DBS Bank of Singapore's recently unveiled Digibank, what it claimed is India's first mobile-only bank. Digibank brings together an entire suite of technology – from biometrics to artificial intelligence (AI) – to customers and is a completely paperless, signatureless and branchless bank. While most banks are offering banking facilities through mobile, DBS has said digibank has some unique features. Here are some of those features. Wherever they are, whatever their need, digibank customers can converse with digibank's AI-powered virtual assistant to get their queries answered or banking transactions performed.

Software services major Infosys has launched an artificial intelligence platform'Mana' that will help clients drive automation and innovation. The company said that the platform, that brings machine learning together with'deep knowledge of an organisation', will enable businesses to continuously reinvent their system landscapes and lower maintenance cost of assets. Coupled with Aikido service offerings, Mana will help clients capture knowledge while delivering new and delightful experiences to their end users, it said. "Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognised the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way," Infosys CEO and Managing Director Vishal Sikka said at Infosys Confluence 2016.

In an Estimation problem, looking at a data to derive any inference about a'characteristic' of a Population, this approach mainly uses a sample taken at'random' from a collection of these similar items. An'estimate' of that characteristic (also known as a parameter) of the collection (or Universe, Population), is computed from that sample. This estimate is then tested to find out how close it might be to the original parameter, which is usually unknown. Graphical methods such EDA (Exploratory Data Analysis) are also used to study and guess the nature of the characteristic in the population, based on the data from the sample. Sampling is repeated or replicated several times, to reduce the error in the estimate.

At Facebook's recent annual developer conference, Marc Zuckerberg outlined the social network's artificial intelligence (AI) plans to "build systems that are better than people in perception." He then demonstrated an impressive image recognition technology for the blind that can "see" what's going on in a picture and explain it out loud. From programs that help the visually impaired and safety features in cars that detect large animals to auto-organizing untagged photo collections and extracting business insights from socially shared pictures, the benefits of image recognition, or computer vision, are only just beginning to make their way into the world -- but they're doing so with increasing frequency and depth. It's busy enough that the upcoming LDV Vision Summit, an annual conference dedicated to all things visual tech, from VR and cameras to medical imaging and content analysis, is already in its third year. "The advancements in computer vision these days are creating tremendous new opportunities in analyzing images that are exponentially impacting every business vertical, from automotive to advertising to augmented reality," says Evan Nisselson of LDV Capital, which organizes the summit.

Computer vision technology will help you find the best stock images of whatever strikes your fancy. It's in a European city somewhere, with narrow cobblestone streets, and the fence is in front of an old-looking brick building. The bike is shiny and blue, with a basket, sort of old fashioned. You can't see the sky, but you can tell it's a somewhat sunny day. There's no way I could possibly find a picture of a scene like this one on the Internet. Sure, I can type in keywords like "blue bike next to fence in Europe" and it will show me some results that are tangentially related if I'm lucky.

WIRE)--VirtusaPolaris, the market-facing brand of Virtusa Corporation and Polaris Consulting & Services, Ltd. and a leading worldwide provider of information technology (IT) consulting and outsourcing services, and WorkFusion, the leading smart process automation (SPA) provider, today announced a partnership to deliver new smart automation solutions for the banking and financial services (BFS) market. The combination of VirtusaPolaris' deep BFS industry and process expertise and WorkFusion's cutting edge platform will help clients reduce operational costs, while improving quality, productivity and agility. "Most financial services organizations continue to struggle with inefficient legacy systems that have not kept pace with the change in business and regulations, introducing gaps in process automation that negatively impact efficiency of business operations. Many of these gaps are low complexity high volume routine process steps and most organizations have deployed large operational workforces, frequently offshore, to handle these processes," said Bob Graham, global solutions head, Banking and Financial Services at VirtusaPolaris. "WorkFusion's combination of robotic and cognitive automation supported by VirtusaPolaris' expert consulting and implementation services allow customers to improve quality through greater accuracy and the removal of human error, reduce costs through rapid automation of manual tasks, and accelerate time to market with our proven delivery approach."

You will not have to stay home. You will not have to plug in, boot up or log out. You may be in the driver's seat, but who will be driving? Well, first, apologies to the late Gil Scott Heron for butchering his famous poem "The Revolution Will Not Be Televised." In it, Heron talks about a fundamental shift in power from passive observation to active participation in societal change which won't be televised, but will be live.

Dreams are successions of images, ideas, emotions, and sensations that occur usually involuntarily in the mind during certain stages of sleep.[1] The content and purpose of dreams are not definitively understood, though they have been a topic of scientific speculation, as well as a subject of philosophical and religious interest, throughout recorded history. The scientific study of dreams is called oneirology.[2] Dreams mainly occur in the rapid-eye movement (REM) stage of sleep--when brain activity is high and resembles that of being awake. REM sleep is revealed by continuous movements of the eyes during sleep. At times, dreams may occur during other stages of sleep. However, these dreams tend to be much less vivid or memorable.[3] The length of a dream can vary; they may last for a few seconds, or approximately 20–30 minutes.[3] People are more likely to remember the dream if they are awakened during the REM phase. The average person has three to five dreams per night, and some may have up to seven;[4] however, most dreams are immediately or quickly forgotten.[5] Dreams tend to last longer as the night progresses. During a full eight-hour night sleep, most dreams occur in the typical two hours of REM.[6] In modern times, dreams have been seen as a connection to the unconscious mind. They range from normal and ordinary to overly surreal and bizarre. Dreams can have varying natures, such as being frightening, exciting, magical, melancholic, adventurous, or sexual. The events in dreams are generally outside the control of the dreamer, with the exception of lucid dreaming, where the dreamer is self-aware.[7]

The modern data analyst must cope with data encoded in various forms, vectors, matrices, strings, graphs, or more. Consequently, statistical and machine learning models tailored to different data encodings are important. We focus on data encoded as normalized vectors, so that their "direction" is more important than their magnitude. Specifically, we consider high-dimensional vectors that lie either on the surface of the unit hypersphere or on the real projective plane. For such data, we briefly review common mathematical models prevalent in machine learning, while also outlining some technical aspects, software, applications, and open mathematical challenges.

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer. The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$. Our contributions are as follows. We present EXP-3-Transfer, a principled policy-reuse algorithm that optimally reuses a given source policy set when learning for a new MDP. We present a framework to cluster the previous MDPs to extract a source subset. The framework consists of (i) a distance $d_V$ over MDPs to measure policy-based similarity between MDPs; (ii) a cost function $g(\cdot)$ that uses $d_V$ to measure how good a particular clustering is for generating useful source tasks for EXP-3-Transfer and (iii) a provably convergent algorithm, MHAV, for finding the optimal clustering. We validate our algorithms through experiments in a surveillance domain.