In this article we describe a new Hermite series based sequential estimator for the Spearman's rank correlation coefficient and provide algorithms applicable in both the stationary and non-stationary settings. To treat the non-stationary setting, we introduce a novel, exponentially weighted estimator for the Spearman's rank correlation, which allows the local nonparametric correlation of a bivariate data stream to be tracked. To the best of our knowledge this is the first algorithm to be proposed for estimating a time-varying Spearman's rank correlation that does not rely on a moving window approach. We explore the practical effectiveness of the Hermite series based estimators through real data and simulation studies demonstrating good practical performance. The simulation studies in particular reveal competitive performance compared to an existing algorithm. The potential applications of this work are manifold. The Hermite series based Spearman's rank correlation estimator can be applied to fast and robust online calculation of correlation which may vary over time. Possible machine learning applications include, amongst others, fast feature selection and hierarchical clustering on massive data sets.

In preparing the the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only …

Former CIA director, author of the book'Undaunted,' John Brennan provides insight on'Fox News Sunday.' The U.S. military flew a pair of B-52 bombers to the Middle East Thursday from Barksdale AFB in Louisiana the second deterrence mission against Iran in recent weeks and comes on the same day U.S. drones attacked al-Qaeda-linked'explosives experts' in Somalia. "We have seen some indications of increased attack planning by Iranian-linked forces inside Iraq" said one U.S. military official who declined to be identified to discuss the sensitive nature of the information. "Presidential transitions are normally a time when our adversaries try to test us," the official added. U.S. military forces are drawing down to 2,500 in Iraq and Afghanistan before January 20th.

Cristian Alonso is an economist in the IMF's Fiscal Affairs Department; Siddharth Kothari is an economist in the IMF's Asia and Pacific Department' Sidra Rehman is an economist in the IMF's Middle East and Central Asia Department. At a joint meeting of the UN's Economic and Social Council (ECOSOC) and its Economic and Social Committee, a robot named Sophia had an interactive session last year with Deputy Secretary-General Amina J. Mohammed. WASHINGTON DC, Dec 8 2020 (IPS) - New technologies like artificial intelligence (AI), machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. The opportunities and potential sources of growth that, for example, the United States and China enjoyed during their early stages of economic development are remarkably different from what Cambodia and Tanzania are facing in today's world. Our recent staff research finds that new technology risks widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established.

As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome. While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. "Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships," says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. "I assumed these robots would never exist in my lifetime." A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality.

Shiraaz Joosub, Healthcare Sales Executive, T-Systems South Africa Medical malpractice litigation costs South Africa millions of rands every year and drives up the cost of healthcare. While some claims of medical negligence have merit, the unfortunate reality is that there has been a spike in fraud in this area since 2017. Over the years, this has cost government billions and has had a devastating effect on the country's public healthcare sector. Now, in the wake of the COVID-19 pandemic, it is more important than ever to stop medical fraud in its tracks and reduce medical malpractice. Fortunately, data analytics, artificial intelligence (AI) and machine learning can assist greatly in developing a digital audit trail to protect healthcare providers against fraudulent medical malpractice claims.A cost beyond billionsIn 2018, the Special Investigations Unit (SIU) began investigating medical fraud in the Eastern Cape and Gauteng, after a spike of R8.4 billion in medical negligence claims.

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline. One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.

Many natural optimization problems are NP-hard, which implies that they are probably hard to solve exactly in the worst-case. However, in practice, it suffices to get reasonably good solutions for all (or even most) instances. This paper presents a new algorithm for computing approximate solution in ${\Theta(N})$ for the MAX-E-$3$-SAT problem by using deep learning methodology. This methodology allows us to create a learning algorithm able to fix Boolean variables by using local information obtained by the Survey Propagation algorithm. By performing an accurate analysis, on random CNF instances of the MAX-E-$3$-SAT with several Boolean variables, we show that this new algorithm, avoiding any decimation strategy, can build assignments better than a random one, even if the convergence of the messages is not found. Although this algorithm is not competitive with state-of-the-art MAX-SAT solvers, it can solve substantially larger and more difficult problems than it ever saw during training.

This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function. We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.

While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hydrodynamic simulations that are computationally costly to run. We explore the merging of an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework. In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties. While there exists a trade-off between the reached accuracy and the speed advantage this approach offers, our results outperform an approach using only machine learning for a subset of baryonic properties. We demonstrate that this novel hybrid system enables the fast completion of dark matter-only information by mimicking the properties of a full hydrodynamic suite to a reasonable degree, and discuss the advantages and disadvantages of hybrid versus machine learning-only frameworks. In doing so, we offer an acceleration of commonly deployed simulations in cosmology.