Google will dedicate more than 10,000 staff to rooting out violent extremist content on YouTube in 2018, the video sharing website's chief has said. Writing in the Daily Telegraph, Susan Wojcicki said some users were exploiting YouTube to "mislead, manipulate, harass or even harm". She said the website, owned by Google, had used "computer-learning" technology that could find extremist videos. More than 150,000 of these videos have been removed since June, she said. In March, the UK government suspended its adverts from YouTube, following concerns they were appearing next to inappropriate content.

We are in the fourth industrial revolution, which is characterized by advances in robotics and self-driving car technology, the proliferation of smart home appliances, and more. At the forefront of all these is artificial intelligence (AI), which is the development of automated computer systems that could match or even surpass humans in intelligence. AI is regarded as the next big thing--so big that future technologies will be dependent on it. But then, do we really know what we are getting ourselves into? Here are ten scary facts about artificial intelligence. Let's assume you're driving down a road.

Location fingerprinting using received signal strengths (RSSs) from wireless network infrastructure is one of the most popular and promising technologies for localization in an indoor environment, where there is no line-of-sight signal from the global positioning system (GPS) available [1]: For example, a vector of pairs of a service set identifier (SSID) and an RSS for a Wi-Fi access point (AP) measured at a location can be its location fingerprint. A location of a user/device then can be estimated by finding the closest match between its RSS measurement and the fingerprints of known locations in a database [2]. Note that the location fingerprinting technique does not require the installation of any new infrastructure or the modification of existing devices, but it is just based on the existing wireless infrastructure, which is its major advantage over alternative techniques. When the indoor localization is to cover a large building complex -- e.g., a big shopping mall or a university campus -- where there are lots of multistory buildings under the same management, the scalability of fingerprinting techniques becomes an important issue. The current state-of-the-art Wi-Fi fingerprinting techniques assume a hierarchical approach to the indoor localization, where the building, floor, and position (e.g., a label or coordinates) of a location are estimated in a hierarchical and sequential way using a different algorithm tailored for each task.

We obtain a new Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of the underlying Banach norm. We use this inequality in order to investigate in asymptotical regime the error upper bounds for the broad family of spectral regularization methods for reproducing kernel decision rules, when trained on a sample coming from a $\tau-$mixing process.

Transfer operators such as the Perron-Frobenius or Koopman operator play an important role in the global analysis of complex dynamical systems. The eigenfunctions of these operators can be used to detect metastable sets, to project the dynamics onto the dominant slow processes, or to separate superimposed signals. We extend transfer operator theory to reproducing kernel Hilbert spaces and show that these operators are related to Hilbert space representations of conditional distributions, known as conditional mean embeddings in the machine learning community. Moreover, numerical methods to compute empirical estimates of these embeddings are akin to data-driven methods for the approximation of transfer operators such as extended dynamic mode decomposition and its variants. In fact, most of the existing methods can be derived from our framework, providing a unifying view on the approximation of transfer operators. One main benefit of the presented kernel-based approaches is that these methods can be applied to any domain where a similarity measure given by a kernel is available. We illustrate the results with the aid of guiding examples and highlight potential applications in molecular dynamics as well as video and text data analysis.

Unsupervised image generation has recently received an increasing amount of attention thanks to the great success of generative adversarial networks (GANs), particularly Wasserstein GANs. Inspired by the paradigm of real-valued image generation, this paper makes the first attempt to formulate the problem of generating manifold-valued images, which are frequently encountered in real-world applications. For the study, we specially exploit three typical manifold-valued image generation tasks: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. In order to produce such kinds of images as realistic as possible, we generalize the state-of-the-art technique of Wasserstein GANs to the manifold context with exploiting Riemannian geometry. For the proposed manifold-valued image generation problem, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware Wasserestein GAN can generate high quality manifold-valued images.

The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied to digital pathology and radiology, nevertheless, there are still practical issues that prevent these tools to be widely used in practice. The main obstacles are low number of available cases and large size of images (a.k.a. the small n, large p problem in machine learning), and a very limited access to annotation at a pixel level that can lead to severe overfitting and large computational requirements. We propose to handle these issues by introducing a framework that processes a medical image as a collection of small patches using a single, shared neural network. The final diagnosis is provided by combining scores of individual patches using a permutation-invariant operator (combination). In machine learning community such approach is called a multi-instance learning (MIL).

Hyperparameter optimization undergoes extensive evaluations of validation errors in order to find its best configuration. Bayesian optimization is now popular for hyperparameter optimization, since it reduces the number of validation error evaluations required. Suppose that we are given a collection of datasets on which hyperparameters are already tuned by either humans with domain expertise or extensive trials of cross-validation. When a model is applied to a new dataset, it is desirable to let Bayesian optimization start from configurations that were successful on similar datasets. To this end, we construct a Siamese network with convolutional layers followed by bi-directional LSTM layers, to learn meta-features over image datasets. Learned meta-features are used to select a few datasets that are similar to the new dataset, so that a set of configurations in similar datasets is adopted as initialization to warm-start Bayesian hyperparameter optimization. Experiments on image datasets demonstrate that our learned meta-features are useful in optimizing hyperparameters in deep residual networks for image classification.

Machine Learning (ML) has found it particularly useful in malware detection. However, as the malware evolves very fast, the stability of the feature extracted from malware serves as a critical issue in malware detection. Recent success of deep learning in image recognition, natural language processing, and machine translation indicate a potential solution for stabilizing the malware detection effectiveness. We present a coloR-inspired convolutional neuRal network-based AndroiD malware Detection (R2-D2), which can detect malware without extracting pre-selected features (e.g., the control-flow of op-code, classes, methods of functions and the timing they are invoked etc.) from Android apps. In particular, we develop a color representation for translating Android apps into RGB color code and transform them to a fixed-sized encoded image. After that, the encoded image is fed to convolutional neural network for automatic feature extraction and learning, reducing the expert's intervention. We have collected over 1 million malware samples and 1 million benign samples according to the data provided by Leopard Mobile Inc. from its core product Security Master (which has 623 million monthly active users and 10k new malware samples per day). It is shown that R2-D2 can effectively detect the malware. Furthermore, we keep our research results and release experiment material on http://R2D2.TWMAN.ORG if there is any update.

Peregrine falcons attack their prey on the wing as if they were air-to-air guided missiles, a study has found. Lessons from the birds' control strategy could aid the development of robot interceptors designed to bring down rogue drones, scientists believe. For the study, researchers obtained a birds-eye-view of falcons in flight using miniature video cameras attached to the birds' backs. Peregrine falcons attack their prey on the wing as if they were air-to-air guided missiles, a study has found. For the study, researchers obtained a birds-eye-view of falcons in flight using miniature video cameras attached to the birds' backs (pictured) The scientists were surprised to find that the peregrine falcon's'terminal attack' trajectory followed a mathematical guidance law used to steer homing missiles to their targets.