Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implements scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning methods to identify conceptual entities. We associate source code entities to these natural language concepts using word embedding and clustering techniques. Practical naming conventions for methods and functions tend to reflect the concept(s) they implement. We take advantage of this specificity by presenting a novel process for joint clustering text concepts that combines word-embeddings, nonlinear dimensionality reduction, and clustering techniques to assist in understanding, organizing, and comparing software in the open science ecosystem. With our pipeline, we aim to assist scientists in building on existing models in their discipline when making novel models for new phenomena. By combining source code and conceptual information, our knowledge graph enhances corpus-wide understanding of scientific literature.

Most of existing manifold learning methods rely on Mean Squared Error (MSE) or $\ell_2$ norm. However, for the problem of image quality assessment, these are not promising measure. In this paper, we introduce the concept of an image structure manifold which captures image structure features and discriminates image distortions. We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold. The LLISE is inspired by Locally Linear Embedding (LLE) but uses SSIM rather than MSE. This paper builds a bridge between manifold learning and image fidelity assessment and it can open a new area for future investigations.

Microsoft Research India has announced the launch of a center for Societal impact through Cloud and Artificial Intelligence (SCAI). Part of the Microsoft Research (MSR) Lab in Bengaluru, SCAI will focus on creating and nurturing projects and transitioning them from lab to scale for real-world impact. "There are so many opportunities to leverage recent advances in cloud computing and AI technologies to address long-term societal challenges spanning multiple sectors and realms, including health and wellness, education, transportation, and agriculture," said Eric Horvitz, Technical Fellow and Director at Microsoft Research. SCAI will engage with NGOs, academicians, and startups through external collaborations; graduate and undergraduate students through the SCAI Fellow program in collaboration; and actively seek collaborators through calls for proposals. To start with, Microsoft is currently working with four organizations – Respirer Living Sciences for a project focusing on urban air pollution, NIMHANS for a project on mental health, Pratham Books for assisted translation system which enables children to read storybooks in multiple languages, and Voicedeck Technologies for Learn2Earn, a program which reinforces education and rewards learning through financial incentives.

After six months of competition (and a few last-minute submissions), we are happy to announce the conclusion and winners of the Obstacle Tower Challenge. We want to thank all of the participants for both rounds and congratulate Alex Nichol, the Compscience.org We are also excited to share that we have open-sourced Obstacle Tower for the research community to extend for their own needs. We started this challenge in February as a way to help foster research in the AI community, by providing a challenging new benchmark of agent performance built in Unity, which we called Obstacle Tower. The Obstacle Tower was developed to be difficult for current machine learning algorithms to solve, and push the boundaries of what was possible in the field by focusing on procedural generation. Key to that was only allowing participants access to one hundred instances of the Obstacle Tower, and evaluating their trained agents on a set of unique procedurally generated towers they had never seen before.

Violeta Misheva works as a data scientist at ABN AMRO bank in Amsterdam. Before her current role, she completed a PhD degree in applied microeconomics from Erasmus School of Economics, after which she worked as a data science consultant. She is passionate about AI for good, fair and unbiased machine learning and is an advocate for diversity in the tech world. She enjoys sharing her data science knowledge with others, that's why part-time she conducts workshops with students, has designed a course for the DataCamp, and regularly attends and presents at conferences and other events.

The report highlights the determined vendor overview of the market along with the summary of the leading market players. The growth of every segment of the market is also predicted in the global research report over the estimated period. Furthermore, the market evaluation in terms of value and volume (US$ mn and thousand units) consists of data from across all six regions of the globe including North America, Asia Pacific, South America, Middle East & Africa, and Europe.

Lagos – Educationists, researchers and others have said that Nigeria must include relevant skills acquisition in its education curricula to remain relevant in the comity of nations. They made their views known in a communique they issued at the end of a three-day University of Lagos International Research Conference and Fair. The International Conference of the Humanities and Science which began on Wednesday had the theme: "Automation and Artificial Intelligence: Opportunities for 21st Research and Development''. The communique was signed by the Director, Academic Planning of the institution, Prof. Obinna Chukwu. The News Agency of Nigeria (NAN) reports that the conference – the 14th edition – was attended by more than 400 scholars, captains of industries and others from within and outside Nigeria. A total number of 168 oral paper presentations and 49 poster presentations were made the conference which had Air Peace and Access Bank as major sponsors. The participants observed that many countries suffered from significant skill mismatch due to inability of the education system to accurately reflect the demands of the labour market. The participants noted that technology, specifically, artificial intelligence, was rapidly changing trends and perceptions in different facets of life including education, employment, economy, communication and healthcare. They added that artificial intelligence would aid resource utilisation and development of smart cities. They urged that researches in the academia that cut across disciplines should be carrief out in partnership with industries to build required competencies. They also recommended that organisations in emerging markets should make investments in automation to bridge the gap between them and their counterparts in developed markets. "Higher education should help students compete in artificial intelligence age by including it in the curriculum.

Millions of people communicate using sign language, but so far projects to capture its complex gestures and translate them to verbal speech have had limited success. A new advance in real-time hand tracking from Google's AI labs, however, could be the breakthrough some have been waiting for. The new technique uses a few clever shortcuts and, of course, the increasing general efficiency of machine learning systems to produce, in real time, a highly accurate map of the hand and all its fingers, using nothing but a smartphone and its camera. "Whereas current state-of-the-art approaches rely primarily on powerful desktop environments for inference, our method achieves real-time performance on a mobile phone, and even scales to multiple hands," write Google researchers Valentin Bazarevsky and Fan Zhang in a blog post. "Robust real-time hand perception is a decidedly challenging computer vision task, as hands often occlude themselves or each other (e.g.

One astronomer had jumped the gun, tweeting ahead of an official announcement by LIGO (the Laser Interferometer Gravitational-Wave Observatory). The observatory had detected an outburst of gravitational waves, or ripples in spacetime, and an orbiting gamma-ray telescope had simultaneously seen electromagnetic radiation emanating from the same region of space. The observations--which were traced back to a colliding pair of neutron stars 130 million light-years away--marked a pivotal moment for multimessenger astronomy, in which celestial events are studied using a wide range of wildly different telescopes and detectors. The promise of multimessenger astronomy is immense: by observing not only in light but also in gravitational waves and elusive particles called neutrinos, all at once, researchers can gain unprecedented views of the inner workings of exploding stars, galactic nuclei and other exotic phenomena. But the challenges are great, too: as observatories get bigger and more sensitive and monitor ever larger volumes of space, multimessenger astronomy could drown in a deluge of data, making it harder for telescopes to respond in real time to unfolding astrophysical events.