The point is, there are too many people asking what AI will transform, and not enough asking how. There may be no greater example than in medicine. AI has remarkable promise for the industry. Done right, even basic machine learning could transform how doctors work, making them smarter, more efficient, and less error-prone. Yet doctors themselves, while eager to try out the newest procedure or medicine, typically remain dead set against a machine telling them what to do.

Most European enterprises (92%) expect to adopt robotic process automation (RPA) technology by 2020. A survey of 500 European businesses by ISG found the core drivers to be improving customer experience and streamlining the internal finance operations. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Reliance has been going all out when it comes to AI and its implications for the future. Under the leadership of Akash Ambani, Reliance is making strides in AI this year. The young leader is hiring the brightest minds in the country to create an area of excellence that can impact the telecom market in a huge way. The team at Reliance is also looking at applications under machine learning and blockchain so that the company can benefit from its potential. The company is taking advantage of the multi-billion-dollar opportunity that lies in the AI space in India.

We describe a novel weakly supervised deep learning framework that combines both the discriminative and generative models to learn meaningful representation in the multiple instance learning (MIL) setting. MIL is a weakly supervised learning problem where labels are associated with groups of instances (referred as bags) instead of individual instances. To address the essential challenge in MIL problems raised from the uncertainty of positive instances label, we use a discriminative model regularized by variational autoencoders (VAEs) to maximize the differences between latent representations of all instances and negative instances. As a result, the hidden layer of the variational autoencoder learns meaningful representation. This representation can effectively be used for MIL problems as illustrated by better performance on the standard benchmark datasets comparing to the state-of-the-art approaches. More importantly, unlike most related studies, the proposed framework can be easily scaled to large dataset problems, as illustrated by the audio event detection and segmentation task. Visualization also confirms the effectiveness of the latent representation in discriminating positive and negative classes.

Indian banks, including state-owned SBI and Bank of Baroda, have started deploying artificial intelligence (AI) in a big way to improve efficiency, detect human behaviour and reduce operational costs. State Bank of India, the India's largest lender, has SBI Intelligent Assistant (SIA) -- a smart chat assistant, evolved from the "cutting edge technology of artificial intelligence, that efficiently resolves queries of NRI customers, similar to that of a bank representative. "It provides instant solutions on everyday banking queries in the chat box on the SBI portal," the state-run lender said. The bank is also in the process of instituting an'Innovation Centre' that will explore how emerging technologies such as AI and Robotic Process Automation (RPA) can help in making internal banking processes more efficient. Another state lender Bank of Baroda has evolved an innovative concept by setting up of hi-tech digital branch equipped with advanced gadgets like artificial intelligence robot named Baroda Brainy and Digital Lab with free Wi-Fi services. Private sector banks too are using the advance innovative technology for improving workforce productivity and enhancing the customer experience. "The Indian banking industry is on a rapid digital journey and has been adopting technologies like artificial intelligence and machine learning which will reshape the future.

Somewhere in the spare room of a home in the US or overseas, a recent high school grad and soon to be member of the Class of 2022 at a school like Stanford or Georgia Tech is gathering up the clothes, gadgets, and dorm room basics that they will need for freshman year. Four years after that traumatic (for the parents) drop-off day, that skinny but brilliant freshman will graduate and join a six-person start-up company, where he or she will play an integral part in building the game-changing technology that you and your organization will use just a few years later. We can't tell you which freshmen are on the way to cashing out their stock options or which companies they will work for, but we can get a glimpse of key information technologies that might be available in the somewhat shorter term. Some of the companies and technologies that will be in the must-have category are pulling in venture capital investments today. This is the first in an occasional series of roundups of just a few of the noteworthy startup reporting investments.

The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP), a well-known non-parametric and interpretable Bayesian model, which suffers from cubic complexity to training size. To improve the scalability while retaining the desirable prediction quality, a variety of scalable GPs have been presented. But they have not yet been comprehensively reviewed and discussed in a unifying way in order to be well understood by both academia and industry. To this end, this paper devotes to reviewing state-of-the-art scalable GPs involving two main categories: global approximations which distillate the entire data and local approximations which divide the data for subspace learning. Particularly, for global approximations, we mainly focus on sparse approximations comprising prior approximations which modify the prior but perform exact inference, and posterior approximations which retain exact prior but perform approximate inference; for local approximations, we highlight the mixture/product of experts that conducts model averaging from multiple local experts to boost predictions. To present a complete review, recent advances for improving the scalability and model capability of scalable GPs are reviewed. Finally, the extensions and open issues regarding the implementation of scalable GPs in various scenarios are reviewed and discussed to inspire novel ideas for future research avenues.

Your responses to the questions in this survey are completely voluntary and any information you share with us is confidential. Feel free to skip questions you do not want to answer. The material may be used in an upcoming article in C&EN. No one will contact you about your answers, unless you specifically give permission for contact. Your data will be properly disposed of once the article is published.**

Predicting potential credit default accounts in advance is challenging. Traditional statistical techniques typically cannot handle large amounts of data and the dynamic nature of fraud and humans. To tackle this problem, recent research has focused on artificial and computational intelligence based approaches. In this work, we present and validate a heuristic approach to mine potential default accounts in advance where a risk probability is precomputed from all previous data and the risk probability for recent transactions are computed as soon they happen. Beside our heuristic approach, we also apply a recently proposed machine learning approach that has not been applied previously on our targeted dataset [15]. As a result, we find that these applied approaches outperform existing state-of-the-art approaches.

NEW DELHI: Indian banks, including state-owned SBI and Bank of Baroda, have started deploying artificial intelligence (AI) in a big way to improve efficiency, detect human behaviour and reduce operational costs. State Bank of India, the India's largest lender, has SBI Intelligent Assistant (SIA) -- a smart chat assistant, evolved from the "cutting edge technology of artificial intelligence, that efficiently resolves queries of NRI customers, similar to that of a bank representative. "It provides instant solutions on everyday banking queries in the chat box on the SBI portal," the state-run lender said. The bank is also in the process of instituting an'Innovation Centre' that will explore how emerging technologies such as AI and Robotic Process Automation (RPA) can help in making internal banking processes more efficient. Another state lender Bank of Baroda has evolved an innovative concept by setting up of hi-tech digital branch equipped with advanced gadgets like artificial intelligence robot named Baroda Brainy and Digital Lab with free Wi-Fi services. Private sector banks too are using the advance innovative technology for improving workforce productivity and enhancing the customer experience. "The Indian banking industry is on a rapid digital journey and has been adopting technologies like artificial intelligence and machine learning which will reshape the future.