Today, Milton Keynes based Artificial Intelligence software company, Celaton has been named a winner of the Queen's Award for Enterprise in Innovation 2017. The Queen's Awards for Enterprise are the UK's most prestigious business awards to celebrate and encourage business excellence. Established in 2004, Celaton Limited has designed and implemented a machine learning software platform which, enables better customer service, faster. An Innovation Award has been given for the development of inSTREAM . Businesses receive a plethora of content on a daily basis from customers, suppliers and staff, which is highly labour intensive to process, make actionable and gain insights from.

This is a guest post by Lauren Delapenha. She is an editor at DiscoverDataScience.com, a one-stop resource for learning about the rapidly-evolving field of data science through comprehensive education and career guides. According to IBM, the world generates 2.5 quintillion bytes of data every day. A decent chunk of those quintillion bytes is made up of people asking the experts how to break into and excel in the dynamic, lucrative field of data science. An even larger chunk of those bytes consists of convoluted, contradicting answers to that question.

On paper alone you would never guess that I grew up poor and hungry. My most recent annual salary was over $700,000. I am a Truman National Security Fellow and a term member at the Council on Foreign Relations. My publisher has just released my latest book series on quantitative finance in worldwide distribution. None of it feels like enough though. I feel as though I am wired for a permanent state of flight or fight, waiting for the other shoe to drop, or the metaphorical week when I don't eat. I've chosen not to have children, partly because--despite any success--I still don't feel I have a safety net. I have a huge minimum checking account balance in mind before I would ever consider having children. If you knew me personally, you might get glimpses of stress, self-doubt, anxiety, and depression.

Frantz Beautifully shot in black and white with the occasional warm burst of color, French writer-director François Ozon's intricately layered post-World War I drama puts a feminist spin on Ernst Lubitsch's 1932 antiwar film "Broken Lullaby." Graduation A film of gripping moral suspense from the writer-director Cristian Mungiu, this tough, clear-eyed and humane movie follows a father (Adrien Titieni) who will do anything to help his daughter (Maria Dragus) escape post-Ceausescu Romania. The Happiest Day in the Life of Olli Mäki A lovely piece of work from Finland, a sweet, warmly observed tale about a boxer falling in love before his biggest bout overlaid with just the right amount of Scandinavian melancholy. I Am Not Your Negro As directed by the gifted Raoul Peck, this documentary on James Baldwin uses the entire spectrum of movie effects, not only spoken language but also sound, music, editing and all manner of visuals, to create a cinematic essay that is powerful and painfully relevant. La La Land Starring a well-paired Ryan Gosling and Emma Stone, writer-director Damien Chazelle's tuneful tribute to classic movie musicals is often stronger in concept than execution, but it's lovely and transporting all the same.

Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering. The most well-known method, the Kalman smoother (and the related Kalman filter), relies on assumptions of linearity and Gaussianity that are rarely met in practice. In this paper, we introduced a new dynamical smoothing method that exploits the remarkable capabilities of convolutional neural networks to approximate complex non-linear functions. The main idea is to generate a training set composed of both latent states and observations from an ensemble of simulators and to train the deep network to recover the former from the latter. Importantly, this method only requires the availability of the simulators and can therefore be applied in situations in which either the latent dynamical model or the observation model cannot be easily expressed in closed form. In our simulation studies, we show that the resulting ConvNet smoother has almost optimal performance in the Gaussian case even when the parameters are unknown. Furthermore, the method can be successfully applied to extremely non-linear and non-Gaussian systems. Finally, we empirically validate our approach via the analysis of measured brain signals.

Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or networks. In this paper, we generalize the traditional concept of wide sense stationarity to signals defined over the vertices of arbitrary weighted undirected graphs. We show that stationarity is expressed through the graph localization operator reminiscent of translation. We prove that stationary graph signals are characterized by a well-defined Power Spectral Density that can be efficiently estimated even for large graphs. We leverage this new concept to derive Wiener-type estimation procedures of noisy and partially observed signals and illustrate the performance of this new model for denoising and regression.

The offer will be available in France for Orange employees from mid-May and for the general public from 6 July 2017. Customers can subscribe directly from the mobile application, online or in one of Orange's 140 certified stores. Innovative and specifically designed for mobile uses, the offer will provide customers from launch with a bank account, a debit card, overdraft protection and an interest-bearing savings account. Additional services, such as credit and insurance, will gradually be included in the offer. Right from the outset, the service will integrate a number of cutting-edge, digital and banking innovations including contactless mobile payments, sending money by SMS, instant bank balances, temporary freezing of the debit card and 24/7 access to a bank advisory service.

Each year, over 20 million people die from heart attacks caused by cardiovascular disease. One of the reasons this keeps happening is due to doctors not having a better way to predict when a person might have a heart attack. As it stands right now, a doctor is the only individual capable of summarizing whether or not a patient could have a heart attack soon. However, things could change in the coming years as a team from the University of Nottingham in the UK is working on ways to make the task easier. According to the latest report, the team established a machine-learning algorithm designed to predict when folks would have a heart attack or even a stroke.

Researchers have seen hundreds of children's books through the eyes of an AI – and it was a nightmare. The team trained a deep learning algorithm to recognize illustrations by feeding it a data set of 6,468 pages from 223 books, by 24 artist. Once the AI had learned the characteristics of a specific artist, it transferred them to a new image that is sure to give children night terrors – it lit a smiling turtle on fire, engulfed a girl in flames and turned a snow covered scene bottom into an image of the apocalypse. Researchers aimed to teach a deep deep learning algorithm to recognize patterns and used children's books illustrations. The team fed the AI a data set of 6,468 pages from 223 books, by 24 artist.