There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. I am certain that healthcare will be the lead industrial area of such a revolution and one of the major catalysts for change is going to be artificial intelligence. With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).

He frames this economic anxiety as a reason for the rise in right-wing, populist politics in the West: "We are living in a world of widening, not diminishing, financial inequality, in which many people can see not just their standard of living, but their ability to earn a living at all, disappearing. It is no wonder then that they are searching for a new deal, which Trump and Brexit might have appeared to represent."

In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and discuss our views on reproducibility.

Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about what-is-happening-now with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that incoming instances are not lost without being captured. Lastly, the learner needs to provide high analytical accuracy measures. Sentinel is a distributed system written in Java that aims to solve this challenge by enforcing both the processing and learning process to be done in distributed form. Sentinel is built on top of Apache Storm, a distributed computing platform. Sentinels learner, Vertical Hoeffding Tree, is a parallel decision tree-learning algorithm based on the VFDT, with ability of enabling parallel classification in distributed environments. Sentinel also uses SpaceSaving to keep a summary of the data stream and stores its summary in a synopsis data structure. Application of Sentinel on Twitter Public Stream API is shown and the results are discussed.

We give a novel formal theoretical framework for unsupervised learning with two distinctive characteristics. First, it does not assume any generative model and based on a worst-case performance metric. Second, it is comparative, namely performance is measured with respect to a given hypothesis class. This allows to avoid known computational hardness results and improper algorithms based on convex relaxations. We show how several families of unsupervised learning models, which were previously only analyzed under probabilistic assumptions and are otherwise provably intractable, can be efficiently learned in our framework by convex optimization.

While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.

Germany composer Johann Sebastian Bach remains a staple in classical music. His illustrious works such as Brandenburg Concertos, the Goldberg variations and Mass B minor remain some of the most powerful pieces ever made. Bach spent over 50 years perfecting his ability to adapt rhythms, forms and textures from styles abroad. Many regard him as one of the greatest composers to have ever lived. And one new artificial intelligence system can recreate Bach's biggest achievements in just minutes. Fast forward to the 21st century where music is created by statistical models and artificial intelligence in no time.

It is important for companies to implement artificial intelligence, personalization, and other new technologies into the strategy, because it will bring the opportunity to find, interpret, dissect and react on difficult market signals. The more we use predictive AI systems and automated decision-making, the more crucial it is to recognize and eliminate bias from data. We idealize smart systems because they include mathematics as the foundation, thinking that AI platforms are more objective than the developers. But researchers found that artificial intelligence absorbs bias from different resources. For example, Harvard professor Latanya Sweeney detected that the word "arrest" connected with names of black babies.

Scientists at the Pharmaceutical Artificial Intelligence (pharma.AI) group of Insilico Medicine, Inc, have announced the publication of a seminal paper demonstrating the application of generative adversarial autoencoders (AAEs) to generating new molecular fingerprints on demand. The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials. Since 2010 deep learning systems demonstrated unprecedented results in image, voice and text recognition, in many cases surpassing human accuracy and enabling autonomous driving, automated creation of pleasant art and even composition of pleasant music.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by FCT (INESC-ID multiannual funding) through the PIDDAC Program funds and under project PEst-OE/EEI/LA0021/2011 and the FP7 Cooperation Work Programme: Food, Agriculture and Fisheries, and Biotechnologies, KBBE-227258 (BIOHYPO project). Quotient Bioresearch received part-funding from the European Union in the scope of BIOHYPO project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: During the elaboration of this manuscript, Ian Morrissey and Daniel Knight were employed by Quotient Bioresearch and belonged to the BIOHYPO European project.