Artificial intelligence and robotics are disrupting every aspect of work and redefining productivity. The old ways of not just working, but also assessing capabilities, hiring and compensation, are undergoing a massive change. In a conversation with Knowledge@Wharton, Srikanth Karra, chief human resource officer at Indian IT services firm Mphasis, discusses what this means for individuals, organizations and countries. Karra said managerial jobs and tasks that are repetitive in nature will be displaced and the ability to learn new skills will be critical for individuals who want to stay relevant. Companies will need to devise new ways of training and assessing the skills of employees while countries must develop a learning ecosystem. "Work will be more contractual in nature and deep technical skills, creativity and learnability will be at a premium," he noted.

SOUTHBOROUGH, Mass., – (March 08, 2018)–Virtusa Corporation (NASDAQ GS: VRTU), a global business consulting and IT outsourcing company that accelerates business outcomes for its clients,has been included in Gartner'sMarket Guide for Data Science and Machine Learning Service Providers. The report, published on October 31, 2017, states: "Data and analytics leaders looking for support for their data science and machine learning projects should use this research to identify and engage with candidate service providers to fill the analytics deficit and augment their existing data scientists with specific skills." According to Gartner, "the growing demand for DS&ML as a competitive differentiator is forcing organizations to acquire an even wider portfolio of skilled resources – from statisticians and data scientists, to chief analytics officers. However, there is a shortage of data science skills in the market, making it difficult to source the right skills." "We feel that we are one of the most visible DS&ML service partners worldwide and are proud to be recognized in Gartner's Market Guide," said Kumar Ramamurthy, senior vice president and global head, Data & Analytics, Virtusa.

Artificial intelligence (AI) is changing the landscape of higher education. According to Dr. Keng Siau, artificial intelligence will "perform an array of general tasks with consciousness, sentience and intelligence." That could mean that higher education may no longer be the path to a professional career. University degrees have always led to professional careers; AI may change that path and offer new forms of learning. Ultimately, AI will change the way colleges have approached education.

Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how hidden predictors are linearly combined or the linear mechanism. In traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such an one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. To overcome this difficulty, a two-stage learning (TSL) scheme, featured by learning through deterministic assignment of hidden parameters (LtDaHP) was proposed, which suggests to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. We also present a series of simulations and application examples to support the outperformance of LtDaHP

In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.

This presentation will review how 360º photography is rapidly changing the way DPR Construction documents both existing conditions and ongoing progress on job sites. We will discuss new workflows related to progress documentation and its benefits. For example, we'll cover scheduling of documentation on a weekly and/or milestone basis to enable virtual quality assurance/quality control walks with architects, engineers, and inspectors. We'll also review workflows for capturing conversations that revolve around actual project locations to assist with radio frequency interference (RFI) creation. We will discuss use for risk mitigation, including documenting existing conditions for design planning/bidding, as well as capture of MEP (mechanical, electrical, and plumbing) rough-in before dry-wall and ceiling close up.

If you ask, many people will say we are in a new era of higher education, one where machine learning and big data analytics are driving rapid change. From the influx of adaptive learning technologies to the automated student support services and predictive analytics models driving new interventions, there are fewer spaces of college and university life that are not being touched by these technological innovations. These technological opportunities could offer a lot to higher education. Indeed, if we ignore the opportunities that machine learning and big data analytics might provide to complement our human capacities, we will do a disservice to those we claim to serve -- our students. But if we treat them as an opportunity to downsize the work force or largely replace human social interactions with automated ones, we are going to lose a lot more than we gain.

The Niti Aayog will soon come out with a national policy on artificial intelligence, outlining the scope of research and for the adoption and commercialisation of the technology to counter China's thrust towards AI. The policy is expected to lay out short-, medium- and long-term goals to be achieved by 2022, 2026 and 2030, as India gears up to meet its commitment towards sustainable development goals, starting 2018. Under the policy, deadlines for commercial rollout of AI may also be proposed in areas like agriculture, health, education, banking, retail and transportation. It may also suggest incentives for startups and venture capital funds that undertake research and adoption of AI. These guidelines would be based on half a dozen pilots the Aayog has undertaken on AI in areas of agriculture, health, education and creation of other social infrastructure.

NEW YORK – For a dozen students from Futaba Future High School in Fukushima Prefecture, a recent visit to the United Nations was a chance to share their plans to improve the lives of others by drawing from their catastrophic earthquake and tsunami experiences as a source of strength. Despite overcoming enormous hurdles in the aftermath of the March 11, 2011, disaster that took more than 19,000 lives, the surviving students have moved forward with aspirations of choosing future paths to benefit the global community. "Thanks to all my experiences like getting bullied, joining the drama club and studying at my high school, I think I could grow well," Satsuki Sekine told U.N. diplomats, staff and youth representatives who gathered to hear their presentation on the current situation in Fukushima early this month as part of a scheduled visit while in New York. The 17-year-old explained how drama can be used to portray the challenges of discrimination and conflict "not as an abstract concept but with specific and visual examples." Recounting how the tsunami rendered her home unlivable, she explained how her life in Tomioka as a normal 9-year-old was turned upside down.

Long-term observation of changes in Activities of Daily Living (ADL) is important for assisting older people to stay active longer by preventing aging-associated diseases such as disuse syndrome. Previous studies have proposed a number of ways to detect the state of a person using a single type of sensor data. However, for recognizing more complicated state, properly integrating multiple sensor data is essential, but the technology remains a challenge. In addition, previous methods lack abilities to deal with misclassified data unknown at the training phase. In this paper, we propose an architecture for multimodal sensor-based ADL recognition which spontaneously acquires knowledge from data of unknown label type. Evaluation experiments are conducted to test the architecture's abilities to recognize ADL and construct data-driven reactive planning by integrating three types of dataflows, acquire new concepts, and expand existing concepts semi-autonomously and in real time. By adding extension plugins to Fluentd, we expended its functions and developed an extended model, Fluentd++. The results of the evaluation experiments indicate that the architecture is able to achieve the above required functions satisfactorily.