Hong Kong researchers have successfully developed a 3D-printed nanoscale robot that can maneuver at a cellular level. In Jun, Science Robotics, a leading robotics technical journal, published a report about the exploits of the City University engineers. It shows that targeted, personalized medicine with tiny robots is no longer science fiction . Precision medicine, as a field, has grown tremendously with the arrival of gene-editing strategies and more-affordable DNA sequencing. WASHINGTON, DC - FEBRUARY 25: U.S. Secretary of Veterans Affairs Robert A. McDonald speaks during the White House Precision Medicine Initiative Summit, in the South Court Auditorium in the Eisenhower Executive Office Building, February 25, 2016 in Washington, DC.

Note: This article will likely be revised and expanded before being submitted for review and publication. At the moment it is missing critical sections, that will be added later. If we have suggestions for improvement, please send them to me directly. In this article I will discuss the well-known technique of feature hashing, but with the modification of performing the hashing step on the client-side before sending data to a server or daemon performing model training and prediction. By using this approach, we can ensure that the system performing the training cannot have any knowledge of the underlying data being received, since the learning takes place only using the hashed representation of the data.

Artificial general intelligence (AGI) can be defined as artificial intelligence (AI) that matches or surpasses human intelligence. It is, in brief, the type of intelligence through which a machine is able to perform any intellectual task that a human being can. And, it is currently one of the main objectives of AI research. The concepts of AGI and consciousness, however, lack definitions that satisfy everyone. The type of artificial AI currently available is focused on specific tasks and is therefore referred to as "applied" or "narrow" AI because of the machines' limited intelligence.

From steam engines to computers, new technologies that emerged from past industrial revolutions stimulated new demand, boosted economic growth and eventually created more jobs than they destroyed. However, every revolution has its winners and losers. Some feel that the idea that AI will take over human jobs is unduly pessimistic. Given that Hong Kong's labour market remains tight, the impact of AI on job displacement appears not to have been felt yet. However, with many people today still trapped in the struggle for survival, there is fear that AI will worsen their lives, not improve it.

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.

Artificial intelligence has become a sort of secret weapon in the battle to build the best cloud service platform. Google Cloud Platform is currently the underdog, trailing both Amazon Web Services and Microsoft Azure. But Google is betting robust AI will give it the edge it needs to catch up. At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products. Earlier this week, Google parent company Alphabet reported its Q2 earnings, which were ahead of Wall Street's expectations.

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.

Completing a data matrix X has become an ubiquitous problem in modern data science, with applications in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is low-rank. A more general model assumes that each column of X corresponds to one of several low-rank matrices. This paper generalizes these models to what we call mixture matrix completion (MMC): the case where each entry of X corresponds to one of several low-rank matrices. MMC is a more accurate model for recommender systems, and brings more flexibility to other completion and clustering problems. We make four fundamental contributions about this new model. First, we show that MMC is theoretically possible (well-posed). Second, we give its precise information-theoretic identifiability conditions. Third, we derive the sample complexity of MMC. Finally, we give a practical algorithm for MMC with performance comparable to the state-of-the-art for simpler related problems, both on synthetic and real data.

With only a subset of its entries available, matrix completion (MC) amounts to recovering the unavailable entries by leveraging just the low-rank attribute of the matrix itself [1]. The relevant task arises in applications as diverse as image restoration [2], sensor networks [3], and recommender systems [4]. To save power for instance, only a fraction of sensors may collect and transmit measurements to a fusion center, where the available spatiotemporal data can be organized in a matrix format, and the unavailable ones can be eventually interpolated via MC [3]. Similarly, collaborative filtering of ratings given by users to a small number of items are stored in a sparse matrix, and the objective is to predict their ratings for the rest of the items [4]. Existing MC approaches rely on some form of rank minimization or low-rank matrix factorization. Specifically, [1] proves that when MC is formulated as the minimization of the nuclear norm subject to the constraint that the observed entries remain unchanged, exact recovery is possible under mild assumptions; see also [5] where reliable recovery from a few observations is established even in the presence of additive noise. Alternatively, [4] replaces the nuclear norm by a product of two low-rank factor matrices that are identified in order to recover the complete matrix. While the low-rank assumption can be sufficient for reliable recovery, prior information about the unknown matrix can be also accounted to improve the completion outcome. Forms of prior information can include sparsity [3], local smoothness [6], and interdependencies encoded by graphs [7]-[10].

We formulate simple assumptions, implying the Robbins-Monro conditions for the $Q$-learning algorithm with the local learning rate, depending on the number of visits of a particular state-action pair (local clock) and the number of iteration (global clock). It is assumed that the Markov decision process is communicating and the learning policy ensures the persistent exploration. The restrictions are imposed on the functional dependence of the learning rate on the local and global clocks. The result partially confirms the conjecture of Bradkte (1994).