The safety of troops in the very near future will rely on Artificial Intelligence-assisted tablets and small screens, networked to drones in the air that feed data back down to ground personnel equipped with information-rich HUD visors. "Robots are going to help humans in dangerous situations contain and control a region," said Dave Bossert, DARPA Program Manager and Senior Engineering Fellow at Raytheon. "Maintaining advantage, communicating, and understanding an area is as good as or better than being aggressive." In a recent interview Deputy Defense Secretary Bob Work expanded on the idea of how AI will power robots in hazardous situations. The networked soldier, Bossert said, will rely heavily on custom-built Android tablets and several wearable devices.

Rembrandt may have died in 1669 but his artistic legacy lives on thanks in part to a new piece of work created with computer data, instead of the artist's hands. Revealed in the Netherlands, 'The Next Rembrandt' is the result of an 18-month project undertaken by a group of art historians, along with software developers, scientists, engineers and data analysts – not the usual team of artists for portraits work. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech. The team worked tirelessly to anyalyze all known works of Rembrandt, which amounts to over 300 paintings, using high resolution 3D scans to capture every little detail and create an algorithm that would eventually be able to accurately recreate his style. That data was then fed to a 3D printer, which recreated the painting using 13 layers of paint-based UV ink.

The lighting in a theatrical production can often be as important as the sets and performances themselves. A new dance production, Pattern Recognition, will make use of artificial intelligence to drive the latest motion sensing technology to respond directly to the movements of two dancers on stage. UK choreographer Alexander Whitley and digital artist Memo Akten created Pattern Recognition using code and a system of moving lights and Microsoft Kinect sensors to intelligently change the pattern of lighting to match their movements. BBC Click's Jen Copestake went to find out more about the technology behind the show.

Writers fret about many things. In fact, agonizing over something, anything, is a writer's natural state, a condition that can feed the muse but also kill inspiration. Writers worry about writing, of course -- the quality and the quantity, the rhythm of the words and the structure of the piece. We worry about the publishing industry. And now we have to worry about automation.

A'new' Rembrandt painting has been revealed - 400 years after the artist's death. Researchers taught an AI to paint exactly like the Dutch painter by analysing 346 of Rembrandt's paintings. The work of art took almost 18 months to complete, consists of 148 billion pixels and was printed in 3D to allow to computer to even replicate the brush strokes. The painting'The Next Rembrandt' is being unveiled at Galerie Looiersgracht60 in Amsterdam, The Netherlands, 05 April 2016. The painting was created by scientists and technicians based on the historical data of all 346 paintings by Rembrandt.

Here's some quick background for readers who are not familiar with lotteries. In the games I studied, the lottery draws 5 or 6 distinct numbers from a set of about 40 integers, and the order in which the numbers are drawn has no effect on prize amounts. For example, New Jersey Cash 5 draws 5 numbers from 1 to 43. The set from which the numbers are selected is called a "matrix" (not to be confused with the mathematical object with the same name). The Cash 5 games have several hundred thousand possible outcomes, and the Oregon game has about 12 million outcomes.

Population migration is valuable information which leads to proper decision in urban-planning strategy, massive investment, and many other fields. For instance, inter-city migration is a posterior evidence to see if the government's constrain of population works, and inter-community immigration might be a prior evidence of real estate price hike. With timely data, it is also impossible to compare which city is more favorable for the people, suppose the cities release different new regulations, we could also compare the customers of different real estate development groups, where they come from, where they probably will go. Unfortunately these data was not available. In this paper, leveraging the data generated by positioning team in Didi, we propose a novel approach that timely monitoring population migration from community scale to provincial scale. Migration can be detected as soon as in a week. It could be faster, the setting of a week is for statistical purpose. A monitoring system is developed, then applied nation wide in China, some observations derived from the system will be presented in this paper. This new method of migration perception is origin from the insight that nowadays people mostly moving with their personal Access Point (AP), also known as WiFi hotspot. Assume that the ratio of AP moving to the migration of population is constant, analysis of comparative population migration would be feasible. More exact quantitative research would also be done with few sample research and model regression. The procedures of processing data includes many steps: eliminating the impact of pseudo-migration AP, for instance pocket WiFi, and second-hand traded router; distinguishing moving of population with moving of companies; identifying shifting of AP by the finger print clusters, etc..

Methods from convex optimization are widely used as building blocks for deep learning algorithms. However, the reasons for their empirical success are unclear, since modern convolutional networks (convnets), incorporating rectifier units and max-pooling, are neither smooth nor convex. Standard guarantees therefore do not apply. This paper provides the first convergence rates for gradient descent on rectifier convnets. The proof utilizes the particular structure of rectifier networks which consists in binary active/inactive gates applied on top of an underlying linear network. The approach generalizes to max-pooling, dropout and maxout. In other words, to precisely the neural networks that perform best empirically. The key step is to introduce gated games, an extension of convex games with similar convergence properties that capture the gating function of rectifiers. The main result is that rectifier convnets converge to a critical point at a rate controlled by the gated-regret of the units in the network. Corollaries of the main result include: (i) a game-theoretic description of the representations learned by a neural network; (ii) a logarithmic-regret algorithm for training neural nets; and (iii) a formal setting for analyzing conditional computation in neural nets that can be applied to recently developed models of attention.

As another example, to achieve high accuracy in recommender systems [45], [60], we need to fully understand the content of items (e.g., documents and movies), analyze the profile and preference of users, and evaluate the similarity among users. Deep learning is good at the first subtask while PGM excels at the other two. Besides the fact that better understanding of item content would help with the analysis of user profiles, the estimated similarity among users could provide valuable information for understanding item content in return. In order to fully utilize this bidirectional effect to boost recommendation accuracy, we might wish to unify deep learning and PGM in one single principled probabilistic framework, as done in [60]. Besides recommender systems, the need for Bayesian deep learning may also arise when we are dealing with control of nonlinear dynamical systems with raw images as input. Consider controlling a complex dynamical system according to the live video stream received from a camera. This problem can be transformed into iteratively performing two tasks, perception from raw images and control based on dynamic models. The perception task can be taken care of using multiple layers of simple nonlinear transformation (deep learning) while the control task usually needs more sophisticated models like hidden Markov models and Kalman filters [21], [38]. The feedback loop is then completed by the fact that actions chosen by the control model can affect the received video stream in return.

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.