The biannual Launch CMU spotlights Carnegie Mellon students, graduates, and professors who combine expertise in cognitive science, computer science, artificial intelligence, educational psychology, and a large dose of creativity to create innovative advancements in education. Because the research and tech showcase focuses on entrepreneurship, the event included demos by various startups, including Expii, billed as a "community-built textbook that thinks like you do." According to its website, Expii makes it easy to create and rate interactive lessons, which simulate one-on-one tutoring experiences. Think rapid-fire question and answer, video, and more. Po-Shen Loh, a Carnegie Mellon math professor and founder of Expii, said his objective is to multiply the number of people who love math and science by 10. "We are creating a product of high value with very low variable and fixed costs, and we charge zero for it. Our team combines expert talent from math, science, and education. As math and science enthusiasts ourselves, we're uniting the enthusiast community to create its unique solution to education," Loh said.

Academia is supposed to be a place where creative types can be free, and with that freedom accomplish great things, whether it be new art, breakthrough treatises, scientific discoveries, or feats of engineering. But academia isn't what it used to be, and to provide some insights into some of its problems, I compared notes with friend and former colleague, Nick Cassimatis, who is associate professor in the Department of Cognitive Science at Rensselaer. In our own ways, he and I have found severe limitations in academia today, limitations that led to my leaving academia to co-found a research institute, 2AI to be funded by intellectual property, and that led Nick to start his own company outside academia, SkyPhrase in order to achieve his ambitions in artificial intelligence. Nick's romantic ambitions started early – he began research into artificial intelligence and natural language at the precocious age of fifteen, and wrote a French-to-English translation program that helped put him on the Top-20 High School Students List by USA Today. More than simply artificial intelligence, his aim is to understand human-level intelligence, and how it can come about via many unintelligent parts.

In recent years, there's been no shortage of criticism that employers aren't providing enough of the training their workforces need to compete in the information economy. So here's some more fuel for this particular fire: executives report they'll probably need to "train" machines as much as they need to train people. A new survey of 2,000 business and IT executives from Accenture finds that more than three-fourths, 77 percent, believe that within three years, they will need to focus on training their machines as much as they do on training their employees. This kind of "training" involves the use of intelligent software, algorithms and machine learning. The same number of executives say they expect employees and intelligent machines to increasingly work side by side, in a collaborative way.

On a recent visit to the doctor, Edward Feigenbaum had the eerie experience of seeing one of his inventions used in a way he never expected: His 25-year-old concept was being used to diagnose a problem with his own breathing. "It's using artificial intelligence," the doctor patiently explained about the spirometer, which measures airflow. A professor of computer science and co-scientific director of the Knowledge Systems Laboratory at Stanford University, Feigenbaum is a pioneer of artificial intelligence (AI) -- the science of making machines think like humans. Dozens of applications have their roots in the Stanford lab he started in 1965 and in related software programs that solve complex problems the same way human experts do. Feigenbaum was the first person to realize that human intelligence springs not from rules of logic but from knowledge about particular problems (whether it's chemistry or auto mechanics) and about the world in general.

While consumers are marveling at this year's new tablets and smartphones, researchers are hard at work developing the next wave of computer technologies that will change our lives. For starters, parallel computing is yielding extraordinary results. The once-experimental technology that allows computers to process multiple problems simultaneously is being used to create new breakthroughs in graphics rendering, language translation and even facial recognition. Berkeley Par Lab research professor Kurt Keutzer predicts that parallel computing will foster enormous advances in speed and power for every kind of electronics, from videogame consoles to handheld devices. In addition to producing more powerful machines, new research is making it easier to work with computers.

What's the best way to prove you "know" something? A. Multiple choice tests B. Essays C. Interviews D. None of the above Go ahead: argue with the premise of the question. Oh yeah, you can't do that on multiple-choice tests. Essays can often better gauge what you know. Writing is integral to many jobs. But despite the fact that everyone can acknowledge that they're a more useful metric, we don't demand students write much on standardized tests because it's daunting to even imagine grading millions of essays.

Emotion-sensing computer software that models and responds to students' cognitive and emotional states – including frustration and boredom – has been developed by University of Notre Dame Assistant Professor of Psychology Sidney D'Mello and colleagues from the University of Memphis and Massachusetts Institute of Technology. D'Mello also is a concurrent assistant professor of computer science and engineering. The new technology, which matches the interaction of human tutors, not only offers tremendous learning possibilities for students, but also redefines human-computer interaction. "AutoTutor" and "Affective AutoTutor" can gauge the student's level of knowledge by asking probing questions, analyzing the student's responses to those questions; proactively identifying and correcting misconceptions; responding to the student's own questions, gripes, and comments; and even sensing a student's frustration or boredom through facial expression and body posture and dynamically changing its strategies to help the student conquer those negative emotions. "Most of the 20th-century systems required humans to communicate with computers through windows, icons, menus, and pointing devices," says D'Mello, who specializes in human-computer interaction and artificial intelligence in education.

Here's an odd fact: Turn-of-the-century photographers used to tell subjects to say "prunes" rather than "cheese," so that they would smile less. By studying nearly 38,000 high-school yearbook photos taken since 1905, UC Berkeley researchers have shown just how much smiling, fashion and hairstyles have changed over the years. The goal was not just to track trends, but figure out how to apply modern data-mining techniques and machine learning to a much older medium: photographs. Their research could advance deep-learning algorithms for dating historical photos and help historians study how social norms change over time. The main challenge for the team was to collect enough photos to create an "average" student profile for each decade from the 1900s to the 2010s.

We Google things we're too lazy to remember or ask another human. We've become accustomed to asking Siri and Cortana about the weather. The current generation of artificial intelligence can pull facts from the web, keep track of your appointments and even crack jokes. What if there were a virtual assistant to help you make real-life decisions, like whether you should start a brewery or go to business school? Project Sapphire, a collaboration between IBM and the University of Michigan, is aimed at building an artificially intelligent academic advisor that guides undergraduate students through their course options, helps pick extracurricular activities and eventually dishes out advice on their careers.

Aldebaran Robotics' Nao robot has already received a few upgrades from both the company itself and other developers, but it now has a proper successor. Aldebaran took the wraps off its new and improved Nao Next Gen robot today, touting features like a 1.6GHz Atom processor and dual HD cameras that promise to allow for better face and object recognition even in poor lighting conditions. What's more, while robot's outward appearance hasn't changed much, it has also received a number of software upgrades, including Nuance voice recognition, an improved walking algorithm, and a number of other measures to cut down on unwanted collisions. As before, the robot is aimed squarely at researchers and developers, but the Aldebaran's chairman notes that the company is continuing to pursue its goal of providing a Nao intended for individuals -- a goal he notes is being aided by the contributions from its developer program. Check out the gallery below and the video after the break for a closer look.