Telling cause from effect using observational data is a challenging problem, especially in the bivariate case. Contemporary methods often assume an independence between the cause and the generating mechanism of the effect given the cause. From this postulate, they derive asymmetries to uncover causal relationships. In this work, we propose such an approach, based on the link between Kolmogorov complexity and quantile scoring. We use a nonparametric conditional quantile estimator based on copulas to implement our procedure, thus avoiding restrictive assumptions about the joint distribution between cause and effect. In an extensive study on real and synthetic data, we show that quantile copula causal discovery (QCCD) compares favorably to state-of-the-art methods, while at the same time being computationally efficient and scalable.

We introduce jet topics: a framework to identify underlying classes of jets from collider data. Because of a close mathematical relationship between distributions of observables in jets and emergent themes in sets of documents, we can apply recent techniques in "topic modeling" to extract jet topics from data with no input from simulation or theory. As a proof-of-concept with parton shower samples, we apply jet topics to determine separate quark and gluon distributions for constituent multiplicity. We also determine separate quark and gluon rapidity spectra from a mixed Z-plus-jet sample. Because jet topics are defined directly from hadron-level multi-differential cross sections, one can predict jet topics from first-principles theoretical calculations, with potential implications for how to define quark and gluon jets beyond leading-logarithmic accuracy. These investigations suggest that jet topics will be useful for extracting underlying jet distributions and fractions in a wide range of contexts at the Large Hadron Collider.

Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based on a composite likelihood approach using a general belief updating framework, which leads to a recursive computation of the predictor as well as of learning the hyper-parameters. We then provide an analysis of the derived composite GP model in predictive and information-theoretic terms. Finally, we evaluate the approximation with both synthetic data and a real-world application.

We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling. Then, for latent Dirich-let allocation,we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. In particular, using Gibbs sampling for latent variable inference is superior to variational inference in terms of test log-likelihoods. Moreover, Bayesian inference through variational methods perform poorly, sometimes leading to worse fits with latent variables of higher dimensionality.

This year sees us on the brink of many exciting technological developments, so we'd thought we would take a look at some of the new technologies that could transform the way we live and do business in the coming months and years. When we hear the term'artificial intelligence' (AI), most of us will think of sci fi films and robots. But AI and machine learning are not just for the big screen anymore; they're here for real, powered by advances in computing and the huge amount of data that's now available. From devices like Alexa that listen and obey, to software that analyses the emotional tone of social media posts, the possible applications for both businesses and consumers are unlimited. Businesses are using machine learning to gain a competitive advantage more and more nowadays.

With artificial intelligence weapons on both sides, are we in a new cold war? It is easy to confuse the current geopolitical situation with that of the 1980s. The United States and Russia each accuse the other of interfering in domestic affairs.…

Scientists from ETH Zurich are training drones how to navigate city streets by having them study things that already know how: cars and bicycles. The software being used is called DroNet, and it's a convolutional neural network. Meaning, it learns to fly and navigate by flying and navigating. The scientists collected their own training data by strapping GoPros to cars and bikes, and rode around Zurich in addition to tapping publicly available videos on Github. So far, the drones have learned enough to not cross into oncoming traffic and to avoid obstacles like traffic pylons and pedestrians.

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Ahead of Samsung Galaxy S9's debut, developers have uncovered information suggesting that the flagship Android smartphone is going to come equipped with an advanced biometric system. XDA Developers published a report over the weekend detailing what appears to be a new unlocking feature they found after doing an APK teardown of the Settings app on the latest Android Oreo beta for the tech giant's Galaxy Note 8 flagship phablet. Apparently, they stumbled upon strings pertaining to Samsung's upcoming 2018 flagships. The report states that the Galaxy S9 and S9 will likely come equipped with a feature called "Intelligent Scan," which is basically a combination of facial recognition and iris scanning technologies. Because this biometric system reads both face and iris scans, it offers better security and accuracy compared to the iris scanner that debuted with last year's flagship handsets.

It is easy to confuse the current geopolitical situation with that of the 1980s. The United States and Russia each accuse the other of interfering in domestic affairs. Russia has annexed territory over U.S. objections, raising concerns about military conflict. As during the Cold War after World War II, nations are developing and building weapons based on advanced technology. AI can also be used to control non-nuclear weapons including unmanned vehicles like drones and cyberweapons, the expert says. Unmanned vehicles must be able to operate while their communications are impaired – which requires onboard AI control.