Singapore-based researchers have built an underwater robot that looks and swims like a manta ray, using only single motors and flexible fins to propel it through water in a way uncannily like its biological cousin.

The question before companies today is not whether to become intelligent, it's a question of how and how fast. The key is to adopt and deploy an intelligent application strategy while simultaneously preparing to scale that intelligence. In her session at 21st Cloud Expo, Sangeeta Chakraborty, Chief Customer Officer at Ayasdi, provided a tactical framework to become a truly intelligent enterprise, including how to identify the right applications for AI, how to build a Center of Excellence to operationalize the intelligence and how to implement a strategy to scale efforts. She pulled from her experience helping tackle HSBC's anti-laundering threats and identifying genetic susceptibilities of diseases for Mt. Speaker Bio Sangeeta Chakraborty is the Chief Customer Officer at Ayasdi where she is focused on delivering the highest quality personalized service experience for each of Ayasdi's customers.

The Bombay Stock Exchange and the National Stock Exchange are exploring the opportunity to venture beyond the current businesses and have, along with other market infrastructure companies, sought SEBI's permission to form a separate entity to take on these businesses. "Stock exchanges (SEs) are seeking a one-time regulatory approval to venture into business areas outside SEBI's purview," sources close to the development told BusinessLine. "They have sought SEBI's permission to engage in activities or businesses that are unrelated to, or not identical to, those of an SE or of clearing corporations (CCs) or their core business, through a separate legal entity," they added. The proposal was made to the newly-formed SEBI committee headed by former Reserve Bank of India Deputy Governor R Gandhi, which has been mandated to review norms for stock exchanges, depositories and CCs. The BSE and the NSE did not respond to an e-mail from BusinessLine seeking their views.

Taking the floor at the internet conference in Ujena, CEO of Apple Inc, Tim Cook, said he is worried about not artificial intelligence as such, but people who think like machines, saying technologies will empower us greatly, if in harmony with humanity, as reported by world's media. "Much has been said about the potential negative aspects of artificial intelligence [AI]. I'm not worried about machines that think as people, I worry about people who think like machines. We need to work together to introduce technology to humanity," he said, bringing his words fully in line with the conference's inspiring motto, which is "digital economic development for openness and sharing benefits – building community of common destiny in cyber space." "Technologies can change the world for the better, if they are embedded in humanity. We believe that artificial intelligence will be able to embroider a person's ability and help to make a breakthrough that transforms our lives in education, in access to health services and in countless other areas."

Saudi Arabia recently declared a robot to be a citizen, which shows just how deep the misunderstanding of where we're at with AI really is. Real talk: we don't have an AI that can come close to a human level of intelligence. Personal assistants like Siri, Alexa, Cortana and Google Assistant all try to give the illusion of intelligence, with jokes and all the knowledge of the internet on tap, but there's a lot of smoke and mirrors going on. The jokes are pre-written by humans, and past the speech interface, it's pretty much a dressed-up internet browser. As the AI hype shoots up into the stratosphere, it gives the public an increasingly unrealistic view of the true applications and progress of the research.

We've all heard and read enough--if not actually seen the impact in our day-to-day lives--to figure out that artificial intelligence and machine learning are changing the world as we know it. From driverless cars and automated recruiting processes to connected machines and voice assistants, these developments are rapidly boosting our productivity at home and at work. The late 1990s to early 2000s was the age of the Indian IT sector, which gave rise to an empowered Indian middle class. The IT movement brought the country recognition for identifying and grabbing a global opportunity just by keeping up with industry trends. The sector employs almost 4 million people directly and indirectly. However, of late, the so-called Fourth Industrial Revolution has hit, leaving data and digital-enabled start-ups, young companies and rebooted traditional organisations in its wake.

The Bombay Stock Exchange and the National Stock Exchange are exploring the opportunity to venture beyond the current businesses and have, along with other market infrastructure companies, sought SEBI's permission to form a separate entity to take on these businesses. "Stock exchanges (SEs) are seeking a one-time regulatory approval to venture into business areas outside SEBI's purview," sources close to the development told BusinessLine. "They have sought SEBI's permission to engage in activities or businesses that are unrelated to, or not identical to, those of an SE or of clearing corporations (CCs) or their core business, through a separate legal entity," they added. The proposal was made to the newly-formed SEBI committee headed by former Reserve Bank of India Deputy Governor R Gandhi, which has been mandated to review norms for stock exchanges, depositories and CCs. The BSE and the NSE did not respond to an e-mail from BusinessLine seeking their views.

We investigate the association between musical chords and lyrics by analyzing a large dataset of user-contributed guitar tablatures. Motivated by the idea that the emotional content of chords is reflected in the words used in corresponding lyrics, we analyze associations between lyrics and chord categories. We also examine the usage patterns of chords and lyrics in different musical genres, historical eras, and geographical regions. Our overall results confirms a previously known association between Major chords and positive valence. We also report a wide variation in this association across regions, genres, and eras. Our results suggest possible existence of different emotional associations for other types of chords.

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete one, which we call the coarsening process. To this end, we propose the concept of rank-dependent coarsening, which assumes that incomplete rankings are produced by projecting a full ranking to a random subset of ranks. For a concrete instantiation of our model, in which full rankings are drawn from a Plackett-Luce distribution and observations take the form of pairwise preferences, we study the performance of various rank aggregation methods. In addition to predictive accuracy in the finite sample setting, we address the theoretical question of consistency, by which we mean the ability to recover a target ranking when the sample size goes to infinity, despite a potential bias in the observations caused by the (unknown) coarsening.

Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes to existing code-bases. In this paper, we propose Vprop, a method for Gaussian variational inference that can be implemented with two minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces the memory requirements of Black-Box Variational Inference by half. We derive Vprop using the conjugate-computation variational inference method, and establish its connections to Newton's method, natural-gradient methods, and extended Kalman filters. Overall, this paper presents Vprop as a principled, computationally-efficient, and easy-to-implement method for Bayesian deep learning.