Machine learning is an increasingly prevalent buzzword in the media. Its applications in science and the private sector are frequently discussed--but what about global development? Can it also help advance fields like health, agriculture, and financial inclusion? That's because it can help us uncover previously invisible patterns in data, to identify the most effective solutions and target them in the right way. Machine learning (ML) has been around for decades, but now is our chance to apply it to development challenges in new ways, for three reasons.

This fall, the School of Science will welcome four new members joining the faculty in the departments of Biology, Brain and Cognitive Sciences, and Chemistry. Evelina Fedorenko investigates how our brains process language. She has developed novel analytic approaches for functional magnetic resonance imaging (fMRI) and other brain imaging techniques to help answer the questions of how the language processing network functions and how it relates to other networks in the brain. She works with both neurotypical individuals and individuals with brain disorders. Fedorenko joins the Department of Brain and Cognitive Sciences as an assistant professor.

Flight Simulator was once one of the jewels in Microsoft's crown, as close to synonymous with PC gaming as it's possible to get. The series debuted a staggering 37 years ago, pre-dating even Windows as an operating system, and demanded exacting attention from players as they guided increasingly detailed planes safely through the skies. Over the course of a dozen iterations spanning nearly four decades, the flying experience evolved from blocky cockpit views to full aerial tours with a hangar's worth of realistically modelled aircraft to get to grips with. It's been running so long that even Microsoft does not know its sales figures, but Flight Simulator has certainly been played by millions. Yet as PC gaming blossomed, becoming home to everything from competitive shooters to arthouse narrative games, Flight Simulator's star began to wane. The last major release was 2006's Microsoft Flight Simulator X (eventually revamped and repackaged for Steam in 2014), while 2012's simplified spin-off, Microsoft Flight, had an aborted take off, cancelled a mere five months after launch.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

In a decentralised knowledge representation system such as the W eb of Data, it is common and indeed desirable for different knowledge graphs to overlap. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Whilst the deductive value of such identity statements can be extremely useful in enhancing various knowledge-based systems, incorrect use of identity can have wide-ranging effects in a global knowledge space like the W eb of Data. With several works already proven that identity in the W eb is broken, this survey investigates the current state of this "sameAs problem". An open discussion highlights the main weaknesses suffered by solutions in the literature, and draws open challenges to be faced in the future.

Few things reveal the limits of someone's problem-solving skills faster than a Rubik's Cube, the multicolored, three-dimensional puzzle that has befuddled so many since the 1970s. Though the cube has furrowed countless human brows over the years, it's not much of a challenge for an emerging group of hyper-intelligent machines, as it turns out. This week, the University of California at Irvine announced that an artificial intelligence system solved the puzzle in just over a second, besting the current human world record by more than two seconds. The system, known as DeepCubeA -- a reinforcement-learning algorithm programmed by UCI computer scientists and mathematicians -- solved the puzzle without prior knowledge of the game or coaching from its human handlers, according to the university. The feat is even more impressive considering that there are billions of potential moves available to a Rubik's Cube player, with the puzzle's six sides and nine sections, but only one goal: each of the cube's six sides displaying a solid color.

Global Artificial Intelligence (AI) in Agriculture Market 2019 by Company, Regions, Type and Application, Forecast to 2024 presents a detailed competitive outlook and systematic framework of Artificial Intelligence (AI) in Agriculture market at a global uniform platform. The report begins with the market summary, chain structure, past and present market size in conjunction with business opportunities in coming back years, demand and lack, various drivers and restrainers. The research study exhibits the historical data that analyzes respective analytical tools including porters five forces analysis, supply chain analysis, pricing analysis, and regulatory analysis. It offers a detailed analysis of top-line vendors along with revenue and cost profit analysis. The research covers a crucial market segmentation analysis that is a rich source of all essential segments including Artificial Intelligence (AI) in Agriculture types, applications, technologies, end-users, and regions.

Tremendous research effort has been dedicated over the years to thoroughly investigate non-monotonic reasoning. With the abundance of non-monotonic logical formalisms, a unified theory that enables comparing the different approaches is much called for. In this paper, we present an algebraic graded logic we refer to as LogAG capable of encompassing a wide variety of non-monotonic formalisms. We build on Lin and Shoham's argument systems first developed to formalize non-monotonic commonsense reasoning. We show how to encode argument systems as LogAG theories, and prove that LogAG captures the notion of belief spaces in argument systems. Since argument systems capture default logic, autoepistemic logic, the principle of negation as failure, and circumscription, our results show that LogAG captures the before-mentioned non-monotonic logical formalisms as well. Previous results show that LogAG subsumes possibilistic logic and any non-monotonic inference relation satisfying Makinson's rationality postulates. In this way, LogAG provides a powerful unified framework for non-monotonicity.

From automated eye scans to analysing the cries of new-born babies, faster drug development to personalised medicine, artificial intelligence (AI) promises huge advances in the field of healthcare. At the recent AI for Good Summit in Geneva, Switzerland, we were told how AI could speed up the development of new drugs, lead to personalised medicine informed by our genomes, and help diagnose diseases in countries suffering from underdeveloped health services and a chronic shortage of doctors. But there are two main obstacles preventing access to this utopian destination. One is that the AI being applied to the world's health problems isn't quite good enough yet. The other related issue is the lack of good quality digital data - less than 20% of the world's medical data is available in a form that AI machine learning algorithms can ingest and learn from, the WHO estimates.

WASHINGTON/DUBAI, UNITED ARAB EMIRATES - Iran said it had seized a British oil tanker in the Strait of Hormuz on Friday but denied Washington's assertion that the U.S. Navy had downed an Iranian drone nearby this week, as tensions in the Gulf region rose again. Britain said it was urgently seeking information about the Stena Impero tanker, which had been heading to a port in Saudi Arabia and suddenly changed course after passing through the strait at the mouth of the Gulf. The tanker's operator, Stena Bulk, said in a statement the ship was no longer under the crew's control and could not be contacted. Iran's state news agency IRNA quoted a military source as saying the vessel had turned off its tracker, ignored warnings from the Revolutionary Guard and was sailing in the wrong direction in a shipping lane. "We will respond in a way that is considered but robust and we are absolutely clear that if this situation is not resolved quickly there will be serious consequences," British Foreign Secretary Jeremy Hunt told reporters.