Indian IT (information technology) industry might like to put all the blame on Donald Trump for its woes – single-digit growth, retrenchment and reduced hiring from campuses. But the'Trump effect' masks the crisis that is silently brewing. In a recent presentation to Nasscom, Global advisory firm McKinsey & Company said that nearly half of the workforce in the IT services firms will be "irrelevant" over the next 3-4 years. A similar view was echoed by Capgemini CEO who feels that 60-65 percent of the workforce are just not trainable. According to a study by Horses for Sources, India is likely to lose 640,000 jobs to IT automation by 2021.

The Supreme Court expressed sympathy Tuesday for the family of a Mexican teenager fatally shot from across the U.S.-Mexico line by a Border Patrol agent, but struggled to reach consensus on whether foreign nationals – like the teen's relatives – can sue in American courts. The divisions were on display during oral arguments for what has become a closely watched case, amid an escalating political debate in Washington over border security. The arguments were held the same day the Department of Homeland Security released new directives on immigration enforcement, and after a lower federal court blocked a separate executive action from President Trump on immigration and refugee restrictions. Apart from renewed interest in the court's consideration of immigration disputes, the case heard Tuesday also could have implications for other U.S. government actions taken overseas -- including military drone strikes against suspected terrorists, and electronic surveillance over the Internet. In the current dispute, 15-year-old Sergio Hernandez was just steps from the border on Mexican soil when he was killed in 2010 by Jesus Mesa Jr., an agent standing on the American side in El Paso, Texas. The federal agent was not prosecuted, and the U.S. refused to extradite him to Mexico.

Three months ago, Google announced it would in early 2017 launch support for high-end graphics processing units (GPUs) for machine learning and other specialized workloads. It's now early 2017 and, true to its word, Google today officially made GPUs on the Google Cloud Platform available to developers. As expected, these are Nvidia Tesla K80 GPUs, and developers will be able to attach up to eight of these to any custom Compute Engine machine. These new GPU-based virtual machines are available in three Google data centers: us-east1, asia-east1 and europe-west1. Every K80 core features 2,496 of Nvidia's stream processors with 12 GB of GDDR5 memory (the K80 board features two cores and 24 GB of RAM).

In one of the first such partnerships ever announced in India, Flipkart, India's largest e-commerce portal has joined forces with Microsoft for using their Azure Cloud platform. In an event in Bengaluru, both Microsoft and Flipkart confirmed this major alliance, under which Flipkart will adapt and use Microsoft Azure as their exclusive public cloud platform. This partnership is significant in several ways – both strategically and tactically. But the most interesting question is: Who will be the greater beneficiary from this massive alliance? As per statements issued by Flipkart, Microsoft's Azure platform will add an additional layer of cloud technologies and analytics to their existing data centers. Last year, Flipkart had established their own data centres in Mumbai and Chennai to manage their burgeoning fan base and traffic.

The rapidity of recent Chinese advances in artificial intelligence indicates that the country is capable of keeping pace with, or perhaps even overtaking, the United States in this critical emerging technology. The successes of major Chinese technology companies, notably Baidu Inc., Alibaba Group and Tencent Holding Ltd.--and even a number of start-ups--have demonstrated the dynamism of these private-sector efforts in artificial intelligence. From speech recognition to self-driving cars, Chinese research is cutting edge. Although the military dimension of China's progress in artificial intelligence has remained relatively opaque, there is also relevant research occurring in the People's Liberation Army research institutes and the Chinese defense industry. Evidently, the PLA recognizes the disruptive potential of the varied military applications of artificial intelligence, from unmanned weapons systems to command and control. Looking forward, the PLA anticipates that the advent of artificial intelligence will fundamentally change the character of warfare, ultimately resulting in a transformation from today's "informationized" (信息化) ways of warfare to future "intelligentized" (智能化) warfare.

On a Friday evening, it's fairly easy to guess the downtown hot spots where taxis likely are needed. But most of the time, cab drivers have little more than intuition to go on to find their next fare. That could change for cab drivers in Japan when NTT Docomo commercializes its artificial intelligence technology, which predicts where ride requests will be. Using ridership data from 4,425 cabs, along with other factors such as weather and mobile phone data and locations, NTT Docomo trained its AI system to predict where localized ride demand will be in 30 minutes within a 500-square-meter area. Drivers participating in its trials used tablets that provided updated ridership data every 10 minutes, which gave drivers enough time to reposition their vehicle based on anticipated requests.

Beijing has given the green light for the creation of China's very first'national laboratory for deep learning', in a move that could help the country to surpass the United States in developing artificial intelligence (AI). The National Development and Reform Commission (NDRC) recently approved the plan to set up a national engineering'lab' for researching and implementing deep learning technologies. The lab will not have a physical presence, instead taking the form of a research network predominantly based online. Regarded as one of the most exciting and fastest-growing areas of AI, deep learning - a subdivision of machine learning - involves feeding data through virtual neural networks designed to mimic the human brain's decision-making process, in order to solve problems and recognise images and sounds. It is seen by many as the key to elevating AI to something approximating human intelligence, and is already credited with major breakthroughs in technologies such as voice recognition in smartphones.

Chinese selfie app and smartphone company Meitu has unveiled its newest flagship, and it's all about making you look better. The T8 includes a front-facing camera with optical image stabilization and dual-pixel phase detection autofocus (PDAF) similar to Samsung's Galaxy S7 and the ASUS ZenFone 3 Zoom -- rare components in a selfie camera. It also has a feature called Magical AI Beautification. Like Meitu's popular beauty apps, it can detect your skin tone, age and gender, then touch up your selfie accordingly. According to Meitu, Magical AI Beautification will enhance group photos as well as selfies, detecting and adjusting each face individually.

Not too long ago, online banking and live chat with a bank or credit card representative felt cutting-edge. Now, financial institutions are upping the ante by launching chatbots to interact with customers. Powered by artificial intelligence, these chatbots can quickly answer questions about balances, recent transactions or other queries. In addition to financial chatbots, bots used in other industries serve all kinds of purposes from helping you order a pizza to answering questions on the U.S. immigration website. Banks and credit card issuers hope that bots will help answer consumers' questions in the online environments where they already are -- without having to log in to a website or app each time they want to view recent transactions or check their balance.

Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical structure is automatically determined by the data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of nCRP topic models. However, hLDA has only been evaluated at small scale, because the existing collapsed Gibbs sampling and instantiated weight variational inference algorithms either are not scalable or sacrifice inference quality with mean-field assumptions. Moreover, an efficient distributed implementation of the data structures, such as dynamically growing count matrices and trees, is challenging. In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS) algorithm, which combines the advantages of collapsed and instantiated weight algorithms to achieve good scalability as well as high model quality. An initialization strategy is presented to further improve the model quality. Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy. Empirical studies show that our algorithm is 111 times more efficient than the previous open-source implementation for hLDA, with comparable or even better model quality. Our distributed implementation can extract 1,722 topics from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders of magnitude larger than the previous largest corpus, with 50 machines in 7 hours.