Now on its 7th year, the Big Data Week returned to London last 5 October for a one-day conference gathering data scientists and experts, and data businesses across the globe. This year the event focused on the latest innovations on Advanced Analytics, data visualisation, AI, and IoT projects. Data experts from top data and tech companies such as VMWare, TUI Group, Bigstep, Satalia, GRSC Group, Data Reply, Experian and 3n Strategy, among many others, discussed topics ranging from handling billions of edges in a graph database to artificial intelligence for marketing, and more. Scott Stevenson, #DataScientist at @ASIDataScience, is explaining how to use #AI for realistic speech #generation, covering fake news & the issues within current media, developments in #ML on #SpeechGeneration, demos, consequences & risks of this #technology. The topic stems from the idea and issue of technology producing fake news.

The human cerebral cortex includes billions of neurons organized into six sheetlike layers. More than a century ago, Santiago Ramón y Cajal appreciated the astonishing diversity of cell types in the brain, but even today, it is unclear how the different neuronal cell types are assembled and distributed across the distinct anatomical areas of the cortex to support its diverse functions. We know that excitatory neurons of the cortex are born from a uniform population of radial glia. During development, radial glia line the fluid-filled ventricles and extend long fibers that connect with the outer pial surface. Newborn neurons migrate along radial glia fibers to the outer layers of the tissue, where they form the cerebral cortex.

IBM (NYSE: IBM) has announced that in less than 12 months since the release of IBM Cloud Private – an open source technology that brings cloud capabilities to organizations running on-premises IT systems – hundreds of leading enterprises worldwide have turned to the platform to help modernize their operations. They include New Zealand Police, China's Fuyao Group, Japan's Aflac Insurance, Turkey's credit bureau Kredi Kayıt Bürosu and Brazil's Fidelity National Information Services. Building on this momentum, IBM is announcing a slate of new advanced features for the on-premises private cloud platform, including the integration of powerful AI capabilities such as IBM Watson Assistant and IBM Watson Speech-to-Text, as well as support for additional public clouds, including the IBM Cloud. The updates provide clients with even more choice and flexibility for their IT journeys, and, for the first time, bring the power of IBM's Watson AI behind the company firewall. "The cloud has evolved in a very short time from being a way to cut costs to a platform for business transformation and innovation," said Robin Hernandez, Director, IBM Private Cloud Offering Management.

The paper introduces a non-linear version of the process convolution formalism for building covariance functions for multi-output Gaussian processes. The non-linearity is introduced via Volterra series, one series per each output. We provide closed-form expressions for the mean function and the covariance function of the approximated Gaussian process at the output of the Volterra series. The mean function and covariance function for the joint Gaussian process are derived using formulae for the product moments of Gaussian variables. We compare the performance of the non-linear model against the classical process convolution approach in one synthetic dataset and two real datasets.

In this work, we propose an analysis of the presence of gender bias associated with professions in Portuguese word embeddings. The objective of this work is to study gender implications related to stereotyped professions for women and men in the context of the Portuguese language.

South American bats speak dialects different from those of their North American counterparts. In response, a group of scientists has developed the first artificial intelligence (AI) algorithm for acoustic identification of bat species in Uruguay. It is available online, under a free license and in open-source code. Moreover, says team leader biologist Germán Botto of the Universidad de la República de Uruguay, new recordings collected by scientists using the algorithm in wind farm studies will enhance the system's proficiency in identifying species. "Windmill farms are a menace for birds…and bats," Botto told Mongabay.

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

Historically, games of all kinds have often been the subject of study in scientific works of Computer Science, including the field of machine learning. By using machine learning techniques and applying them to a game with defined rules or a structured dataset, it's possible to learn and improve on the already existing techniques and methods to tackle new challenges and solve problems that are out of the ordinary. The already existing work on card games tends to focus on gameplay and card mechanics. This work aims to apply neural networks models, including Convolutional Neural Networks and Recurrent Neural Networks, in order to analyze Magic: the Gathering cards, both in terms of card text and illustrations; the card images and texts are used to train the networks in order to be able to classify them into multiple categories. The ultimate goal was to develop a methodology that could generate card text matching it to an input image, which was attained by relating the prediction values of the images and generated text across the different categories.

Fundamental Big Five personality traits and their facets are known to correlate with a wide range of linguistic features and, accordingly, the recognition of personality traits from text is a well-established NLP task. Obtaining facets information may however require extensive personality inventories and, as a result, existing computational models are usually limited to the recognition of the five main personality categories. Based on these observations, this paper investigates the recognition of a number of personality facets from a Brazilian Facebook corpus obtained (at low cost) from a shorter personality inventory. In doing so, we compare a number of personality facet recognition models, and present preliminary reference results for further studies in the field.

Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification. For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy. Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies. Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'. Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling. Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.