Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

As the skills gap widens for software developers, machine-learning specialists are stepping in with collaboration platforms designed to streamline time-consuming tasks such as tracking down technical solutions when working with new technologies. Collokia, a New York-based startup, announced the beta launch of a machine-learning platform last month that uses artificial intelligence to promote greater collaboration in software development. The other goal is reducing the "technical debt" of development teams by using AI to track down relevant information about a technology project, including information that already resides in company systems. The platform edits and updates search results as a way to disseminate information more widely among development teams. Automation tools that leverage AI promise to address the growing shortfall of qualified software engineers.

Recognizing the central role that technology now plays in the global economy, the World Economic Forum is establishing a new center in San Francisco to connect tech companies and policymakers in the heart of the world's technology industry. Building off the Forum's thesis of a "Fourth Industrial Revolution," the new facility will focus on bringing government officials and tech companies together to create frameworks for more productive legislative policies that can be implemented worldwide. "Depending on the collective choices we make -– as consumers, as communities, as business, government, and civil society leaders -– these technological breakthroughs could give us the power to move into a world that is even more prosperous, while being more sustainable and more inclusive," reads an early version of remarks prepared by World Economic Forum founder and chairman, Klaus Schwab. "Alternatively, we could end up in a world where our economic, political and social systems are more rigid, more unequal and more conflicted." Despite their deep roots in government-funded research, the relationship between policymakers and the tech companies that have sprung from the civic-minded seeds they nurtured with financing has always been a thorny or even openly antagonistic one (cf.

When a sleepy Marc Dubois walked into the cockpit of his own aeroplane, he was confronted with a scene of confusion. The plane was shaking so violently that it was hard to read the instruments. An alarm was alternating between a chirruping trill and an automated voice: "STALL STALL STALL." His junior co-pilots were at the controls. In a calm tone, Captain Dubois asked: "What's happening?" Co-pilot David Robert's answer was less calm. "We completely lost control of the aeroplane, and we don't understand anything! The crew were, in fact, in control of the aeroplane. One simple course of action could have ended the crisis they were facing, and they had not tried it. But David Robert was right on one count: he didn't understand what was happening. As William Langewiesche, a writer and professional pilot, described in an article for Vanity Fair in October 2014, Air France Flight 447 had begun straightforwardly enough – an on-time take-off from Rio de Janeiro at 7.29pm on 31 May 2009, bound ...

When a sleepy Marc Dubois walked into the cockpit of his own aeroplane, he was confronted with a scene of confusion. The plane was shaking so violently that it was hard to read the instruments. An alarm was alternating between a chirruping trill and an automated voice: "STALL STALL STALL." His junior co-pilots were at the controls. In a calm tone, Captain Dubois asked: "What's happening?" Co-pilot David Robert's answer was less calm. "We completely lost control of the aeroplane, and we don't understand anything! The crew were, in fact, in control of the aeroplane. One simple course of action could have ended the crisis they were facing, and they had not tried it. But David Robert was right on one count: he didn't understand what was happening. As William Langewiesche, a writer and professional pilot, described in an article for Vanity Fair in October 2014, Air France Flight 447 had begun straightforwardly enough – an on-time take-off from Rio de Janeiro at 7.29pm on 31 May 2009, bound ...

In a subsequent conversation with Santiago, Dom acknowledges that Darlene is winning the interrogation--her armor is impenetrable enough to keep any confession, no matter how mild, from coming through. But in a fascinating revelation with major implications for the future of the entire series, it turns out that the FBI's game has been rigged all along. Dom takes Darlene into a conference room covered with whiteboards that reveal all the information they've collected in their investigation: The real names, hacker handles, and connections between pretty much every single member of fsociety. The FBI has been playing a long con, letting their suspects operate in the real world while they build a case and wait for them to slip up. The FBI has it all--with one key mistake: They assume Tyrell, not Elliot, is the ultimate mastermind of the 5/9 hack, with Elliot standing as a kind of second-in-command.

Google wants to change how it relates to enterprise customers, and it's going to use Google Cloud to do so. Today at an event in San Francisco, it made announcements about machine learning, Kubernetes, and expansion of its Google Cloud Platform presence. But the bigger-picture news is that cloud will be taking a leading role at the company. Google is bringing together its massive Google Cloud Platform (GCP), along with a new application platform called G Suite (formerly Google Apps). And it's hiring 1,000 people to boost its cloud business. This is all under one big umbrella called Google Cloud.

"There is more talent in Mexico than in Silicon Valley". This is the forceful argument of Andrés Rodríguez, founder of BlueMessaging, a Big Data platform that incorporates artificial intelligence. Rodríguez insists that there is "much innovative talent" in this country and points out that he has worked in Silicon Valley and in Mexico to be able to compare them. According to its management, Bluemessaging is a startup capable of processing millions of requests at the same time and generating savings of up to 50% compared with other platforms or service centers. This startup has been assisted by the Program to Stimulate Innovation (PEI) and the Technological Innovation Fund (FIT), both run by the National Council for Science and Technology (Conacyt), the latter in partnership with the Secretariat of Economy (SE).

I have always been an optimist, almost to a fault. Being this optimistic in the dating world can put you in some regretful situations. You think, "How much worse could this possibly get?" This is a story of a young lady (me) getting an answer to that question. We met on Tinder, talked for a day or two, and then we went out on a Sunday night.

This global network of digital humanitarians used Silk to document who responded to the Ecuador Earthquake emergency. Google has to report all accidents with its self-driving cars. This Silk takes a good look at this data. More data on the world's richest 2000 individuals than ever before: we looked at the data collected by PIIE to answer questions like: Who are we talking about and how much money exactly do the superrich have? What are the sources of all that wealth and how was it accrued?