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?"

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.

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.

Abstract: Attendees will learn the foundations of text mining approaches in addition to learn basic text mining scripting functions used in R. The audience will learn what text mining is, then perform primary text mining such as keyword scanning, dendogram and word cloud creation. Later participants will be able to do more sophisticated analysis including polarity, topic modeling and named entity recognition. Bio: Ted Kwartler is the Director of Customer Success at DataRobot where he manages the end-to-end customer journey. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators.

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!".

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The race to build a viable self-driving car is getting a little congested. In a matter of months, the concept went from being a futuristic dream driven mainly by Alphabet Inc.'s Google to an ultra-competitive field populated by traditional automakers and tech companies alike. Ford Motor Co. says it will mass-produce an autonomous vehicle without a steering wheel or pedals by 2021. Uber Technologies Inc. plans to have passengers riding in self-driving cars on the streets of Pittsburgh within weeks. Auto supplier Delphi Automotive PLC is teaming up with Israeli software maker Mobileye to build a self-driving platform that will be sold to automakers by 2019.

Those are some of the fanciful new roles that could be created by the global design industry in the next few years. How will they favor over the next 15 years? Will every company by 2030 have a chief design officer, or will they all go extinct? Should a generation of creatives who grew up worshipping Apple's Jonathan Ive put all their eggs in the industrial design basket? We talked to a dozen design leaders and thinkers from companies such as Frog, Artefact, and Ideo to find out which design jobs could die out in the next 15 years, and which could grow.