Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Yet, caution should avoid using machine learning as a black-box tool, but rather consider it as a methodology, with a rational thought process that is entirely dependent on the problem under study. In particular, the use of algorithms should ideally require a reasonable understanding of their mechanisms, properties and limitations, in order to better apprehend and interpret their results. Accordingly, the goal of this thesis is to provide an in-depth analysis of random forests, consistently calling into question each and every part of the algorithm, in order to shed new light on its learning capabilities, inner workings and interpretability. The first part of this work studies the induction of decision trees and the construction of ensembles of randomized trees, motivating their design and purpose whenever possible. Our contributions follow with an original complexity analysis of random forests, showing their good computational performance and scalability, along with an in-depth discussion of their implementation details, as contributed within Scikit-Learn. In the second part of this work, we analyse and discuss the interpretability of random forests in the eyes of variable importance measures. The core of our contributions rests in the theoretical characterization of the Mean Decrease of Impurity variable importance measure, from which we prove and derive some of its properties in the case of multiway totally randomized trees and in asymptotic conditions. In consequence of this work, our analysis demonstrates that variable importances [...].

The newest version of Microsoft Windows arrives July 29. Microsoft announced the launch date for Windows 10 on Monday. Upgrading to the major new edition of its operating system will be free for most consumers with a Windows 8 or Windows 7 machine. Microsoft didn't announce a price for those ineligible for a free upgrade. Nor did it say when the smartphone version would be available. Windows 10 represents Microsoft's first attempt to build an operating system that looks and feels the same regardless of the size of the screen or the type of device being used.

Microsoft will roll out the latest version of its Windows operating system at the end of July. The company said Monday that Windows 10 is designed with mobile computing in mind, allowing users to switch seamlessly between personal computers, tablets, smartphones and other gadgets. The operating system is intended to give apps a similar feel on all devices and comes with a new Web browser integrated with Cortana, the company's voice-activated answer to Apple's Siri. Microsoft Corp. says Windows 10 will be available in 190 countries as a free upgrade on July 29 for anyone currently running Windows 8.1 or 7, the two previous versions of the software.

Windows 10 will be released as a free update on 28 July, Microsoft has announced. It will be the last major release of the 29-year-old operating system before Microsoft switches to a "Windows as a service" system, which entails updates being rolled out when ready. This marks a change in Microsoft's business model. The operating system will be offered as a free upgrade for users of Windows 7 and Windows 8.1 within the first year. Each version of Windows has cost upwards of 100, although the majority of Windows users receive new versions of the operating system when buying a new computer, and not by upgrading the software themselves.

MOL Group announced yesterday the winners of the Freshhh 2015 competition, which sees students from all over the world compete in technology and business strategy simulations related to the oil and gas industry. 'Just Ask Siri', consisting of three students from the Prague University of Economics and the Czech Technical University, was awarded first place, with Hungary's'Oil's Creed' and Slovenia's'Decore' teams placing in second and third respectively. All three teams will now be given the opportunity to join MOL Group's graduate recruitment and development program. MOL Group HR Vice President Zdravka Demeter Bubalo commented in a company statement: "We congratulate the top three teams for winning the Freshhh competition 2015. I would like to thank all participants for their endless efforts during the competition. It is incredible to see how young students work with such difficult real-life cases and always find new solutions. The outstanding results from the participants and number of applications are showing us once more that we are heading in the right direction in order to attract top talents of the oil and gas industry."

The AIIDE-14 Workshop program was held Friday and Saturday, October 3–4, 2014 at North Carolina State University in Raleigh, North Carolina. The workshop program included five workshops covering a wide range of topics. The titles of the workshops held Friday were Games and Natural Language Processing, and Artificial Intelligence in Adversarial Real-Time Games. The titles of the workshops held Saturday were Diversity in Games Research, Experimental Artificial Intelligence in Games, and Musical Metacreation.

Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.

Information extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. This work describes the methodology for web scale information extraction in the LODIE project (linked open data information extraction) and highlights results from the early experiments carried out in the initial phase of the project. LODIE aims to develop information extraction techniques able to scale at web level and adapt to user information needs. The core idea behind LODIE is the usage of linked open data, a very large-scale information resource, as a ground-breaking solution for IE, which provides invaluable annotated data on a growing number of domains.

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.