While innovation can be categorized in familiar terms -- such as a new product, service, or business model -- we're finding companies are applying analytics and artificial intelligence techniques to the concept of innovation. Organizations we work with are turning to tools such as machine learning to pursue a fast, effective route to innovative results as they seek to stay competitive and top-of-mind for consumers. For our Accenture Technology Vision 2016 report, we surveyed 3,100 business and IT executives in 11 countries. We found that 70% of respondents are making significantly more investments in artificial intelligence technologies than they did in 2013, with 55% stating that they plan on using machine learning and embedded AI solutions extensively. An analytics-driven approach to innovation involves a blend of technological experimentation and cultural collaboration -- bringing together fresh minds and analytics techniques to uncover insights and ideas which can inspire agility, industry disruption and customer loyalty.

On a summer day in 1968, professor Julian Stanley met a brilliant but bored 12-year-old named Joseph Bates. The Baltimore student was so far ahead of his classmates in mathematics that his parents had arranged for him to take a computer-science course at Johns Hopkins University, where Stanley taught. Having leapfrogged ahead of the adults in the class, the child kept himself busy by teaching the FORTRAN programming language to graduate students. Unsure of what to do with Bates, his computer instructor introduced him to Stanley, a researcher well known for his work in psychometrics -- the study of cognitive performance. To discover more about the young prodigy's talent, Stanley gave Bates a battery of tests that included the SAT college-admissions exam, normally taken by university-bound 16- to 18-year-olds in the United States. Bates's score was well above the threshold for admission to Johns Hopkins, and prompted Stanley to search for a local high school that would let the child take advanced mathematics and science classes.

IBM (NYSE: IBM) today revealed a series of new servers designed to help propel cognitive workloads and to drive greater data center efficiency. Featuring a new chip, the Linux-based lineup incorporates innovations from the OpenPOWER community that deliver higher levels of performance and greater computing efficiency than available on any x86-based server. Collaboratively developed with some of the world's leading technology companies, the new Power Systems are uniquely designed to propel artificial intelligence, deep learning, high performance data analytics and other compute-heavy workloads, which can help businesses and cloud service providers save money on data center costs. The three new systems are an expansion of IBM's Linux server portfolio comprised of IBM's specialized line of servers co-developed with fellow members of the OpenPOWER Foundation. The new servers join the Power Systems LC lineup that is designed to outperform x86-based servers on a variety of data-intensive workloads.

LinkedIn has changed the way it generates search results to remove prompts that had asked people who searched for some common female names if they meant to look for similar-sounding male names instead. The professional social networking site has rolled out a change to its search algorithm designed to recognize when a person searches for another user's full name, and doesn't try to prompt them to search for another one, spokeswoman Suzi Owens said in an email. The changes follow a Seattle Times report that found that, in searches for at least a dozen of the most common female names in the U.S., LinkedIn's results included a note asking if users had meant to look for a predominantly male name instead. A search for "Stephanie Williams" brought up a prompt asking if the searcher meant "Stephen Williams." The site similarly suggested changing Andrea to Andrew, Danielle to Daniel, and Michaela to Michael, among others.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

The Pentagon's research agency has a new challenge for scientists: make wireless radios with artificial intelligence that can figure out the most effective, efficient way to use the radio frequency spectrum, and win a pile of cash. Winners of the Defense Advanced Research Projects Agency's (DARPA) Spectrum Collaboration Challenge (SC2) could take home up to 3.5 million, but to do that, teams will have to demonstrate new technologies that represent a "paradigm shift" with both military and commercial applications, said Paul Tilghman, a DARPA program manager who is leading the challenge. "The real crux of the problem is -- when you look at users of the spectrum, whether they are commercial users of the spectrum, whether they're consumers or they're the military -- the thing that is ubiquitously true is we all are placing more and more and more demand on the spectrum, and all of that demand is really adding up and going to stress the way that we actually manage the spectrum," he said. "Where do we put our communications systems? Where do we put our radars? Where do we put our [electronic warfare] systems?"

Could a robot designed as a sexual companion ever feel something like love for me? And could I, as a human with emotional intelligence, ever feel love for it? These questions challenge our definition of love, but they also challenge our understanding of both human emotion and artificial intelligence. Will intimate relationships between humans and robots ever get beyond just sex? This is a topic up for debate at the 12th Human Choice and Computers Conference in Manchester, UK, where academics and researchers are gathering this week to discuss humanity's relationship--sexual, romantic, or otherwise--with our AI counterparts.

I've been writing about the work of Cathy "Mathbabe" O'Neil for years: she's a radical data-scientist with a Harvard PhD in mathematics, who coined the term "Weapons of Math Destruction" to describe the ways that sloppy statistical modeling is punishing millions of people every day, and in more and more cases, destroying lives. Today, O'Neil brings her argument to print, with a fantastic, plainspoken, call to arms called (what else?) Weapons of Math Destruction. Discussions about big data's role in our society tends to focus on algorithms, but the algorithms for handling giant data sets are all well understood and work well. Models are what you get when you feed data to an algorithm and ask it to make predictions. As O'Neil puts it, "Models are opinions embedded in mathematics." Other critical data scientists, like Patrick Ball from the Human Rights Data Analysis Group have located their critique in the same place.

Spark 2.0 has been released since last July but, despite the numerous improvements and new features, several annoyances still remain and can cause headaches, especially in the Spark machine learning APIs. Today we'll have a look at some of them, inspired by a recent answer of mine in a Stack Overflow question (the question was about Spark 1.6 but, as we'll see, the issue remains in Spark 2.0). We'll first try a simple binary classification problem in PySpark using Spark MLlib, but, before doing so, let's have a look at the current status of the machine learning APIs in Spark 2.0. Spark MLlib was the older machine learning API for Spark, intended to be gradually replaced by the newest Spark ML library; in Spark 2.0 this terminology has changed (enough, in my opinion, to cause unnecessary confusion): now the whole machine learning functionality is termed "MLlib", with the old MLlib being the so-called "RDD-based API", and the (old) Spark ML library now termed the "MLlib DataFrame-based API". The oldest RDD-based API has now entered maintenance mode, heading for gradual deprecation.

The second beauty contest, where humans are judged by the robots completes with over six thousand images evaluated by the five robot judges. In addition to the panel of judges from the first contest, Beauty.AI 2.0 featured three new robot judges including: "Average Face" built on the hypothesis that the closer the face is to the average face within the ethnic group, the more attractive it is "AntiAgeist" evaluating the difference between the predicted and actual chronological age "PIMPL" evaluating the number and distribution of pimples and other dark spots (but not freckles) The results were sent to the individual participants via secure link and winners were announced at http://winners2.beauty.ai/#win . The results were surprising, since the consensus scores provided by the robot jury disagreed with human opinion. Tens of participants responded with angry emails criticizing the winners selected by the robot jury. Statements including "what is your "robot" worth??? One walk through a shopping-mall and I will discover more attractive people vs. that ones "won" your Beauty Contest", "If this is how I will be judged in the future, I don't want to see it", "You need human opinion" were among the most pleasant ones with rare positive comments including "this contest is a confidence booster!".