The global healthcare Natural Language Processing (NLP) market is expected to grow from 1.10 billion in 2015 to 2.67 billion by 2020, according to a new report. "Natural Language Processing Market for Health Care and Life Sciences Industry by Type (Rule-Based, Statistical, and Hybrid NLP Solutions) – Worldwide Forecast and Analysis to 2015 – 2020" is published by MarketsandMarkets, The explosive growth in healthcare and life sciences industries, with their vast troves of unstructured clinical data in EHRs, are the main market drivers. As the report describes it, NLP technologies assist machines in understanding the language used by humans to communicate both reading and writing. This form of communication assists the computer in performing various other additional tasks. NLP techniques extract important information from the vast amount of clinical data and analyze it for enhanced processing and analytics.

This article focuses not what the athletes are putting into their bodies, but what they are putting on their bodies and shows how technology affects gears used by them. An hour long lecture by Demis Hassabis, the CEO of DeepMind, where he discusses what is happening at the cutting edge of AI research, including the recent historic AlphaGo match, and its future potential impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind. Here, Margaret Boden, a Professor of cognitive science at the University of Sussex, examines what it means to be "creative" and whether we can ever translate this into our computers. Steven Pinker believes there's some interesting gender psychology at play when it comes to the robopocalypse. Could artificial intelligence become evil or are alpha male scientists just projecting?

Five years ago, Tim Cook officially took over as CEO of Apple amid questions about his ability to live up to the legacy of Steve Jobs. This week, as Cook celebrates his fifth anniversary as head of the company (the exact date is Wednesday), questions continue to swirl. When Jobs officially stepped down from the top spot, he said he believed "Apple's brightest and most innovative days are ahead of it." Cook's track record leaves you scratching your head. Yes, Cook has had some big wins.

Pop culture's many takes on artificial intelligence New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

Enrique Zarate, 19, had spent just a year in college when he landed an apprenticeship at a new BMW facility in San Luis Potosí, Mexico. If he performs well, in a year he'll win a well-paid position, with benefits, working with robots at the company's newest plant. Within a decade or so, most of the BMW 3 series cars that Americans buy will probably come from Mexico, built by people like Zarate. "When you start with such little experience, and get such a big salary, it's unbelievable," says Zarate, whose father is a taxi driver and whose mother is a housewife. Exports from Mexican factories have jumped 13% since 2012.

Google is pitching its artificial-intelligence software to commercial customers in a bid to catch rivals in the increasingly lucrative business of renting its computer servers to other companies. Google is making a renewed push into the cloud business, where it spent much of its 10 billion in capital expenditures last year to build new data centers and tapped Diane Greene, a high-profile Silicon Valley executive, to run the business. A core part of Google's cloud strategy is artificial intelligence. Wednesday, Google said it would start letting cloud customers tap into two software programs it has used internally to draw meaning from text and convert speech to text. The programs use so-called machine learning, a rapidly accelerating technology that enables computers to make inferences based on data they previously had analyzed.

Even a decade ago, talking to your computer was probably a sign that you'd been working too hard and could do with a lie down. Today, no such stigma applies. That's because chatbots -- the conversational agents capable of simulating intelligent conversations with human users -- have made some massive leaps forward. From changing the way kids learn in schools to picking you out the perfect meal this evening, here are the seven of the most interesting chatbots doing the rounds at the moment. From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn.

Pop culture's many takes on artificial intelligence New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Editor's note: You may want to check out part 1 of this tutorial before proceeding. In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/). In machine learning (ML) literature, we come across the term'training' very often, let us literally look at what that means in TF. The goal in linear regression is to find W, b, such that given any feature value (x), we can find the prediction (y) by substituting W, x, b values into the model. However to find W, b that can give accurate predictions, we need to'train' the model using available data (the multiple pairs of actual feature (x), and actual outcome (y_), note the underscore).