In an ongoing effort to extract more useful information and insights from massive volumes of structured and unstructured data, many organizations have turned to cloud based Hadoop big data analytics solutions such as Qubole. And as effective as these solutions are at capturing and analyzing large data volumes, their ability to interact with powerful Business Intelligence (BI) tools such as Machine Learning Libraries (MLL), is taking big data analytics capabilities to a whole new level. What follows is a look at 5 Machine Learning Libraries and the Big Data use case for each. MLlib features a host of common algorithms and data types, all designed to run at speed and scale. This makes MLlib a good fit for network security and other use cases such as predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis.

There have been an increasing number of reports about the rapidly developing world of AI, where computers can learn to use big data independently. Most recently, Google yesterday announced it would begin exploring whether automated machines are capable of creativity and producing original artwork and music. Many people's gut reaction to this is that nothing, not even the innately human ability to create, is safe from the machines. It makes me think of the "infinite monkey theorem": give a monkey a typewriter and an infinite amount of time and eventually, it will type something that makes sense. So, if we give computers (AI) huge amounts of big data and time, they will eventually develop creative ideas that can compete with the likes being dreamt up by skilled creatives and brilliant agencies all over the world?

Inc.'s 11th annual 30 Under 30 list features the young founders taking on some of the world's biggest challenges. In 2013, a high-frequency trader named Michael Coscia was arrested in New Jersey for an activity called "spoofing"--essentially manipulating the market by flooding trading systems with future orders he had no intention of completing. He was fined 6 million--with the possibility of jail time. It was the first such prosecution under a new set of financial regulations from the 2010 banking reform law called the Dodd-Frank Act. That was an aha! moment for David Widerhorn, 28, and it became his reason for founding Neurensic.

In a future when cars no longer need humans to drive, choices about who might live or die in a crash are already being made -- by the so-called "moral codes" that are preprogrammed into a car's neurology. Like humans, autonomous cars make countless tiny decisions while navigating the complexities of street traffic. But instead of a brain, driverless cars rely on a preprogrammed set of parameters to decide whether to brake, turn or accelerate. "Suppose we have some [trouble-making] teenagers, and they see an autonomous vehicle, they drive right at it. They know the autonomous vehicle will swerve off the road and go off a cliff," said Keith Abney, an associate professor of philosophy at California Polytechnic State University.

This blog was originally posted as part of our Text Analysis 101 blog series. It aims to explain how the classification of text works as part of Natural Language Processing. The automatic classification of documents is an example of how Machine Learning (ML) and Natural Language Processing (NLP) can be leveraged to enable machines to better understand human language. By classifying text, we are aiming to assign one or more classes or categories to a document or piece of text, making it easier to manage and sort the documents. Manually categorizing and grouping text sources can be extremely laborious and time-consuming, especially for publishers, news sites, blogs or anyone who deals with a lot of content.

Right now, Terrapattern only covers four American cities: Pittsburgh, Detroit, San Francisco, and New York City. Terrapattern is so computing-hungry that it is effectively a proof of concept right now, at least for a team of artists working with less than 35,000. Each metro region takes about 10 gigabytes of RAM--not storage, but active memory. That said, Terrapattern is relatively technically straightforward. It's constructed from a convolutional neural network and CoverTree, an algorithm that remembers some descriptions and allows the searches to happen quickly.

MINDLER was founded in July, 2015 by Prateek Bhargava along with his mentor and career coach, Prikshit Dhanda. The organisation is based in Punjabi Bagh in New Delhi. MINDLER is a technology-enabled eco-system for career planning, development and mentoring for school students (class VIII-XII). The startup blends artificial intelligence and machine learning with strategic human interventions to help students and parents choose the best-suited career path. MINDLER's distinctive feature comes in the form of a 5 step assessment process - world's most advanced multi dimensional career assessment battery, algorithm driven semi-automated career planner & tracker and course correction mechanism.

I recently caught up with Fernando Lucini, Chief Technology Officer, Big Data Hewlett-Packard Enterprise, to discuss the future of machine learning at HPE Big Data. Fernando leads HPE IDOL software portfolio and is the global business leader for the Haven OnDemand platform, as part of the Big Data Platform business group in HPE Software. HPE IDOL is the industry's leading augmented intelligence software for human information and the Haven OnDemand platform powers a new generation of applications with machine learning APIs and Services. He has a deep technical background, and has been critical in the launch of many products throughout his career, spanning enterprise search, rich media analytics, and compliance, among many others. He holds a BEng Hons in Communications and Electronic Engineering from the University of Kent, and an MBA from IE Business School, Madrid.

Similar to word2vec's skipgram negative-sampling (SGNS) algorithm, we'll start by trying to discriminate pairs of (context j, word i) that appear in the corpus from those randomly sampled from a'negative' pool of words and contexts.

Artificial Intelligence (AI) promises everything from self-driving cars to self-writing newspapers, but AI may be missing its greatest opportunity in healthcare, where AI-driven "conversational interfaces" hold untapped potential to influence the health and wellbeing of billions of people. Fueled by the massive popularity of messaging platforms such as WhatsApp, "conversational UI" is providing an emerging generation of chat-based digital services that may be the next thing in consumer technology. Instead of manipulating a graphical interface, users have a conversation with a chat-bot: software that is able to understand and respond to natural language inputs. The pace of technical advances combined with a shift in cultural norms is making AI conversations feel normal for increasing numbers of people. The idea of a "computer you can talk to" has captured the imagination of the computer science community, and the general public, for decades.