Even as the world's top artificial-intelligence researchers gathered in Los Angeles this week, many are beginning to wonder just how much longer the U.S. will remain the epicenter of AI. The Neural Information Processing System (NIPS) conference in Long Beach is the number one place for presenting breakthroughs in AI. But U.S. government policies threaten to put a dampener on the recent boom in the field. The U.S. Congress's tax plan is the latest challenge, threatening to raise costs for graduate students significantly. This follows reduced funding for fields including AI and tightening of rules on immigration for international researchers.

Trombino was a favorite of road builders who successfully helped push for Iowa's 10-cent-per-gallon gas tax increase in 2015. The Wisconsin native had spent most of his career at the Wisconsin DOT as an engineer and administrator. Iowa Gov. Terry Branstad picked him to run the Iowa DOT in 2011 and he was considered a visionary leader fluent in everything from self-driving cars to shipping policy. Trombino served as president of the American Association of State Highway and Transportation Officials for 2015-2016, a prestigious position in the field.

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, average treatment effects, average lifts, and demand or supply elasticities. In fact, estimates of such causal parameters obtained via naively plugging ML estimators into estimating equations for such parameters can behave very poorly due to the regularization bias. Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools. Specifically, we can form an orthogonal score for the target low-dimensional parameter by combining auxiliary and main ML predictions. The score is then used to build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal, and from which valid confidence intervals for these parameters of interest may be constructed. The resulting method thus could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models. In order to avoid overfitting, our construction also makes use of the K-fold sample splitting, which we call cross-fitting. This allows us to use a very broad set of ML predictive methods in solving the auxiliary and main prediction problems, such as random forest, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregators of these methods.

Nasa is holding a major press conference after its planet-hunting telescope made a new breakthrough. The Kepler space telescope is operated by Nasa to discover other earths, some of which could support life. And its latest discovery is significant enough to bring with it a huge press conference. Very little further information was given about the announcement, which will take place on Thursday. But it will almost certainly relate to exoplanets – Earth-sized worlds that orbit around their own stars, and are our best hope of finding alien life.

Nasa is preparing for a huge announcement from its planet-hunting telescope. It has said only that it will brief the press on Thursday and that the discovery has been made by the Kepler space telescope. It also said that Google has been involved in the breakthrough discovery. But beyond that it said very little. Still, some clues give us a little insight into what the major announcement might be about to actually reveal. The craft was launched into space in 2009, and has been looking for exoplanets ever since.

See how self-driving cars prepare for the real world inside a private testing facility owned by Google's autonomous car company, Waymo. The Navya passenger shuttle is among myriad autonomous vehicles worldwide in various stages of development. And at an event Nov. 17 and 18 on the University of Wisconsin Madison College of Engineering campus, visitors will have the opportunity to check it out. The Taiwan-based electronic manufacturer's plans to use driverless vehicles to move thousands of workers a day at its 22 million-square-foot campus about 30 miles south of Milwaukee could pave new ground for the technology, which promises to reshape transportation in this country. More than a dozen states are scrambling to get ready for self-driving cars, and while major companies from Google to General Motors are testing such cars, few are in use yet.

As people increasingly migrate to cities in search of jobs, services, and other urban benefits, local governments are turning to emerging technologies to respond to the pressures of their growing populations. Tech-savvy "smart cities" are reacting to heightened demands on scarce resources by developing new capabilities such as artificial intelligence (AI) and sensor-driven analytics to resolve myriad challenges, from crime to congestion. The new insights that result are helping city managers look at old problems in a new light, while cloud computing is making these efforts affordable and realistic. Analytics, for example, can help cities use existing resources more efficiently, according to Joel Cherkis, a group vice president at Oracle. "Traditionally, when crime rates go up, cities hire more police," he says.

What happens when you create an open AI marketplace that can learn from itself? Remember Sophia, the humanoid robot who was granted citizenship by Saudi Arabia? From the general media hubbub to the discussion surrounding the Saudi government's treatment of women's rights, the landmark event caused quite a stir across the web. But behind all that buzz, SingularityNET, whose artificial intelligence technology powers the robot built by Hanson Robotics, has been working to completely revolutionize web as we understand it. In some sense, the premise behind SingularityNET is fairly simple.

Tenured professors are being offered 3X their salaries to leave academia and join leading AI companies including Google. Demand for Ph.D-level expertise in AI continues to fuel academic recruiting efforts globally. It's becoming increasingly common for any of the leading AI companies to sponsor global conferences where the primary goal is to meet leading academicians, patent-producing researchers, and AI experts with Ph.D backgrounds. A recent New York Times article, Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent provides insights into how competitive it is to find AI expertise today including a glimpse into the salary bidding wars that go on for academicians with proven expertise in the field. CEOs of AI companies most skilled keeping innovation moving at quick, high-intensity pace that keeps top talent challenged while keeping the core business growing are getting the highest approval ratings.

Russia's next president could be an artificially intelligent robot that claims'enemies of the people will be shot'. Forty thousand Russians have nominated a piece of AI software on their phones to stand against Vladimir Putin for the 2018 Russian presidency. The AI assistant known as Alisa, similar to the Apple's voice-activated Siri, was created by Russian technology company Yandex. Russia's next president could be an artificially intelligent robot that claims'enemies of the people will be shot'. Since the AI's launch in September, Alisa has stirred controversy on social media, with users sharing a series of contentious statements from the software.