It could be the ultimate shopping companion, able to pick and pack goods at lightning speed. A German robotic'suckbot' arm has been crowned the winner in a prestigious warehouse robot contest run by Amazon. Sixteen teams competed in Amazon's Picking Challenge this year, where robots selected specific items from containers and placed them in a tote or on a shelf. Team Delft's robotic arm robotic'suckbot' arm has been crowned the winner in a prestigious warehouse robot contest run by Amazon. It uses suction cups to lift and move objects, allowing it to easily shop.

In recent times, robotics technology is widely recognized for delivering efficiency, reliability, low spoilage, and higher overall productivity. Owing to the benefits and also to realize its complete potential in the business domain, extensive research and development works are conducted to further improve the quality while obliterating various design and operational constraints in robots. Latest techniques and algorithms are making robots smaller, safer, more practical, and cost-effective to be used by organizations in multiple industries such as manufacturing, construction, and healthcare. Moreover, the convergence of robotics with a wide range of complementary technologies such as machine vision, force sensing, speech recognition, and advanced mechanics is offering increased levels of functionality. Well positioned to fulfill the demand for robots in different industries is the Denmark based firm, Blue Ocean Robotics that identifies the users and their generic challenges to develop robots as the solution.

Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms that govern stellar evolution, and tracing the dynamics of our Galaxy. The volume of data that is becoming available, however, brings with it the need to process this information accurately and rapidly. While existing methods can constrain fundamental stellar parameters such as ages, masses, and radii from these observations, they require substantial computational efforts to do so. We develop a method based on machine learning for rapidly estimating fundamental parameters of main-sequence solar-like stars from classical and asteroseismic observations. We first demonstrate this method on a hare-and-hound exercise and then apply it to the Sun, 16 Cyg A & B, and 34 planet-hosting candidates that have been observed by the Kepler spacecraft. We find that our estimates and their associated uncertainties are comparable to the results of other methods, but with the additional benefit of being able to explore many more stellar parameters while using much less computation time. We furthermore use this method to present evidence for an empirical diffusion-mass relation. Our method is open source and freely available for the community to use. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at https://github.com/earlbellinger/asteroseismology

We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.

Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the large scale data and predict in real-time dynamic fashion. In this paper, we study such a problem towards a smart city initiative, where a generic ride sharing system is conceived capable of making predictions about ride share opportunities based on the historically recorded data while satisfying real-time ride requests. Underpinning the system is an application of a powerful machine learning convex optimization framework called Network Lasso that uses the Alternate Direction Method of Multipliers (ADMM) optimization for learning and dynamic prediction. We propose an application of a robust and scalable unified optimization framework within the ride sharing case-study. The application of Network Lasso framework is capable of jointly optimizing and clustering different rides based on their spatial and model similarity. The prediction from the framework clusters new ride requests, making accurate price prediction based on the clusters, detecting hidden correlations in the data and allowing fast convergence due to the network topology. We provide an empirical evaluation of the application of ADMM network Lasso on real trip record and simulated data, proving their effectiveness since the mean squared error of the algorithm's prediction is minimized on the test rides.

Researchers at the University of Oslo's Robotics and Intelligent Systems (ROBIN) group are building 3D-printed self-learning robots.

Amazon is always on the lookout for new robotic technologies to improve efficiency in its warehouses, and this year deep learning appears to be leading the way. That's according to the results of the second annual Amazon Picking Challenge, which has been won by a joint team from the TU Delft Robotics Institute of the Netherlands and the company Delft Robotics. Amazon's 2016 event was held in conjunction with Robocup 2016 in Leipzig, Germany. Two parallel competitions took place: a Pick Task much like last year's, in which a mix of items has to be lifted from warehouse shelves and packed into a container; and a new "Stow Task," which involves taking items out of a tote and putting them onto the shelves. The Pick Task asked contestants to pick up and safely deposit 12 items from a mixed shelf into a container in the shortest possible time.

One million anonymised eye scans from Moorfields Eye Hospital will be used to train an artificial intelligence (AI) system from Google. Machine learning algorithms will scour the images for signs of diseases such as macular degeneration and diabetes-related sight loss. Moorfields is teaming up with Google's AI division DeepMind during the scheme. Previously, DeepMind faced criticism over a little-known data sharing agreement with three London hospitals. An agreement to share patient data from the Royal Free, Barnet and Chase Farm hospitals over the past five years and continuing until 2017 was revealed by the New Scientist in May. In that case, Google said it was analysing kidney data in the hope of developing an app for medical staff.

A scan of a human eye. SAN FRANCISCO -- Google plans to use more than one million anonymized eye scans to teach computers how to diagnose ocular disease. The Menlo Park, Calif.-based company has signed a deal with a British eye hospital to use artificial intelligence to learn from the medical records of 1.6 million patients in London hospitals. The goal is to teach a computer program to recognize the signs of two common types of eye disease, diabetic retinopathy and age-related macular degeneration. That's something humans are surprisingly imperfect at.

There are certain clues that will expose someone when they are lying right to your face – but how do you spot a liar in emails or the'About Me' section of a dating profile? Researchers have developed an algorithm that can spot a deceiver 70 percent of time just by analyzing their word use, structure and context. This computerized lie detector was designed by feeding it emails with both lies and truthful statements until it learned patterns linked to deception. Researchers have developed an algorithm that takes the guessing out by telling users when someone is lying through analyzing word use, structure and context. While comparing the truths and the lies in the sample emails, the City University of London discovered that those who are being deceitful less likely to use personal pronounces – such as'I', 'me', mine' – and will use more adjectives instead, reports The Telegraph.