Access to data is one thing. Moving from data to insight is another entirely. Jane Zavalishina, CEO of the Yandex Data Factory wants to make it easier for businesses to gain real insight from their data. Built on the real time personalization and predictive analytics technology of Russia's largest Internet business Yandex- Yandex Data Factory helps clients improve their business through the exploitation of their own data by machine learning. Disproportionate growth of figures suggesting that 90% of the world's data was generated over the course of the past two years has led Zavalishina to be of the opinion that "we should not really care about data anymore," rather, we should see it as a thing of the past.

At a glance, machine learning and statistics seem to be very similar, but many people fail to stress the importance of the difference between these two disciplines. Machine learning and statistics share the same goals--they both focus on data modeling--but their methods are affected by their cultural differences. In order to empower collaboration and knowledge creation, it's very important to understand the fundamental underlying differences that reflect in the cultural profile of these two disciplines. To gain a deeper understanding of these differences, we need to take a step back and look at their historical roots. In 1946, the first computer system, ENIAC, was developed with the vision of reforming numerical computation using a machine (instead of manual numerical computation using pencil and paper).

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.

Of all the fast and powerful computers in the world, our brain remains by far the most impressive. Now an interdisciplinary team of scientists, led by Baylor College of Medicine, aims to reveal the computational building blocks of our brain and use them to create smarter learning machines. To enable this ambitious project, the U.S. government's Intelligence Advanced Research Projects Activity(IARPA) has awarded a 21 million contract to an interdisciplinary team of neuroscientists, computer scientists, physicists and mathematicians, led by principal investigator Dr. Andreas Tolias, associate professor of neuroscience at Baylor. The research team includes scientists from Baylor, the California Institute of Technology, Columbia University, Cornell University, Rice University, the University of Toronto and the University of Tuebingen. The program supporting this research is known as Machine Intelligence from Cortical Networks (MICrONS) and was envisioned and organized by Jacob Vogelstein, a neuromorphic engineer and program manager with IARPA.

Look closely at what many journalists write about artificial intelligence – from AlphaGo's triumph at the ancient Chinese board game Go to Microsoft's accidentally racist Twitter bot – and you might detect some smugness. Research by Oxford University has predicted that journalism is among the jobs least likely to be replaced by a machine in the near future. And yet, as Columbia University prepares to celebrate 100 years of the Pulitzer prize, intelligent robots will publish financial reports, sports commentaries, clickbait and myriad other articles formerly the preserve of trained journalists. "A machine will win a Pulitzer one day," predicts Kris Hammond from Narrative Science, a company that specialises in "natural language generation". "We can tell the stories hidden in data."

Google has made one its largest European acquisitions to date with a deal to buy DeepMind technologies, a London-based artificial intelligence firm which specialises in machine learning, advanced algorithms and systems neuroscience. The Guardian understands that Google paid 400m ( 650m) for DeepMind, which develops technologies for e-commerce and games, and has demonstrated computer systems capable of playing computer games. It aims, it says, to develop computers that think like humans. The two-year-old artificial intelligence startup was founded by former child chess prodigy and neuroscientist Demis Hassabis alongside Shane Legg and Mustafa Suleyman. DeepMind has reportedly competed with Google and other major artificial intelligence companies for talent and Google's chief executive Larry Page is said to have led the deal himself.

The virtual brain will be an exceptional tool giving neuroscientists a new understanding of the brain and a better understanding of neurological diseases. The Blue Brain project began in 2005 with an agreement between the EPFL and IBM.The Blue Brain Project goal is to create a synthetic brain by reverse engineering the mammalian brain down to the molecular level. The project, founded in May 2005 in Switzerland, is to study the brain's architectural and functional principles. There are a number of sub-projects, including the Cajal Blue Brain, and others run by universities and independent laboratories. HNeT Application Development System which was released in 1990 contained a number of example applications, based on the complex valued phase coherence/decoherence process including complex valued Hopfield network or complex associative memory.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

This is the core of the event. This is where you will meet a lot of cool people. There will probably be beer and pizza.