier performs best, though all models exhibit an F1 score above 0.96. GRU performs best on chief complaints (F1=0.4859) and MNB with bigrams performs worst (F1=0.3998). Certain syndrome types are easier to detect than others. For examples, the GRU predicts alcohol-related disorders well (F1=0.8084) but predicts influenza poorly (F1=0.1363). In all instances the RNN models outperformed the bag-of-word classifiers, suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.

Much of the attention has focused on Theranos founder Elizabeth Holmes. But another character played a central role behind the scenes in the alleged fraud: Ms. Holmes's boyfriend, Ramesh "Sunny" Balwani, according to more than three dozen former Theranos employees who interacted with Mr. Balwani extensively over a number of years. Mr. Balwani, who met Ms. Holmes when she was a teenager, jointly ran the company with her for seven years as president and chief operating officer and enforced a corporate culture of secrecy and fear until his departure in the spring of 2016, the former employees say. Unlike Ms. Holmes and Theranos, who reached a settlement with the SEC to resolve the agency's civil charges in March without admitting or denying wrongdoing, Mr. Balwani has denied separate charges the SEC filed against him in a parallel action and is fighting them in a California federal court. A spokeswoman for Mr. Balwani provided a statement from his lawyer, Jeffrey B. Coopersmith, saying Mr. Balwani accurately represented Theranos to investors to the best of his ability, worked hard to maximize shareholder value and took on significant risk investing in the company while never benefiting financially from his work.

If you're reading the tech news these days it may seems that the industry is rapidly moving toward a robot-centric dystopia lifted from a Stephen King novel. There is a reason it's called "science fiction." Not that AI, machines, and robots don't have a place in our future. Practically speaking, we are a far cry from an autonomous office or completely driverless trucking. Popular questions such as "will artificial intelligence be a massive, job-destroying shock to our economy?"