The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of their high clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data. Sketch-SC accelerates the computationally heavy parts of state-of-the-art SC approaches by compressing the data matrix across both dimensions using random projections, thus enabling fast and accurate large-scale SC. Performance analysis as well as extensive numerical tests on real data corroborate the potential of Sketch-SC and its competitive performance relative to state-of-the-art scalable SC approaches.

Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social signals in multi-modal corpora of considerable size. For the involved annotators this defines an exhausting and time-consuming task. In the article at hand we present a novel method and also provide the tools to speed up the coding procedure. To this end, we suggest and evaluate the use of Cooperative Machine Learning (CML) techniques to reduce manual labelling efforts by combining the power of computational capabilities and human intelligence. The proposed CML strategy starts with a small number of labelled instances and concentrates on predicting local parts first. Afterwards, a session-independent classification model is created to finish the remaining parts of the database. Confidence values are computed to guide the manual inspection and correction of the predictions. To bring the proposed approach into application we introduce NOVA - an open-source tool for collaborative and machine-aided annotations. In particular, it gives labellers immediate access to CML strategies and directly provides visual feedback on the results. Our experiments show that the proposed method has the potential to significantly reduce human labelling efforts.

Various technologies at Microsoft that began transforming the lives of individuals and communities in India last year will gain further momentum in 2018, the company's President Anant Maheshwari said on Tuesday. In a mission statement, Maheshwari said he is proud to play a role in the transformation of India's core pillars of education, health care, agriculture and governance, among others. Today, about 25 per cent students drop out from grade 10 to grade 11. "We have partnered with Andhra Pradesh government to see how machine learning can help educators foresee signs of a student being on the risk of dropping out and take preventive action. In 2017, the government expanded the rollout of the programme to all 13 districts in the state," Maheshwari said. Microsoft has also initiated "Project Sangam" that aims to upskill the workforce to prepare it for jobs that exist now and in the future.

Chinese and American tech giants are preparing for a showdown that may shape the future of artificial intelligence. China's cloud providers, Alibaba, Tencent, and Baidu, are getting ready to do battle with US giants Amazon, Google, and Microsoft to deliver AI online. As Chinese companies seek to expand their reach, they may increasingly aim their cloud services at US companies and developers, and vice versa. Speaking at EmTech China, an event held by MIT Technology Review in Beijing, Jian Wang, president of Alibaba's technology committee and a senior figure at the company, predicted that cloud AI would become a major trend. "I'm convinced that AI or machine learning will be the major consumer of applications [in the cloud]," Wang said through a translator.

Microsoft founder Bill Gates, who is believed to be worth around 86 billion, told an audience at the Misk Global Forum that advancements in robotics and artificial intelligence (AI) will help improve efficiency in a range of industries. Speaking at the forum in Riyadh, Saudi Arabia, Mr Gates said: "We are in a world of shortage, but these advances will help us take on all of the top problems." "We need to solve these infectious diseases...We need to help health care workers do their job." The 62-year-old business magnate believes AI will transform the working environment in countries across the world and could eventually be used to help with social care and education. He continued: "As we free labour up from things like manufacturing, we can shift it to some of these very human-centric needs."

Scientists around the world are racing to be the first to develop artificially intelligent algorithms that can see inside our minds. The idea is not new: in the science fiction of the 1950s and 60s, crazed doctors were frequently seen putting weird contraptions on people's heads to decipher their thoughts. British TV serial Quatermass and the Pit -- in which such a machine is used to translate the thoughts of alien invaders -- is a prime example. Now reality is catching up with fantasy. In the past year, AI experts in China, the US and Japan have published research showing that computers can replicate what people are thinking about by using functional magnetic resonance imaging (or fMRI) machines -- which measure brain activity -- linked to deep neural networks, which replicate human brain functions.

Big numbers are nothing new to Quizlet, one of the most widely-used digital study tools in the United States. And according to one website tracker, it is among the most visited sites in the country (besting LinkedIn and Spotify). But there's another number that also has the company excited. This week Quizlet raised an additional $20 million in a Series B round led by Icon Ventures. Other investors include Union Square Ventures, Costanoa Ventures, Owl Ventures and Altos Ventures.

South Korea is deploying a fleet of drone-catching drones to bolster security at this month's 2018 Winter Olympics. Police will use the drones to drop nets on dangerous-looking unmanned aerial vehicles approaching the Olympics grounds in Pyeongchang. The Games, which start Friday, are expected to draw 35,000 spectators, with a host of world leaders including US Vice President Mike Pence set to attend. Officials are concerned that terrorists could use rogue flying machines to disrupt the two-week event by carrying bombs toward crowd members or athletes. South Korea is deploying a fleet of drone-catch drones to bolster its security at this month's 2018 Winter Olympics.

"I have been running during the past six years with full force, and as Sony is going into the next stage, I thought it would be best for me and the company that I pass the baton now," Mr. Hirai, 57 years old, said at a news conference. Mr. Yoshida, 58, inherits a company that is making money in all its major areas, from traditional electronics such as televisions to videogame machines, smartphone game software and movies. That is a shift from the beginning of Mr. Hirai's term when the television business, among others, was churning out losses year after year. Even the movie business, which struggled in recent years, is riding a wave thanks to "Jumanji: Welcome to the Jungle," which was No. 1 at the U.S. box office for several weeks. The stock has risen 63% over the last year.

The UK could be hit hard by the increasing use of machine learning technology over the next few decades, new research has claimed. A report from Google has predicted that the UK economy would see an average negative growth in the years up to 2030 as artificial intelligence and machine learning take over a range of traditional job roles. Governments and policymakers were also warned that major investment needs to be made in skills and education for workers in all industries, ensuring that human productivity continues to rise in tandem with the growth of technology. The report, which evaluated three econometric scenarios affected the UK, US, Australia, Japan and developing Asia regions, also looked at the impact machine learning would have on four major industries, concluding that a number of benefits could emerge. Across transportation, healthcare, energy and manufacturing, machine learning was predicted to have a positive effect, with the report noting that such technology was already commonplace in many areas, with improvements in safety and efficiency highlighted.