As cognitive computing continues to make great strides, how can compliance professionals harness the power of machine learning and artificial intelligence (AI) to help solve the regulatory challenges they encounter on a daily basis? Cognitive computing encompasses a range of different technologies, two of which include machine learning -- where computers analyze volumes of data and produce recommendations -- and artificial intelligence (AI), which involves the additional aspect of relying on the machine to apply'reason'. Teaching a machine how to think like a person, however, is not without many pitfalls. For example, algorithms can be used to analyze all available information when making a decision, but cannot efficiently take ethical or humanitarian considerations into account. Such pitfalls aside, both machine learning and AI are already used to help compliance professionals make sense of the vast volumes of data they encounter every day, including regulatory changes and updates, breaking news and KYC-related documentation received from clients.

Technology is promising more advantages than ever before. People want things to be cheaper, faster, more convenient and delivered to their doors at no extra cost. Supermarkets without checkouts; clothes shops that take your measurements in seconds and carry out custom tailoring in minutes; schools with increasing robotization of teachers and hospitals with non-human doctors; autonomous cars; restaurants with mechanized menus; galleries showing art made by artificial intelligence (AI); and live music performances by algorithmic composers are just a few examples of future possibilities. Many of these examples may seem like science fiction – but they are nevertheless already being realized in society. Automation refers to processes that are performed without human intervention or assistance.

In a year of High Street gloom one item beamed bright from the sales figures - the giant TV screen. It may still be a tiny segment of the market but sales are rocketing. Dixons Carphone saw a 70% surge in the sale of screens over 65in over Christmas, and a tripling of sales of screens sized 75in or more. John Lewis said sales of 70in TVs have risen 150% compared with last year, partly thanks to a spike in sales from the World Cup. According to the Broadcasters' Audience Research Board (Barb) sales of screens over 40in have been on the increase since at least the beginning of the decade.

Abstract: Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa- Bianchi et al., 2017) it has been shown that pure Boltzmann exploration does not perform well from a regret perspective, even in the simplest setting of stochastic multi-armed bandit (MAB) problems. In this paper, we show that a simple modification to Boltzmann exploration, motivated by a variation of the standard doubling trick, achieves $O(K\log{1 \alpha} T)$ regret for a stochastic MAB problem with $K$ arms, where $\alpha 0$ is a parameter of the algorithm. This improves on the result in (Cesa-Bianchi et al., 2017), where an algorithm inspired by the Gumbel-softmax trick achieves $O(K\log2 T)$ regret. We also show that our algorithm achieves $O(\beta(G) \log{1 \alpha} T)$ regret in stochastic MAB problems with graph-structured feedback, without knowledge of the graph structure, where $\beta(G)$ is the independence number of the feedback graph.

Many neural network architectures rely on the choice of the activation function for each hidden layer. Given the activation function, the neural network is trained over the bias and the weight parameters. The bias catches the center of the activation, and the weights capture the scale. Here we propose to train the network over a shape parameter as well. This view allows each neuron to tune its own activation function and adapt the neuron curvature towards a better prediction. This modification only adds one further equation to the back-propagation for each neuron. Re-formalizing activation functions as CDF generalizes the class of activation function extensively. We aimed at generalizing an extensive class of activation functions to study: i) skewness and ii) smoothness of activation functions. Here we introduce adaptive Gumbel activation function as a bridge between Gumbel and sigmoid. A similar approach is used to invent a smooth version of ReLU. Our comparison with common activation functions suggests different data representation especially in early neural network layers. This adaptation also provides prediction improvement.

In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical not only from a marketing perspective but also from a network optimization perspective. By predicting whether the content will be successful or not in advance, the content file, which is large, is efficiently deployed in the proper service providing server, leading to network cost optimization. Many previous studies have done view count prediction research to do this. However, the studies have been making predictions based on historical view count data from users. In this case, the contents had been published to the users and already deployed on a service server. These approaches make possible to efficiently deploy a content already published but are impossible to use for a content that is not be published. To address the problems, this research proposes a hybrid machine learning approach to the classification model for the popularity prediction of newly video contents which is not published. In this paper, we create a new variable based on the related content of the specific content and divide entire dataset by the characteristics of the contents. Next, the prediction is performed using XGBoosting and deep neural net based model according to the data characteristics of the cluster. Our model uses metadata for contents for prediction, so we use categorical embedding techniques to solve the sparsity of categorical variables and make them learn efficiently for the deep neural net model. As well, we use the FTRL-proximal algorithm to solve the problem of the view-count volatility of video content. We achieve overall better performance than the previous standalone method with a dataset from one of the top streaming service company.

A new artificial intelligence could help sort normal cells from diseased cells, researchers report in a new study. The Human Cell Atlas is a deep learning algorithm method that uses single-cell RNA sequencing to distinguish activated and deactivated cells within humans at any point, according to a study published Wednesday in Nature Communications. The ability to pinpoint healthy cells from diseased cells at a given time within a person's life cycle. "From a methodological point of view, this represents an enormous leap forward. Previously, such data could only be obtained from large groups of cells because the measurements required so much RNA," Maren Büttner, a researcher at the Institute of Computational Biology of the Helmholtz Zentrum München, said in a news release.

"Artificial intelligence has firmly entered our daily lives. AI used by adults, kids, teenagers, students in many parts of everyday tasks. It makes our lives easier and it's a fact." For decades, technology has been the centerpiece of invention and innovation, and would probably remain so even in the unforeseeable future. The fact that we can access information anywhere, and at any time, is not something to be taken for granted.

Published by the Royal Society.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.