Artificial intelligence, machine intelligence, cognitive computing -- whatever you want to call machines that are capable of understanding and acting upon their environment -- is no longer solely the purview of highly credentialed lab directors and deep-thinking computer scientists. It has entered mainstream consciousness, and the public expects IT to play a leadership role as machine learning enters our workplaces, our living spaces and our lives. Chances are that you are not. Most executives, in the opinion of New York Times technology columnist John Markoff, are "ill prepared for this new world in the making." People have been thinking about automated work forever.

Computing giant IBM has always had big plans for its brainy thinking computer named Watson. Since Watson beat human players at the TV game show "Jeopardy" in 2011, the company has given Watson a varied series of job assignments: Cancer researcher, fitness coach, customer service rep. It has even learned Japanese, designed dresses and provided the brain for a talking toy dinosaur. Now Big Blue says it will train Watson to fight cybercrime. The company says its security division will team up with a group of eight universities to teach the cloud-based computer, programmed to learn subjects in a manner similar to the human brain, on the complicated subject of cyber security.

The White House is going to spend the summer researching how the government should deal with artificial intelligence. When you consider that we've got drones flying everywhere, robots automating jobs out of existence, and self-driving cars right around the corner, it's about time. The administration is pitching its new AI research program as an "interagency working group" that will "learn more about the benefits and risks of artificial intelligence," which is a welcome move--researchers in the field have been calling for a "Federal Robotics Commission" for the last couple years, and this at least looks like a small step toward that future. With companies like Google, Uber, and Tesla getting close to wanting to put self-driving cars on the road, drone companies hoping to begin to automate the devices, and AI-driven software taking jobs left and right, it's clear AI is going to have a significant impact on our society. AI is going to continue to make everything a lot more seamless, which is great, but we're also probably going to have to start thinking about things like a basic income for people whose jobs are automated away.

Amazon's digital assistant, the Echo, next to a can of Pringles potato chips, whose size it is often compared to. SAN FRANCISCO -- The smartphone's grip over our every moment may be slowly loosening as digital assistants leap into speakers and other devices that bring the Internet into the public sphere. So far, the field belongs to Amazon's Echo and its voice-activated assistant Alexa. An early hit since it became widely available a year ago, it is still in only a sliver of homes. But its interactions, distinctly different from smartphone use, have caused academics to take notice.

The Microsoft Azure Machine Learning Studio Capabilities Overview diagram gives you a high-level overview of how you can use Machine Learning Studio to develop a predictive analytics model and operationalize it in the Azure cloud. Azure Machine Learning Studio has available a large number of machine learning algorithms, along with modules that help with data input, output, preparation, and visualization. Using these components you can develop a predictive analytics experiment, iterate on it, and use it to train your model. Then with one click you can operationalize your model in the Azure cloud so that it can be used to score new data. This diagram shows how all those pieces fit together.

Traditionally, materials scientists have used a combination of trial-and-error and intuition to discover and perfect new materials with advantageous properties. Increasing chemical complexities make this strategy prohibitively time-consuming. To speed up the process, researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments. The team's "informatics-based adaptive design strategy" successfully accelerated the materials discovery process. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," Turab Lookman, a physicist and materials scientist at Los Alamos, said in a news release.

Troparé has developed the ability for its clients to discover unbiased patterns, insights, and trends in their data; perform thorough analysis; and use this information to make accurate, micro-targeted predictions, all without having to write a single line of code. As a result, marketing and sales departments can attract, retain, and grow their most profitable customers and maximize campaign spending, without having to rely on IT. "Data has become the new life-line of modern day marketing. However, unless marketing departments have the tools to unify data and perform correct analysis on a large scale, simply having enormous amounts of data is useless without tremendous reliance on IT," said Greg Carpenter, CEO of Troparé. The second platform addition, (fuzzy) data matching & appending, is developed as an extension to the industry's widely-acknowledged struggle of data integration. The solution allows customers to easily combine, match, and append their own data with second and/or third party data derived from disparate sources.

If there's one thing the optimization community agree on it's that we know nothing. We all have biases, not least of which is our experience as professional website optimizers. This point was raised again and again during Conversion World conference. So, we need a lot of support to understand our users and optimize their experiences. We try to start the process of optimization from as unbiased a point as possible.

This project shows how to easily integrate Apache Spark, a fast and general purpose engine for large-scale data processing, with Elasticsearch, a real-time distributed search and analytics engine. Spark is an in-memory processing framework and outperforms Hadoop up to a factor of 100. If you are more interested in an Elasticsearch plugin-in that brings the power of Predictiveworks. Predictiveworks. is an ensemble of dedicated predictive engines that covers a wide range of today's analytics requirements from Association Analysis, to Context-Aware Recommendations up to Text Analysis. Besides linguistic and semantic enrichment, for data in a search index there is an increasing demand to apply knowledge discovery and data mining techniques, and even predictive analytics to gain deeper insights into the data and further increase their business value.

Way back in the last century, one of the most common put-downs of computers was the accusation that they only did what they were programmed to do. These days, it is increasingly common for marketing and many other kinds of systems to employ some variety of "machine learning," which moves away from the days when programmers dictated computers' every move. In this article, part of our MarTech Landscape Series, we look at this increasingly popular form of computing intelligence. "Historically," Bluecore CEO and co-founder Fayez Mohamood pointed out, "people wrote programs that were rule-based." His company is an email and marketing personalization platform.