GK Software SE is presenting innovations and new product versions at the EuroCIS trade show in Düsseldorf. The major focus is on the new full-service solution known as cloud4retail and the personalization of the customer journey with the help of artificial intelligence. GK Software is attending the EuroCIS to present new solutions and innovative concepts, as it does every year. The European market leader is demonstrating its new cloud service known as cloud4retail and solutions for personalisation and dynamic pricing, which are based on AI (artificial intelligence), for the first time. Customers can also expect innovations like the Mobile Customer Assistant, the Virtual Reality Store as well as integrated new solution concepts for the catering sector and gas stations.

Police should look at using artificial intelligence to help cope with the scale of information involved in investigations and avoid the kinds of mistakes that have led to a string of collapsed rape trials, a senior police chief said on Wednesday. Sara Thornton, the chair of the National Police Chiefs' Council, said the volume of data held by individuals had massively increased the number of potential lines of enquiry that officers must pursue to understand a case. In recent months, several rape prosecutions have been dropped after it emerged that police had failed to hand over evidence that undermined their cases. Since then, the Crown Prosecution Service has announced a review of all current rape cases and Nick Ephgrave, the NPCC's lead on criminal justice, has admitted that police have a "cultural problem" with disclosure. The attorney general's guidelines on disclosure say that police have a duty to pursue all reasonable lines of investigation, leading both towards and away from a conviction, Thornton said.

Aalto University and the University of Helsinki have joined forces in artificial intelligence research by establishing the Finnish Center for Artificial Intelligence (FCAI). In partnership with companies and the public sector, FCAI works towards the next generation of AI that is interactive, dependable and data-efficient. AI Day brought together researchers, companies and the public sector with a strong interest in applications and cross-field collaboration.

For Elon Musk, the term artificial intelligence conjures apocalyptic scenarios of autonomous robots wreaking destruction in a world dominated by hyper-intelligent machines. Stephen Hawking foresees a future in which smart machines replace sluggish humans across a range of activities, driving million...

Development work is getting more technologically sophisticated by the day. The World Bank's Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise. A growing family of Artificial Intelligence techniques are being employed in development.

An important issue in neural network research is how to choose the number of nodes and layers such as to solve a classification problem. We provide new intuitions based on earlier results by An et al. (2015) by deriving an upper bound on the number of nodes in networks with two hidden layers such that linear separability can be achieved. Concretely, we show that if the data can be described in terms of N finite sets and the used activation function f is non-constant, increasing and has a left asymptote, we can derive how many nodes are needed to linearly separate these sets. This will be an upper bound that depends on the structure of the data. This structure can be analyzed using an algorithm. For the leaky rectified linear activation function, we prove separately that under some conditions on the slope, the same number of layers and nodes as for the aforementioned activation functions is sufficient. We empirically validate our claims.

Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of large models in statistics and machine learning, can be investigated via the spectrum of the Hessian of the empirical loss function. Network data can be understood via the eigenstructure of a graph Laplacian matrix using spectral graph theory. Quantum simulations and other many-body problems are often characterized via the eigenvalues of the solution space, as are various dynamic systems. However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors. Even worse, one may only have noisy estimates of such matrix vector products. In this work, we combine several different techniques for randomized estimation and show that it is possible to construct unbiased estimators to answer a broad class of questions about the spectra of such implicit matrices, even in the presence of noise. We validate these methods on large-scale problems in which graph theory and random matrix theory provide ground truth.

ATPboost is a system for solving sets of large-theory problems by interleaving ATP runs with state-of-the-art machine learning of premise selection from the proofs. Unlike many previous approaches that use multi-label setting, the learning is implemented as binary classification that estimates the pairwise-relevance of (theorem, premise) pairs. ATPboost uses for this the XGBoost gradient boosting algorithm, which is fast and has state-of-the-art performance on many tasks. Learning in the binary setting however requires negative examples, which is nontrivial due to many alternative proofs. We discuss and implement several solutions in the context of the ATP/ML feedback loop, and show that ATPboost with such methods significantly outperforms the k-nearest neighbors multilabel classifier.

We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the training dataset is augmented sequentially. Specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covariance-fitting methodology to learn the model parameters which yields a predictor that can be updated sequentially with each new data point. The proposed method is evaluated using both synthetic and real climate data which demonstrate its ability to accurately predict data missing in spatial regions over time.

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.