The robots feature technology that senses where the person is and will stay close by for assistance. This also allows them to make sure they don't maneuver in such a way that could injure the person. In addition to heavy lifting, Ford notes the robots can even be programmed to make coffee as well as other delicate tasks. Ford also explains that these robots can lead to safer, faster and higher quality vehicle assembly, as well as making the process easier for employees. Although the robots are only in use at the Cologne factory, they may end up in other locations in the near future.

That's not much of a surprise, since the online message board is all about the ins and outs of erotic dolls, as in the kind men have sex with. Some regulars use the site to trade tips on gel butt implants. Others complain about the pubic hair of one doll or the breasts of another. Nukeno, however, uses it to tell the crowd what makes him happy: Nele and Kiko, his two dolls. "Perhaps I have been alone for too long," writes the self-described 34-year-old from Germany.

Helen of Troy may have had the face that launched a thousand ships, but even the best facial recognition algorithms may have had trouble finding her face in a crowd of one million strangers. The first benchmark test based on one million faces has shown how facial recognition algorithms from Google and other research groups around the world can still fall short in accurately identifying and verifying faces. Facial recognition algorithms that had previously performed with more than 95 percent accuracy on a popular benchmark test involving 13,000 faces saw significant drops in accuracy when faced with the new MegaFace Challenge involving one million faces. The best performer on one test, Google's FaceNet algorithm, dropped from near-perfect accuracy on five-figure datasets to 75 percent on the million-face test. Other top algorithms dropped from above 90-percent accuracy on the small datasets to below 60 percent on the MegaFace Challenge.

Object queries are essential in information seeking and decision making in vast areas of applications. However, a query may involve complex conditions on objects and sets, which can be arbitrarily nested and aliased. The objects and sets involved as well as the demand---the given parameter values of interest---can change arbitrarily. How to implement object queries efficiently under all possible updates, and furthermore to provide complexity guarantees? This paper describes an automatic method. The method allows powerful queries to be written completely declaratively. It transforms demand as well as all objects and sets into relations. Most importantly, it defines invariants for not only the query results, but also all auxiliary values about the objects and sets involved, including those for propagating demand, and incrementally maintains all of them. Implementation and experiments with problems from a variety of application areas, including distributed algorithms and probabilistic queries, confirm the analyzed complexities, trade-offs, and significant improvements over prior work.

Functional Magnetic Resonance Imaging (fMRI) is a noninvasive technique for studying brain activity, which receives an increasing attention in the last decade or so. During an fMRI experiment, a series of brain images is acquired, while the subject possibly performs a set of tasks responding to external stimuli. Changes in the measured blood-oxygen-level dependent (BOLD) signal are used to examine different types of activation in the brain. There are several objectives in the analysis of fMRI data, the most common of which are the localization of regions of the brain, that are activated by a task, and the determination of the functional brain connectivity [1, 2]. The localization of the activated areas in the human brain is a challenging "cocktail party" problem, where several people are talking (areas activated) simultaneously behind a wall (skull). Our goal is to distinguish those areas (spatial maps) as well as activation patterns (time courses) through some blind source separation (decomposition) method [3, 4]. Each source is the outcome of a combination of a time course with a spatial map. In fMRI studies of the brain function, the structure of the data involves multiple modes, such as trial, task condition, subject, in addition to the intrinsic dimensions of time and space [5].

We formalize Simplified Boardgames language, which describes a subclass of arbitrary board games. The language structure is based on the regular expressions, which makes the rules easily machine-processable while keeping the rules concise and fairly human-readable.

It was a worrying refusal that does not bode well for the future of humanity. In New York to help Mastercard launch its rebrand and a new mobile payment service, the machine answered several questions - but refused to reveal its ultimate ambitions, simply flashing its eyes. 'I was named Pepper as I'm here to spice up your life, and my nickname is Pepperoni,' the robot then told us. Pepper also revealed it knows the three laws of robots, which include not harming humans, adding'I think robots should love humans.' However, it also refused to answer whether is was looking to take our reporter's job, simply waving and saying goodbye at that point, cutting the interview short. Betty DeVita of Mastercard reveal the Pepper unit normally works in a Pizza restaurant.

FILE - In this Sept. 15, 2015, file photo, a Tesla Model S is on display on the first press day of the Frankfurt Auto Show IAA in Frankfurt, Germany. Consumer Reports magazine is calling on electric car maker Tesla Motors to change the name of its Autopilot semi-autonomous driving system and to disconnect the automatic steering feature after a fatal crash in Florida. The magazine says in a statement that calling the system Autopilot promotes a dangerous assumption that Teslas can drive themselves. FILE - In this Sept. 15, 2015, file photo, a Tesla Model S is on display on the first press day of the Frankfurt Auto Show IAA in Frankfurt, Germany. Consumer Reports magazine is calling on electric car maker Tesla Motors to change the name of its Autopilot semi-autonomous driving system and to disconnect the automatic steering feature after a fatal crash in Florida. The magazine says in a statement that calling the system Autopilot promotes a dangerous assumption that Teslas can drive themselves.

A new study suggests that common settings used in software for analyzing brain scans may lead to false positive results. Researchers led by Anders Eklund, an electrical engineer at Linköping University in Sweden, analyzed functional magnetic resonance imaging (fMRI) data from several public databases. Certain software settings, the team found, could give rise to a false positive result up to 70% of the time. In the context of a typical fMRI experiment, that could lead researchers to wrongly conclude that activity in a certain area of the brain plays a role in a cognitive function such as perception or memory.

The hot new trend in self-learning algorithms--a technology that's embedded in our phones, our social networks, and more--is trying to figure out how the hell it works. The thing is that the algorithms known as neural networks are essentially black boxes. We've developed the high-level concepts that govern them and designed the networks themselves, but picking apart decisions that they make on their own is intensely difficult due to their internal complexity. As impressive as these systems are, however, they're not perfect, and to make them better we need to understand what makes them tick. The latest attempt at tearing the top off of a computational black box was published to the ArXiv preprint server this week by researchers at the Queen Mary University of London in the UK. They took a peek inside how a neural network understands music genres.