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."

Machine learning and the holy grail of anomaly detection are getting a lot of attention from investors and businesses at the moment. A subset of artificial intelligence, machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. To detect an anomaly, you first have to determine what is normal and this is easier said than done for the majority of businesses. Anomaly detection requires an organisation to define roles and responsibilities and to put in place robust identity and access controls – all of which pose the question: if this was defined and in place, would I need anomaly detection anyway? There is huge scope for machine learning to become effective, simply because of the way we can now collate, store and analyse data with new business dynamic SMAC (Social, Media, Analytics and Cloud). Machine learning is now being used for assigning hospital beds to root cause analysis for quality improvements and for advanced marketing activities to personalise the consumer shopping experience.

Taking a break from the tradition where Google founders Larry Page and Sergey Brin shared the company's progress and vision every year, this time it was Indian-origin CEO Sundar Pichai who updated the world with some of Google's achievements and key highlights. In a letter posted on official Google blog on Friday, Pichai reiterated "to organise the world's information and make it universally accessible and useful". Touching upon artificial intelligence (AI), powerful computing platforms and cloud, he stressed that mobile phone has become the remote control for daily lives and people are communicating, consuming, educating and entertaining themselves on smartphones "in ways unimaginable just a few years ago". "Search -- the very core of Google, comes from mobile and an increasing number of them via voice. The company made this easy and via Google Now, user can get information like the weather in your upcoming vacation spot," he posted.

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.

You probably have read about it. Being a man of action he is doing something about it and has founded, together with other folks a non profit organization called Open AI. Open AI has assembled some of the best talent in the Deep Learning community. They went to Joshua Benglio, who is the only one of the Deep Learning "founding fathers" not working for a software company, and asked him to name a list of the best talent working on Deep Learning. They made offers to 10 of them and 9 accepted to join the effort.

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]

I am highlighting several recent papers that show the potential of residual neural networks. Residual neural networks, or ResNets (Deep Residual Learning for Image Recognition), are a technique Microsoft introduced in 2015. The ResNet technique allows deeper neural networks to be effectively trained. ResNets won the ImageNet competition in December with a 3.57% error score. Recently, researchers have published several papers augmenting the ResNet model with some interesting improvements.

SpaceX and Tesla Motors boss Elon Musk has open-sourced OpenAI Gym, which is a kind of training gym for artificial intelligence programs. The virtual gym is created to help computer programmers improve their AI systems. The gym is under Musk's OpenAI, an artificial intelligence research organization supported by over 1 billion in commitments. OpenAI is Elon Musk's nonprofit dedicated to releasing cutting-edge AI research for free. It is also backed by other Silicon Valley heavies, including LinkedIn's Reid Hoffman, Y-Combinator founders Jessica Livingston and Sam Altman, PayPal cofounder Peter Thiel and Stripe's Greg Brockman.

And that's probably the reason he's into the Google service that automatically generates replies to incoming messages. Smart Reply, as my editor will tell you, is pretty smart (It is!--Ed.). Having analyzed millions of messages from across Google's Gmail service, it can guess how you might respond to a particular missive. That may sound impersonal, but it's useful. It lets you instantly reply to someone when you don't have time to open a laptop or even tap out a message on your smartphone.

Machine learning makes sentiment analysis more convenient. This post would introduce how to do sentiment analysis with machine learning using R. In the landscape of R, the sentiment R package and the more general text mining package have been well developed by Timothy P. Jurka. You can check out the sentiment package and the fantastic RTextTools package. Actually, Timothy also writes an maxent package for low-memory multinomial logistic regression (also known as maximum entropy).