In recent years, artificial intelligence algorithms have become very good at recommending content to users -- a bit too good, you might say. Tech companies use AI to optimize their recommendations based on how users react to content. This is good for the companies serving content, since it results in users spending more time on their applications and generating more revenue. But what's good for companies is not necessarily good for the users. Often, what we want to see is not necessarily what we should see.

AI researchers have demonstrated a self-teaching algorithm that gives a robot hand remarkable new dexterity. Their creation taught itself to manipulate a cube with uncanny skill by practicing for the equivalent of a hundred years inside a computer simulation (though only a few days in real time). The robotic hand is still nowhere near as agile as a human one, and far too clumsy to be deployed in a factory or a warehouse. Even so, the research shows the potential for machine learning to unlock new robotic capabilities. It also suggests that someday robots might teach themselves new skills inside virtual worlds, which could greatly speed up the process of programming or training them.

Josh Sullivan, the senior vice president at government consulting firm Booz Allen Hamilton, told The Washington Post that the AI systems would do basic surveillance, object identification and other mundane activities while allowing soldiers and officers to perform higher-level tasks. "Part of this is (about) making sure our government has the access to the best technology and using it responsibly in service of our citizens and warfighters," Sullivan said as quoted by the media outlet. READ MORE: US Congress Sees Russia as'Competitor', Aims to Prolong Ban on Military Ties Artificial intelligence is becoming an increasingly important technology in warfare and national security across the world. In particular, China is reportedly developing unmanned AI submarines expected to be put into service in strategic waters in the early 2020s, while Russia is actively funding research of drones and robotics technologies.

Most consumers would be dismayed with how little we know about the majority of chemicals. Only 3 percent of industrial chemicals – mostly drugs and pesticides – are comprehensively tested. Most of the 80,000 to 140,000 chemicals in consumer products have not been tested at all or just examined superficially to see what harm they may do locally, at the site of contact and at extremely high doses. I am a physician and former head of the European Center for the Validation of Alternative Methods of the European Commission (2002-2008), and I am dedicated to finding faster, cheaper and more accurate methods of testing the safety of chemicals. To that end, I now lead a new program at Johns Hopkins University to revamp the safety sciences.

This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method.

This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot. It presents two techniques, sequence to sequence learning and Multi-Task Learning, which might improve on the previous task.

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.

Deep neural networks are workhorse models in machine learning with multiple layers of non-linear functions composed in series. Their loss function is highly non-convex, yet empirically even gradient descent minimisation is sufficient to arrive at accurate and predictive models. It is hitherto unknown why are deep neural networks easily optimizable. We analyze the energy landscape of a spin glass model of deep neural networks using random matrix theory and algebraic geometry. We analytically show that the multilayered structure holds the key to optimizability: Fixing the number of parameters and increasing network depth, the number of stationary points in the loss function decreases, minima become more clustered in parameter space, and the tradeoff between the depth and width of minima becomes less severe. Our analytical results are numerically verified through comparison with neural networks trained on a set of classical benchmark datasets. Our model uncovers generic design principles of machine learning models.

For the published version of the article, see: Heck, D. W., Moshagen, M., & Erdfelder, E. (2014). Correspondence concerning this article should be addressed to Daniel W. Heck, Department of Psychology, School of Social Sciences, University of Mannheim, Schloss EO 254, D-68131 Mannheim, Germany. FISHER INFORMATION APPROXIMATION 2 Abstract The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it has the advantage of taking the functional form of a model into account. Unfortunately, FIA can be misleading in finite samples, resulting in an inversion of the correct rank order of complexity terms for competing models in the worst case.

This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in emerging and future wireless networks. Various techniques have been proposed in the literature to find near-optimal power allocations, often by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a model-free distributed dynamic power allocation scheme is developed based on deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling (with weights that are changing over time). Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. This work indicates that deep reinforcement learning based radio resource management can be very fast and deliver highly competitive performance, especially in practical scenarios where the system model is inaccurate and CSI delay is non-negligible.