Toyota is close to acquiring two robotics companies from Google, according to a report in Wednesday's Nikkei newspaper. The newspaper said the two companies are in final talks, but details are yet to be discussed so the deal could still fall through. The deal would see Boston Dynamics, a developer of advanced two- and four-legged robots for the U.S. military, and Schaft, a Tokyo-based developer of humanoid robots, transferred to the Toyota Research Institute. The unit is a recently launched billion-dollar research arm based in Silicon Valley. Google acquired Boston Dynamics and Schaft several years ago as part of a push into robotics, but that hasn't gone anywhere since robotics head Andy Rubin left the company in late 2014.

The U.S. military's plans to build a satellite-launching robotic space plane are moving forward. On May 23, the Defense Advanced Research Projects Agency (DARPA) put out its official call for proposals for the futuristic space plane design. The goal of the Experimental Spaceplane (XS-1) project is to build a reusable space plane that, at optimal operation, should be able to fly 10 times in 10 days, at a cost of no more than 5 million per flight. Typically, space vehicles are not fully reusable, and the pieces that are reused need to undergo time-consuming safety checks between flights. XS-1 would be used primarily as a cheap and fast way to deliver satellites to orbit, DARPA officials said.

A rough estimate would show that thousands of Chinese robotics companies may have recorded, on average, annual sales of less than 100,000 yuan each. Mainland media reported that low-end robots have been installed on the assembly line of known labour-intensive factories as a showcase for local government subsidies and left unused. "There remains a big shortage of specialists who can assess the true situation in the country's robotics industry and determine arbitrary decision–making by local governments in granting subsidies," Wang said. His urgent call for a sweeping audit of subsidies to the domestic robotics industry followed a recent nationwide investigation launched by the Ministry of Finance on so-called "new energy vehicle" makers. The agency is targeting electric car manufacturers suspected of fraudulently obtaining subsidies from the government in 2013 and 2014, as well as those firms that applied last year.

The Netherlands is not the only country concerned about drones near airports. Last month, the European Aviation Safety Agency said it is setting up a task force to examine the risk of collisions between drones and aircraft.

Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that comes together in real time to make predictions, said Louis Rosenberg, its creator.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University researchers could provide important insights to this process. Was it a person's age, gender or education level that had the most influence on a decision? Was it a particular combination of factors? CMU's Quantitative Input Influence (QII) measures can provide the relative weight of each factor in the final decision, said Anupam Datta, associate professor of computer science and electrical and computer engineering.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include, applicability to a wide range of diseases, and ability to capture disease dynamics - including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China and India. We noted that temporal topic trends extracted from disease-related news reports successfully captured the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Donald Trump can buy himself many things, but he will never be able to officially build a 10,000-block-high statue of himself in Minecraft. And no, don't expect to mine Moria as part of a Lord of the Rings server. Microsoft and its Mojang subsidiary said Tuesday that they will begin blocking corporations and politicians from using Minecraft to promote their own agendas, including the sale of products, movies, or political views. "We want to empower our community to make money from their creativity, but we're not happy when the selling of an unrelated product becomes the purpose of a Minecraft mod or server," Mojang wrote in a blog post. The new additions are now part of Mojang's Commercial Usage Guidelines.

Cooney goes on, "With Fun LoL DARPA is looking for information about mathematical frameworks, architectures, and methods that would help answer questions such as: What are the number of examples necessary for training to achieve a given accuracy performance? What are important trade-offs and their implications? How close is the expected achievable performance of a learning algorithm compared to what can be achieved at the limit? What are the effects of noise and error in the training data? What are the potential gains possible due to the statistical structure of the model generating the data?"