The remote site is believed to be a secondary mass grave, meaning the bodies were removed from the execution site to this location some time later in an attempt to hide the crime, Amor Masovic, the head of a regional Commission for Missing Persons, told Reuters.

Almost every word has more than one meaning. Modern search engines solve this problem using knowledge bases. Yago was one of the first knowledge bases, developed by scientists at the Max Planck Institute for Informatics in Saarbrücken and the Télécom ParisTech in Paris. Last week, the researchers received an award for their work on Yago from the most important scientific journal in the field of artificial intelligence. Today, they are releasing Yago's source code.

Forty years later, there are more architects, and more work for us, than ever--yet the existential angst remains: If recessions, construction managers, and liability insurance underwriters didn't manage to dismantle the profession, now what? Answering that question comes the Oxford duo of Richard and Daniel Susskind and their 2015 tome The Future of the Professions, an exhaustive examination of how the broad influences of digital technology may be the end-of-times challenge to the professional class so desired by Illich. The Susskinds argue that it will not be a loss of faith in architects, lawyers, and accountants, but rather the broad democratization of expertise through big data and data sharing, expert systems, and automation that will "transform the work of human experts." As knowledge work begins the same transfiguration in the world of computation that manufacturing experienced with machine automation, the bespoke relationships curated by architects with clients will be circumvented by widely accessible knowledge systems, architects will no longer be the anointed "gatekeepers" of professional knowledge or judgment, and the increasing complexity of building problems will face economic pressures demanding that architects provide even more service for less money. Large swaths of professional services will be routinized by computers, further decomposing those services into discrete automated tasks.

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an "in-scope" filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry. Our system solves almost twice as many molecules and is 30 times faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally after a 60 year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature. We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.

Despite large incentives, correctness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming. To address the performance bottleneck in deriving an algorithm from a specification, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query via the most efficient routes. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system designed to achieve them. In a preliminary empirical study of adaptive evaluation for a simple test program, the evaluation strategy adapts successfully to evaluate a query efficiently.

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.

Abstract--Support V ector Data Description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of the data boundary. For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting, and an extremely wiggly data boundary leads to overfitting. In this paper, we propose an empirical criterion to obtain good values of the Gaussian kernel bandwidth parameter . This criterion provides a smooth boundary that captures the essential geometric features of the data. Support V ector Data Description (SVDD) is a machine learning technique used for single-class classification and outlier detection.

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 The car is displayed at Downing Street, when the team visited David Cameron to demonstrate the project. Artist's rendering of Nasa's LRO spacecraft, which will have to withstand a rapid drop in temperature during an upcoming lunar eclipse that could lead to it shutting down. The regulator will now charge far more to phone companies for using the mobile spectrum -- and though it says that fee will not be passed on to customers, experts have said that prices are likely to go up. Apple has released a bright pink new iPhone 6s -- likely the only way that you'll be able to tell that someone has the new handset. The company released the new phone with much fanfare, but almost all of the changes -- a new camera and pressure-sensitive display -- were on the inside. The only new noticeable addition to the phone's look is the very pink rose gold colour, and a tiny "S" on the back.