Researchers from Google AI (formerly Google Research) and Toyota Technological Institute of Chicago have created ALBERT, an AI model that achieves state-of-the-art results that exceed human performance. ALBERT now claims first place on major NLP performance leaderboards for benchmarks like GLUE and SQuAD 2.0, and high RACE performance score. On the Stanford Question Answering Dataset benchmark (SQUAD), ALBERT achieves a score of 92.2, on General Language Understanding Evaluation (GLUE) benchmark, ALBERT achieves a score of 89.4, and on ReAding Comprehension from English Examinations (RACE) benchmark, ALBERT gets a score of 89.4%. ALBERT is a version of Transformer-based BERT that "uses parameter reduction techniques to lower memory consumption and increase the training speed of BERT," according to a paper published on OpenReview.net The paper was published alongside other papers being considered for publication as part of the International Conference of Learning Representations, which will take place in April 2020 in Addis Ababa, Ethiopia.

A whole bunch of fresh info on Microsoft Flight Simulator has come up thanks to a recent preview event, and that includes some notable detail on how the game's cloud tech actually shapes the world you're flying around. In short, the answer to the obvious question is yes: you can totally seek out and fly by your house in-game. That's a pretty simple idea in concept, but making it happen requires a combination of satellite imagery – pulled from Microsoft's own Bing maps – and cloud processing – similarly built from Microsoft's Azure tech. It all works together through the process of photogrammetry, which interprets two-dimensional imagery into three-dimensional game objects. So as long as you're connected to Microsoft servers, you will find the entirety of the real world recreated in the simulator, down to the last detail.

In Part 1 of a 3-part series, Marcell Vollmer, chief digital officer at SAP Ariba, shares his take on where digital procurement is headed. What is the role of e-procurement in B2B e-commerce and what is the role of the chief procurement officer? Vollmer: Procurement has earned an unfair reputation of being a complicated process, but let's break it down simply here. Procurement's role in B2B e-commerce is to streamline the purchasing process and ensure that businesses get the right goods and services that meet all of their specifications, from a corporate-approved vendor, for the right price. Call this the golden triangle of procurement.

The Square Kilometre Array (SKA) has a celestial ambition. The project aims to discover the origins of the universe through the lens of the world's largest radio telescope. The telescope will search outer space for signals travelling through the cosmos and could reveal how the first stars and galaxies were formed after the big bang and the nature of the mysterious force of dark energy. It may even answer the question that humans have pondered since the dawn of civilisation: are we alone in the universe? It will also provide the scientific foundations for more practical applications.

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Posting the landscapes of drone strikes to Instagram: Follow Dronestagram (also on Tumblr and Twitter). These are the names of places. They are towns, villages, junctions and roads. They are the names of places where people live and work, where there are families and schools. They are the names of places in Afghanistan and Yemen, which are linked by one thing: they have each been the location of drone strikes in the past couple of months.

Six of us are huddled together in Cavendish Square Gardens in central London, fighting a horde of warrior skeletons. To passersby, however, we must look like a bunch of adults pointing our smartphones at nothing while shouting about incoming monsters. What we're doing is playing a beta version of Minecraft Earth, an augmented reality (AR) spinoff from the multimillion-selling block-building game – and very soon, parks all over the world will be filled with people just like us. This month, Minecraft is launching an early-access version of the game in a select few territories around the world, ahead of a global roll-out. Microsoft has yet to reveal exactly when and where, but soon thousands of fans used to playing on their console, PC or tablet, are going to be taking their creations to the streets.

A BSTRACT We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. We study the prevalence of local minima in loss landscapes, whether small-norm parameter vectors generalize better (and whether this explains the advantages of weight decay), whether wide-network theories (like the neural tangent kernel) describe the behaviors of classifiers, and whether the rank of weight matrices can be linked to generalization and robustness in real-world networks. In statistical learning, principled kernel methods have vastly improved the performance of SVMs and PCA (Suykens & V andewalle, 1999; Sch olkopf et al., 1997), and boosting theory has enabled weak learners to generate strong classifiers (Schapire, 1990). Optimizers in deep learning are borrowed from the field of convex optimization, where momentum optimizers (Nesterov, 1983) and conjugate gradient methods provably solve ill-conditioned problems with high efficiency (Hestenes & Stiefel, 1952). Deep learning harnesses foundational tools from these mature parent fields. Despite its rigorous roots, deep learning has driven a wedge between theory and practice. Recent theoretical work has certainly made impressive strides towards understanding optimization and generalization in neural networks. But doing so has required researchers to make strong assumptions and study restricted model classes. In this paper, we seek to understand whether deep learning theories accurately capture the behaviors and network properties that make realistic deep networks work. Following a line of previous work, such as Swirszcz et al. (2016), Zhang et al. (2016), Balduzzi et al. (2017) and Santurkar et al. (2018), we put the assumptions and conclusions of deep learning theory to the test using experiments with both toy networks and realistic ones. We focus on the following important theoretical issues: - Local minima: Numerous theoretical works argue that all local minima of neural loss functions are globally optimal or that all local minima are nearly optimal. In practice, we find Authors contributed equally. 1 arXiv:1910.00359v1 Y et for neural networks, it is not at all clear which form of null 2-regularization is optimal.

The dynamics of DNNs during gradient descent is described by the so-called Neural Tangent Kernel (NTK). In this article, we show that the NTK allows one to gain precise insight into the Hessian of the cost of DNNs: we obtain a full characterization of the asymptotics of the spectrum of the Hessian, at initialization and during training.

In the presence of inconsistencies, repair techniques thrive to restore consistency by reasoning with several repairs. However, since the number of repairs can be large, standard inconsistent tolerant semantics usually yield few answers. In this paper, we use the notion of syntactic distance between repairs following the intuition that it can allow us to cluster some repairs "close" to each other. In this way, we propose a generic framework to answer queries in a more personalise fashion.