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Pilotless Passenger Plane Takes 500 Mile Test Flight In Commercial Airspace

Last month, a robot plane safely carried passengers 500 miles from England to Scotland and back again, the British consortium operating the plane revealed Monday.

Dubbed “the Flying Test Bed,” the plane is a normal 19-seat Jetstream—the kind a corporate executive might fly in—that was converted to fly autonomously.

The group behind the plane is Autonomous Systems Technology Related Airborne Evaluation and Assessment (Astraea), a business consortium funded by the British government and private businesses.

The test flight wasn’t completely autonomous—a human pilot onboard entered the cockpit to steer the plane through take-off, and then later the landing. The majority of the flight, however, that long tedium of maintaining a plane at cruising altitude, was in control of the remote pilot, with autonomous systems doing much of the actual flying.

While this was a test flight, it didn’t interrupt normal air traffic, and it’s 500-mile round-trip between Warton, England, and Inverness, Scotland, occurred in regular commercial airspace, shared with other airplanes.

(via Robot Plane Flies Humans 500 Miles | Popular Science)

Google Investing in Drone Autopilot Systems

Google’s venture capital arm announced yesterday that it is investing $10.7 million in a company that makes drone brains. The company, Airware, builds autopilots for unmanned aerial systems.

Because space and weight are at a premium on drones, especially small ones, Airware’s systems can get pretty tiny—one model weighs 32 grams, or about the same as a pocketful of coins.

Airware made news in January (under their previous name of Unmanned Innovations, Inc.) when a Kenyan wildlife conservation group purchased one of its drones to fly over a nature preserve and watch for poachers.

(via Google Bets $10.7 Million On Drone Intelligence | Popular Science)

The End of Psychology: Diagnosing Depression With Kinect

Computer scientists at the University of Southern California have used Microsoft’s Kinect sensor to detect, with 90% accuracy, whether you are depressed. All you have to do is sit down in front of Kinect, answer some questions from an on-screen virtual psychologist, and the clever software does the rest. The process is entirely automated, objective, and self-contained, meaning accurate testing could be carried out in complete privacy at home.

The software, called SimSensei and developed by Stefan Scherer and colleagues, is essentially a clever mix of computer vision algorithms and the psychological model of depression. The on-screen psychologist asks you leading questions — a lot like the old-school Eliza, or Alice — and then watches how you physically respond. Using Kinect, the computer vision algorithms build up a very detailed model of your face and body, including your “smile level,” horizontal gaze and vertical gaze, how wide open your eyes are, and whether you are leaning toward or away from the camera. From these markers, SimSensei can work out whether you’re exhibiting signs that indicate depression — gaze aversion, smiling less, and fidgeting. Watch the video below and be amazed.

(via Kinect-based system diagnoses depression with 90% accuracy | ExtremeTech)

Brain Simulation: IBM’s Artificial Synapse Inches Closer to Electrochemical Properties of Actual Brain

What’s exciting about this work isn’t the short-term implications, but the long-term goals. It’s extremely difficult to model the behavior and function of a system if you can’t build a representative model of it.

The Blue Brain project is one of the world’s leading efforts to simulate neuronal structure. The last major project milestone was the simulation of a cellular mesocircuit with 100 neocortical columns and a million cells in total. Doing so required the use of an IBM Blue Gene/P, one of the most power-efficient supercomputers in existence. At present, simulating one simplified component of a rat brain requires multiple orders of magnitude more power than an organic brain uses.

And that’s why advances like this matter. The ability to modify a material’s insulative properties without applying electricity could be critical to future attempts to scale brain modeling downward.

Creating circuits that model synapse functions (even if they do so imperfectly and very simply) can help us understand how their biological counterparts function. It could dramatically reduce the power consumption (and waste heat) generated by such attempts, just as the advent of modern semiconductor manufacturing reduced computers from structures that fit into warehouses to pockets.

(via IBM takes a step towards building artificial semiconductor synapses | ExtremeTech)

DARPA Funded Research Aims to Automate Video Surveillance, Teach Computers to Identify Suspicious Behavior

This approach relies heavily on advances by machine vision researchers, who have made remarkable strides in last few decades in recognizing stationary and moving objects and their properties.

It’s the same vein of work that led to Google’s self-driving cars, face recognition software used on Facebook and Picasa, and consumer electronics like Microsoft’s Kinect.

When it works well, machine vision can detect objects and people — call them nouns — that are on the other side of the camera’s lens. But to figure out what these nouns are doing, or are allowed to do, you need the computer science equivalent of verbs.

…that’s where Oltramari and Lebiere have built on the work of other Carnegie Mellon researchers to create what they call a “cognitive engine” that can understand the rules by which nouns and verbs are allowed to interact. Their cognitive engine incorporates research, called activity forecasting, …which tries to understand what humans will do by calculating which physical trajectories are most likely.

They say their software “models the effect of the physical environment on the choice of human actions.”

Both projects are components of Carnegie Mellon’s Mind’s Eye architecture, a DARPA-created project that aims to develop smart cameras for machine-based visual intelligence. Predicts Oltramari: “This work should support human operators and automatize video-surveillance, both in military and civil applications.”

(via U.S. looks to replace human surveillance with computers | Security & Privacy - CNET News)

Robots That Learn Through Demonstration Instead of Programming

Neil Dantam at the Georgia Institute of Technology in Atlanta and colleagues are developing a system in which robots learn by example. Instead of programming a robot to carry out a task, a demonstration could suffice.

However, the robot does not simply mimic the human’s actions. The prototype system uses an Xbox Kinect camera to observe the human performing an activity, then breaks that activity into a sequence of key actions necessary to carry out the task.

These actions are converted into a general set of instructions – much like those found in an IKEA furniture instruction manual – that can be interpreted even by non-humanoid robots. “You want to somehow capture the important aspects of the human’s motion and transfer that to the robot,” says Dantam. “Think about how you’d tell someone how to make a cake,” he says. The way people follow those steps may vary. “An adult might bend down over the counter to work while a child may stand on tiptoes.”

(via Robot learns using IKEA-style instructions - tech - 10 October 2012 - New Scientist)

AI Developed to Improve Relevance of Commentator Patter During Live Sports

WHEN watching sport on TV …a good commentator can make all the difference, peppering a play-by-play account of the action with expert knowledge and anecdotes. But even the best commentator’s repertoire is limited.

…Created by sports fan Greg Lee at the University of Alberta in Canada, [AI Program] Scores can tap into a stash of sporting stories to find relevant anecdotes that a commentator might not have thought of.

…The system works by matching the features of a live event - such as the teams, key players, the score and the remaining time - against a database of available stories. Once stories that include some of those features are found it selects the few that are most relevant and suggests them to a human commentator.

The challenge is in evaluating the relevance of candidate stories and ranking them. Lee’s system uses machine-learning techniques to do this. The most important feature was the teams involved and the second was the difference in number of runs.

To test the system, the researchers used it to create commentary for pre-recorded sports broadcasts and presented them to 254 volunteers, who said they found the commentary relevant and enjoyable. 

(via AI sports commentator knows all the best stories - tech - 04 October 2012 - New Scientist)

UPDATE: Google’s Cat-Video Identifying Neural Net Now Working to Improve Voice Commands, Image Search, Google Glass and Self-Driving Cars

Google is now using these neural networks to recognize speech more accurately, a technology increasingly important to Google’s smartphone operating system, Android, as well as the search app it makes available for Apple devices.

“We got between 20 and 25 percent improvement in terms of words that are wrong,” says Vincent Vanhoucke, a leader of Google’s speech-recognition efforts. “That means that many more people will have a perfect experience without errors.”

The neural net is so far only working on U.S. English, and Vanhoucke says similar improvements should be possible when it is introduced for other dialects and languages.

Other Google products will likely improve over time with help from the new learning software. The company’s image search tools, for example, could become better able to understand what’s in a photo without relying on surrounding text. And Google’s self-driving cars and mobile computer built into a pair of glasses could benefit from software better able to make sense of more real-world data.

(via Google Puts Its Virtual Brain Technology to Work - Technology Review)

See Also: “Google and Stanford have created the [digital equivalent of the] visual cortex of an infant human”

Algorithm Analyzing Artistic Style Identifies Same Patterns as Art Historians

Computer scientists Computer scientists… have developed a program that analyzes paintings in a manner similar to how expert art historians perform their analysis, and conducted an experiment that showed that machines can outperform untrained humans in the analysis of fine art.

In the experiment, the researchers used approximately 1, 000 paintings of 34 well-known artists, and let the computer algorithm analyze the similarity between them based solely on the visual content of the paintings, and without any human guidance.

Surprisingly, the computer provided a network of similarities between painters that is largely in agreement with the perception of art historians.

I take this research with a grain of salt: the algorithm seems to have detected the patterns it was designed to detect.  I would have been more excited if it drew a new connection that human art experts found interesting.

(via Can computers understand art? | KurzweilAI)

Italian Roboticist Wants to Study Consciousness By Teaching Japanese Robots to Sing Jazz

Intelligence is often defined as the ability to find connections between existing entities - understanding that a key goes in a lock, for instance.

But Chella suggests that a conscious organism should be able to go a step further and introduce novel connections - between, say, musical phrases - that result in the creation of something new. That, in essence, is the idea behind improvisation…

Chella wants to replicate these states in a machine. “Consciousness could be linked to these moments of combination,” he says.

“[This work] raises interesting questions about the link between consciousness and music making,” says musician and computer scientist Philippe Pasquier… But he is sceptical about whether a robot musician needs a physical body, citing examples of AI composers that exist only in software.

(via Jazz-singing robot could shed light on consciousness - tech - 27 September 2012 - New Scientist)