A Saudi-led coalition has launched air raids on Yemen's Hodeidah, in an apparent resumption of military operations on the strategic Red Sea city after Houthi rebels attacked two Saudi oil tankers and one of the United Arab Emirates' (UAE) main airports. The Houthi-run al-Masirah TV said in a series of tweets on Friday that coalition air strikes had targeted a radio station inside the city and a fishing pier. There were no immediate reports of casualties. The latest offensive on the port city of Hodeidah came a day after Houthi rebels claimed responsibility for a drone attack on Abu Dhabi's international airport. According to the Al-Masirah television channel, the Sammad-3 drone launched three attacks on the airport.

Did you know machine learning is behind a vast of technologies that we use today? Muthoni Wanyoike, the team lead at Instadeep Kenya and an Actuarial Science graduate from Dedan Kimathi University of tech is one of the great brains behind the Women in Machine Learning and Data Science community Nairobi chapter, (WiMLDS) Kenya. "When I started the Nairobi chapter of WiMLDS, I was just starting out in Data Science at the ICT Authority's Kenya Open Data Initiative. I was really looking for a community where I would gain new skills and also connect with people with the same interest as well as those already in the industry," says Muthoni Muthoni, however, admits setting up a community was not easy. At first, they organized meetups where they would charge participants to attend sessions on data science and machine learning with an abundance of Faith that attendees would believe in their vision.

We propose a tensor-based model that fuses a more granular representation of user preferences with the ability to take additional side information into account. The model relies on the concept of ordinal nature of utility, which better corresponds to actual user perception. In addition to that, unlike the majority of hybrid recommenders, the model ties side information directly to collaborative data, which not only addresses the problem of extreme data sparsity, but also allows to naturally exploit patterns in the observed behavior for a more meaningful representation of user intents. We demonstrate the effectiveness of the proposed model on several standard benchmark datasets. The general formulation of the approach imposes no restrictions on the type of observed interactions and makes it potentially applicable for joint modelling of context information along with side data.

The use of facial recognition software is growing in China--s universities, ostensibly to improve security, but concerns are growing that it is used for monitoring students -- including foreing students -- and teachers, creating massive data bases on student attendance and movements around campus. Peking University in Beijing now screens students entering the university--s south-western gate by using a camera to scan their faces in a trial that began at the end of June to see if the technology can replace the use of university identity cards. The system scans through a database of thousands of photographs taken for student and staff identity cards, using a powerful system to match the photograph against a database of thousands of others. Facial recognition devices have already been installed outside the university--s libraries, classrooms, student accommodation, sports facilities and computer centres, but these match a face to an existing photograph of that person on the database rather than sifting through the entire database. Photographs can be retaken in the guard room at the gates if the photos do not quite match, according to the university--s social media account on Sina Weibo, though it does not say what the failure rate is -- in particular for foreign students.

Yemen's Houthi rebels say they attacked Abu Dhabi's international airport in the United Arab Emirates with a drone. According to the Houthi-run Al-Masirah television channel, the Sammad-3 drone launched three attacks on the airport on Thursday. It was not immediately clear if there was any damage or casualties. Abu Dhabi airport tweeted earlier in the day there had been an incident involving a supply vehicle that had not affected operations. It was unclear if it was related to the reported drone attack.

In March 2018, members of the International Monetary Fund's (IMF's) executive board gave their blessing to a dramatic overhaul of the way the organisation gathers, governs and uses data. The Overarching strategy on data and statistics, the first of its kind, lays out how the fund plans to improve the quality of data, boost the ease with which it can be shared, and start making greater use of innovations in big data and artificial intelligence (AI). Key to the strategy is the "global data commons" – an ambitious, cloud‑based platform for gathering large quantities of data from IMF members. The aim is to bring all of the data together in one place in an readily comparable format, making use of common data standards and methodologies. Researchers, journalists and members of the public should no longer be required to trawl through an array of often-labyrinthine websites belonging to national statistics offices and instead be able to access all of the data through a single portal.

LONDON, / CHICAGO – Boffins at U.K. engineering giant Rolls-Royce proudly displayed an array of miniature robots at this year's Farnborough air show, best known as a major marketplace for passenger planes but also a test bed for the aviation industry's wilder imaginings. Designed to speed up engine overhauls, the manufacturer's tiny cockroach-like drones would remove the need for power plants to be detached from aircraft during maintenance work. The "swarming" bots, less than half an inch across, are designed to roam engine turbines in gangs, beaming pictures back to inspection crews after being deposited by so-called "snake" hosts that work their way through the engine. If the bots don't get you the drones will. The biannual air show was awash with unmanned aerial vehicles, or UAVs, ranging from delivery craft that guarantee to gently deposit a parcel by your door to the latest military types intent on blowing stuff up.

Of all corporate functions, sales by its very nature is surely the most people-focused. While it may no longer involve quite as much face-to-face interaction as it once did, selling has remained emphatically a job for people rather than machines. However, artificial intelligence (AI) and machine-learning are already starting to make major inroads into the sales process, adding an extra dimension to everything from marketing automation to customer relationship management. According to Salesforce Research, high-performing teams are at least twice as likely to be using intelligent sales technologies such as artificial intelligence, sentiment analysis, next-step analysis and deep-learning. So, what further changes in the sales environment can we expect to see over the coming years?

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach, and decomposition approach, and then discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing are reviewed to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.

Reprinted with permission from Quanta Magazine's Abstractions blog. Occasionally the machine may rattle reality enough to have a few of those collisions generate something that's never been seen before. But because these events are by their nature a surprise, physicists don't know exactly what to look for. They worry that in the process of winnowing their data from those billions of collisions to a more manageable number, they may be inadvertently deleting evidence for new physics. "We're always afraid we're throwing the baby away with the bathwater," said Kyle Cranmer, a particle physicist at New York University who works with the ATLAS experiment at CERN.