Attackers adding sophistication and machine learning to maximise compromises, while business sits back. A toxic combination of poor cybersecurity hygiene and risky application usage is creating an environment where widespread attacks are considerably more dangerous to businesses, according to a new report. In fact, the data looks pretty damning - while the number of exploits detected in Q2 2017 increased nearly 30 per cent over Q1, attacks became increasingly sophisticated, leveraging machine learning and AI-like strategies to maximise impact and minimise costs for the hackers. Although WannaCry and NotPetya hit the headlines and caused big-dollar damage to many large organisations, much of that could have been avoided with better security hygiene, argues Fortinet's latest Global Threat Landscape Report. However, beyond the headlines the story holds water too - during the period, 90 per cent of organizations recorded exploits against vulnerabilities that were three or more years old.

Software is eating the world and Artificial Intelligence (AI) is at the heart of this takeover. Since we at Asgard are deeply involved in the European AI market, we thought we would share our insights. The United Kingdom takes the lead as the strongest AI ecosystem in Europe. We have counted 121 AI firms in the UK, with London clearly the largest hub. In second place is Germany (51), with Berlin as the main AI hub supporting 30 AI companies.

Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a large chunk of its workforce with robots. CEO John Cryan warned today that a'big number' of people will lose their jobs at the firm as it automates to embrace its'revolutionary spirit.' The Frankfurt, Germany-based company employs 100,000 people globally, but it's unknown how many will be laid off and replaced by machines or when the overhaul will occur. Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a large chunk of its workforce with robots. A'big number' of the company's 100,000 employees will be replaced by machines Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a'big number' of its 100,000 employees with robots.

Amazon announced today that it will open a new Research and Development Centre in Barcelona in the first half of 2018. Over time, Amazon expects to hire more than 100 scientists and software engineers at this centre. The centre will be located in Barcelona's start-up district - 22@ - in the same building as the Amazon Seller Support Hub which, from early 2018, will offer assistance to small and medium companies from Southern Europe that sell on Amazon Marketplace. Amazon's global Machine Learning teams work on algorithms which automatically extract patterns from large collections of data to make accurate predictions. Forecasting customer demand, translating product information and automatic speech recognition are just some examples of how Machine Learning is leading to lower prices, greater selection and better customer experiences at Amazon.

TensorFlow is the most popular open-source machine learning framework in the world. In this video, Andrew Gasparovic gives you an introduction to TensorFlow and goes through new things that have been developed. He also covers some use-cases demonstrating how TensorFlow is used by Google as well as out in the real-world. Check out the'All Sessions' playlist for the rest of the talks that were given at GDD Europe '17: https://goo.gl/6BFfHv Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

If you believe everything you read, you are probably quite worried about the prospect of a superintelligent, killer AI. The Guardian, a British newspaper, warned recently that "we're like children playing with a bomb," and a recent Newsweek headline reads, "Artificial Intelligence Is Coming, and It Could Wipe Us Out." Numerous such headlines, fueled by comments from the likes of Elon Musk and Stephen Hawking, are strongly influenced by the work of one man: professor Nick Bostrom, author of the philosophical treatise Superintelligence: Paths, Dangers, and Strategies. Bostrom is an Oxford philosopher, but quantitative assessment of risks is the province of actuarial science. He may be dubbed the world's first prominent "actuarial philosopher," though the term seems an oxymoron given that philosophy is an arena for conceptual arguments, and risk assessment is a data-driven statistical exercise. So what do the data say?

Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization. Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications. He is author of the following Packt volumes: Python Parallel Programming Cookbook and Getting Started with TensorFlow. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures, focusing C/C, Java, Scala, R, and Python and big data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Hadoop, and MapReduce.

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. Keywords: Distributed Learning, Large Scale Learning, Deep Learning, Bayesian Learn- ing, Variational Inference, Expectation Propagation, Stochastic Approximation, Natural Gradient, Markov chain Monte Carlo, Parameter Server, Posterior Server.

The exponential growth of data generation, measurements and collection in scientific and engineering disciplines leads to the availability of huge and high-dimensional datasets, in domains as varied as text mining, social network, astronomy or bioinformatics to name a few. The only viable path to the analysis of such datasets is to rely on data-intensive distributed computing frameworks [1]. MapReduce has in the last decade established itself as a reference programming model for distributed computing. The model is articulated around two main classes of functions, mappers and reducers, which greatly decrease the complexity of a distributed program while allowing to express a wide range of computing tasks. MapReduce was popularised by Google research in 2008 [2], and may be executed on parallel computing platforms ranging from specialised hardware units such as parallel field programmable gate arrays (FPGAs) and graphics processing units, to large clusters of commodity machine using for example the Hadoop or Spark frameworks [2]-[4]. In particular, the expressiveness of the MapReduce programming model has led to the design of advanced distributed data processing libraries for machine learning and data mining, such as Hadoop Mahout and Spark MLlib. Many of the standard supervised and unsupervised learning techniques (linear and logistic regression, naive Bayes, SVM, random forest, PCA) are now available from these libraries [5]-[7]. Little attention has however yet been given to feature selection algorithms (FSA), which form an essential component of machine learning and data mining workflows. Besides reducing a dataset size, FSA also generally allow to improve the performance of classification and regression models by selecting the most relevant features and reducing the noise in a dataset [8].

Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of conditional GANs (cGANs) for SE, and in particular, we make use of the image processing framework proposed by Isola et al. [1] to learn a mapping from the spectrogram of noisy speech to an enhanced counterpart. The SE cGAN consists of two networks, trained in an adversarial manner: a generator that tries to enhance the input noisy spectrogram, and a discriminator that tries to distinguish between enhanced spectrograms provided by the generator and clean ones from the database using the noisy spectrogram as a condition. We evaluate the performance of the cGAN method in terms of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and equal error rate (EER) of speaker verification (an example application). Experimental results show that the cGAN method overall outperforms the classical short-time spectral amplitude minimum mean square error (STSA-MMSE) SE algorithm, and is comparable to a deep neural network-based SE approach (DNN-SE).