The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize these approaches. In our setting, the cause-mechanism relationship is assessed by comparing it against a null hypothesis through the application of random generic group transformations. We show that the group theoretic view provides a very general tool to study the structure of data generating mechanisms with direct applications to machine learning.

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.

We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the retrieval time (the distance-based account). An alternative theory, direct-access, assumes that retrieval times are a mixture of two distributions: one distribution represents successful retrievals (these are independent of dependency distance) and the other represents an initial failure to retrieve the correct dependent, followed by a reanalysis that leads to successful retrieval. We implement both models as Bayesian hierarchical models and show that the direct-access model explains Chinese relative clause reading time data better than the distance account.

Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a "compressive learning" framework where we estimate model parameters from a sketch of the training data. This sketch is a collection of generalized moments of the underlying probability distribution of the data. It can be computed in a single pass on the training set, and is easily computable on streams or distributed datasets. The proposed framework shares similarities with compressive sensing, which aims at drastically reducing the dimension of high-dimensional signals while preserving the ability to reconstruct them. To perform the estimation task, we derive an iterative algorithm analogous to sparse reconstruction algorithms in the context of linear inverse problems. We exemplify our framework with the compressive estimation of a Gaussian Mixture Model (GMM), providing heuristics on the choice of the sketching procedure and theoretical guarantees of reconstruction. We experimentally show on synthetic data that the proposed algorithm yields results comparable to the classical Expectation-Maximization (EM) technique while requiring significantly less memory and fewer computations when the number of database elements is large. We further demonstrate the potential of the approach on real large-scale data (over 10 8 training samples) for the task of model-based speaker verification. Finally, we draw some connections between the proposed framework and approximate Hilbert space embedding of probability distributions using random features. We show that the proposed sketching operator can be seen as an innovative method to design translation-invariant kernels adapted to the analysis of GMMs. We also use this theoretical framework to derive information preservation guarantees, in the spirit of infinite-dimensional compressive sensing.

At universities, "people learn how to approach new things, ask questions and find answers, deal with new situations," wrote Uta Russmann, a professor of communications at the FHWien University of Applied Sciences in Vienna. "All this is needed to adjust to ongoing changes in work life. Special skills for a particular job will be learned on the job." Schools will also need to teach traits that machines can't yet easily replicate, like creativity, critical thinking, emotional intelligence, adaptability and collaboration. The problem, many respondents said, is that these are not necessarily easy to teach.

In many real-world classification tasks such as churn prediction and fraud detection, we often encounter the class imbalance problem, which means one class is significantly outnumbered by the other class. The class imbalance problem brings great challenges to standard classification learning algorithms. Most of them tend to misclassify the minority instances more often than the majority instances on imbalanced data sets. For example, when a model is trained on a data set with 1% of instances from the minority class, a 99% accuracy rate can be achieved simply by classifying all instances as belonging to the majority class. Indeed, the problem of learning on imbalanced data sets is considered to be one of the ten challenging problems in data mining research.

In recent years, popular culture has done a pretty good job of scaring us with the threat of machines turning on man. Just think of the red-eyed Terminator, from the movie franchise, shorn of its artificial flesh, the foot soldier of the Skynet artificial intelligence that lost patience with its human overlords and decided to wipe us out. Fortunately, that is science fiction, not science fact. The reality is that machines are among us every day and are becoming increasingly integrated into our lives in positive ways. Hence the report into machine learning, just published by the Royal Society.

Researchers tested the AI by having a pianist play a variety of music -- baroque, classical, ragtime and jazz -- in a live demonstration. The AI then assessed the likely genre in real time, vastly outperforming conventional software hand-coded by humans. "I think the deep learning system performs better because it's had a dispassionate look at quite a lot of audio material," says Monty Barlow, director of Machine Learning at Cambridge Consultants. "It's found the best way to detect one genre from another without any prejudice or bias. It's strangely more human-like in its capabilities than our programmers were in the classical engineering approach."

People are unable to send or receive chats or even load up conversations, according to users. And there doesn't seem to be any easy way of fixing the issue, which is affecting many of its users and is likely a problem with its servers. Problems with the app surged over the last hour, according to the website Down Detector. Those problems were particularly focused in western Europe, the East Coast of the US and South America, according to the same website. But that may simply be a result of timezones, and the problems could be happening elsewhere.

Amazon is continuing its fierce expansion in the UK, unveiling plans to hire 400 people for a new development centre due to open in Cambridge in autumn. The retail giant said on Thursday that it was recruiting "extensively" for machine learning scientists, knowledge engineers, data scientists, mathematical modellers, speech scientists and software engineers to staff the new facilities and work on products like the Kindle, Fire tablet, Fire TV Stick, Echo, Echo Dot and the new Echo Look. Once the new centre is open, an existing facility in Cambridge will largely be used for research and development related to Amazon's Prime Air – a delivery system which aims to get parcels to customers in 30 minutes or less using drones. Last week Amazon announced that it was creating 1,200 new jobs at a site in Warrington under plans announced in February to expand its UK workforce by 5,000, despite uncertainty stemming from Brexit. The UK has for some years been a major market for Amazon and in March the sprawling Seattle-headquartered group launched Amazon Business for the UK, aimed at doing for businesses what it already does for individual customers, by offering a marketplace where companies can buy everything from industrial machinery to paper clips and janitorial equipment, even in bulk.