In the last week of the month alone, the Senate marched toward advancing the GOP's tax bill, the Dow topped 24,000, Bitcoin leaped above $11,000, President Trump retweeted anti-Muslim videos and invoked a rebuke from British Prime Minister Theresa May, Matt Lauer was fired after allegations of sexual misconduct, and North Korea tested another ICBM (a fact that is, as WIRED's own Lily Hay Newman puts it, even scarier than that sounds on its own). Oh, and the public continued to sound alarms about the impending end of net neutrality. But, in the WIRED realm, a small bit of wonderment prevailed. When Elon Musk and his electric big rig rumbled onto the scene on November 16, the news captured more eyeballs than you can shake a turkey leg at. As our own transportation team put it, the semitruck--powered by an enormous battery that runs up to 500 miles on a single charge--marks one more mile traveled on Musk's quest to unshackle humanity from its reliance on oil and gas, and drive us--autonomously, of course--into a cleaner future.

At the Latin Grammy Awards: 'Despacito' takes home 4 trophies; a performance in honor of'Dreamers' Painter Ellen Gallagher's tragic sea tales: How African slaves went from human to cargo on the Atlantic

Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning models often seek to optimize predictions by incorporating features that are not present in the databases readily available to providers and policy makers, limiting generalizability and implementation. Here we tested a number of machine learning classifiers for prediction of six-month mortality in a population of elderly Medicare beneficiaries, using an administrative claims database of the kind available to the majority of health care payers and providers. We show that machine learning classifiers substantially outperform current widely-used methods of risk prediction but only when used with an improved feature set incorporating insights from clinical medicine, developed for this study. Our work has applications to supporting patient and provider decision making at the end of life, as well as population health-oriented efforts to identify patients at high risk of poor outcomes.

A memo written by former Uber security analyst Ric Jacobs, which has become a primary piece of evidence in the company's legal battle with autonomous car development company Waymo, is expected to be made public Dec. 13. The 37-page letter from Jacobs was addressed to Uber's deputy general counsel Angela Padilla and details the company's "marketplace analytics" team that, according to Jacobs, existed for the sole purpose of stealing trade secrets from competitors. The revelation of the letter--which was raised by the United States Department of Justice after the agency collected the document earlier this year as part of its own criminal investigation into Uber--raised questions about whether Uber knowingly withheld evidence that was relevant to its dispute with the Google subsidiary company Waymo. The two firms have been locked in a dispute since February, when Waymo sued the ride hailing service for allegedly stealing proprietary information pertaining to Waymo's autonomous vehicle technology. The case revolves around former Waymo employee Anthony Levandowski who, prior to departing from the company to found his own that would eventually be acquired by Uber, downloaded 14,000 "highly confidential and proprietary design files" from Waymo servers.

I love it when I get feedback from a blog that I've written. I appreciate the different perspectives and insights that others bring to a topic of interest. And no blog that I've written has drawn more comments than my blog, "Isaac Asimov: The 4th Law of Robotics." The section of the blog that fueled the most comments stem from a scene in the movie I, Robot where Detective Spooner (played by Will Smith) is explaining to Doctor Calvin (who is responsible for giving robots human-like behaviors) why he distrusts and hates robots. He is describing an incident where his police car crashed into another car and both cars were thrown into a cold and deep river – certain death for all occupants.

Not long after Jefferson was put behind bars, another political corruption case blossomed, this time around Virginia Gov. Robert McDonnell and his wife, Maureen. The McDonnells reportedly accepted more than $175,000 in loans and gifts from a local businessman in return for his exposure to state officials and industry leaders. McDonnell and his wife were indicted and convicted after the governor left office in January 2014.

A considerable number of articles cover machine learning and its ability to protect us from cyberattacks. Still, it's important to separate the hype from the reality and see what exactly machine learning (ML), deep learning (DL) and artificial intelligence (AI) algorithms can do right now in cybersecurity. First of all, I have to disappoint you. Unfortunately, machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving. There will always be a person who tries to find issues in our systems and bypass them.

A small mystery from Robocar history was resolved recently, and revealed at the DARPA grand challenge reunion at CMU. The story is detailed here at IEEE spectrum and I won't repeat it all, but a brief summary goes like this. In the 2nd grand challenge, CMU's Highlander was a favourite and doing very well. Mid-race it started losing engine power and it stalled for long enough that Stanford's Stanley beat it by 11 minutes. It was discovered recently a small computerized fuel injector controller in the Hummer (one of only two) may have been damaged in a roll-over that Highlander had, and if you pressed on it, the engine would reduce power or fail.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. We're not at IREX in Japan this year, sadly, but we're starting to see some videos showing up from the show. Here's a nice long demo of Toyota's new T-HR3, showing how flexible it is.

Parliament has been warned that terrorists and rogue states will get their hands on killer robots in the next few years. Academics and senior scientists warned the'genie is out the bottle' when it comes to the use of artificial intelligence (AI) on the battlefield. And now AI experts also warned the House of Lords inquiry this week that terrorists are now looking to hijack self-driving cars to mow down innocent people in a copycat Westminster Bridge-style attack. Autonomous weapons which can pull the trigger without the use of human control are already being developed, experts warned. Alvin Wilby, of French defence firm Thales, said it is an'absolute certainty in the very near future' that rogue states will be able to get their hands on robot arms.