Finally, the Task Force on Artificial Intelligence (AI) set up by the government of India has submitted its preliminary recommendations, pending a detailed report. The set of recommendations, submitted last week, has raised many eyebrows as to the extent of work done by a group of experts drawn from across the academia, government and industry, with respect to the time consumed and the outcome, notwithstanding the delay by the government in setting up a committee to advise a strategy. While many countries driven by technology have put their act together as early as 2016 to capitalise on the development and focused their energies to make benefits for their countries, India had a slow start by the middle of last year and set up a committee of 19 experts. By then, most countries that have understood the importance of the new technology have progressed to a good extent to be ahead of the curve by working on to: 1) evolve a strategy to nurture, promote and innovate the technology, and 2) regulate the technology before it overpowers the growth of the economy in any unwarranted manner. Most of these countries that ride on technologies have come up with strategies to deal with the emergence of AI by the middle of 2017.

Tech giant Microsoft will host'AI for All' summit in Bengaluru on March 28 to showcase use cases of artificial intelligence across sectors. The event will feature discussions around the benefits of artificial intelligence (AI), and how this can be used to amplify human ingenuity, a Microsoft India spokesperson told PTI. "This is our first summit of its kind in the country. While we had an AI event last year for developers, this one is for a broader audience," the spokesperson added. Participation is expected from industry representatives spanning across verticals like healthcare, auto and IT and IT-enabled services, among others. The event will be addressed by global and Indian Microsoft executives, including Peggy Johnson (Executive Vice President, Business Development), Anant Maheshwari (President, Microsoft India) and Anil Bhansali, Managing Director at Microsoft India (R&D).

Tech giant Microsoft will host'AI for All' summit in Bengaluru on March 28 to showcase use cases of artificial intelligence across sectors. The event will feature discussions around the benefits of artificial intelligence (AI), and how this can be used to amplify human ingenuity, a Microsoft India spokesperson told PTI. "This is our first summit of its kind in the country. While we had an AI event last year for developers, this one is for a broader audience," the spokesperson added. Participation is expected from industry representatives spanning across verticals like healthcare, auto and IT and IT-enabled services, among others. The event will be addressed by global and Indian Microsoft executives, including Peggy Johnson (Executive Vice President, Business Development), Anant Maheshwari (President, Microsoft India) and Anil Bhansali, Managing Director at Microsoft India (R&D).

India must take a leaf from China's book on improving healthcare. China, which, in 2015, had 3.6 physicians for every 1,000 population is deploying artificial intelligence (AI) in a big way to make up with automation what it lacks in manpower in healthcare. An article in the MIT Technology Review (MTR) talks of how it is easing work in many areas of healthcare, from diagnostics to dentistry--in fact, the Chinese government has made computerised medical diagnosis one of the cornerstones of its grand plan to embrace AI by 2020. While over 130 companies are already working on AI applications in the country's healthcare sector, the IDC estimate of a $930-million market in China for AI-led healthcare services by 2022 means more of such companies are expected to come online. China's homegrown tech giants, Alibaba and Tencent, have also placed significant bets on AI diagnostic tools.

Artificial intelligence has the potential to improve people's lives in profound ways -- from helping diagnose diseases and breaking down language barriers to making businesses more efficient. Google believes that AI will help tackle huge challenges like healthcare, environmental protection and other social and developmental problems, while also spurring innovation for businesses and developers. The opportunity is huge and not constrained by location – a company in Bangalore or Gurgaon could serve the whole world. In fact, a recent report by Accenture concluded that India, by embracing AI technologies could add nearly $1 trillion to its GDP by 2035. India already has some of the key ingredients to becoming a major force in leading the next generation of disruptive innovation in machine learning (ML): a tech-savvy talent pool, renowned universities, healthy levels of entrepreneurship and strong corporations.

New Delhi, Mar 25 (ANI): Union Minister for Railways and Coal Piyush Goyal, while addressing an Artificial Intelligence (AI) Conference on Saturday, said the AI is not something to be feared instead it must be harnessed for the benefit of various sectors, including railways. "Artificial Intelligence has to be harnessed to find digital innovations for better customer interface and better service delivery. Artificial Intelligence is about creating trains with brains," said Goyal. "Artificial Intelligence can transform Indian Railways in terms of safety, passenger amenities, better revenues, growth and efficiency," he added.

Japanese brokerages are launching a consortium dedicated to driving the adoption of blockchain and other innovative technologies in the industry, with the goal of boosting efficiency and providing more convenient services to their customers. The group's 18 founding members include online players like SBI Securities and Rakuten Securities, as well as conventional brokerages like Nomura Securities and Daiwa Securities. SBI Holdings unit SBI Ripple Asia will be the lead organizer for the group. It will hold one or two working meetings a month, as well as carry out proof-of-concept tests on cutting-edge technologies. The group aims to cut costs through industry-wide cooperation. Specifically, the consortium is looking into a shared log-in mechanism for brokerage accounts for customers that uses biometrics and other personal identification information, as well as using artificial intelligence to screen trading activity.

Consider users -- perhaps from the research, intelligence, or recruiting community -- who seek to explore graphical data -- perhaps knowledge graphs or social networks. If the graph is small, it is reasonable for these users to directly explore the data by examining nodes and traversing edges. For larger graphs, or for graphs with noisy edges, it rapidly becomes necessary to algorithmically aid users. The problems that arise in this setting are essentially those of information retrieval and recommendation for graphical data, and are well studied [Hasan and Zaki2011, Blanco et al.2013]: identifying the most important edges, predicting links that do not exist, and the like. The responsiveness of these retrieval systems is critical [Gray and Boehm-Davis2000], and has driven numerous system designs in both hardware [Hong et al.2011] and software [Low et al.2014].

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVMs in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We present the first approximation named ESP+SFM for alignment kernels by leveraging a metric embedding named edit-sensitive parsing (ESP) and space-efficient feature maps (SFM) for random Fourier features (RFF) for large-scale string analyses. Input strings are projected into vectors of RFF by leveraging ESP and SFM. Then, SVMs are trained on the projected vectors, which enables to significantly improve the scalability of alignment kernels while preserving their prediction accuracies. We experimentally test ESP+ SFM on its ability to learn SVMs for large-scale string classifications with various massive string data, and we demonstrate the superior performance of ESP+SFM with respect to prediction accuracy, scalability and computation efficiency.