Kutliroff’s speech ends around 5:40 followed by a media room gesture control application, a demonstration of an avatar (7:43), and a pretty neat-looking boxing game (8:43).

Of course one of the big differences between Project Natal and Shadow is that you’ll only ever see Natal on the Xbox or other Microsoft platforms. Shadow might be popping up everywhere. At least, that’s what Kutliroff and Omek seemed to be aiming for. Other companies in the gesture control business are focusing on a single application (Toshiba/Hitachi for TVs and home media, g-speak for computers, and Project Natal for video games). Omek Interactive isn’t married to one particular kind of hardware and they’re definitely trying to court a plurality of application developing firms. While they’ve created some interesting demo games and applications, Kutliroff’s presentation clings to the middleware status. Shadow is, after all, a SDK. Omek is poised to enable third party developers to build the next generation of gesture controlled technologies. Probably in video games, but possibly for TVs and computers as well.

The only question I have is whether the products that would sandwich Shadow (the 3D cameras on one side, and the gesture enabled applications on the other) are actually ready. We’ve seen some depth-perceptive cameras on the market (such as the 3D stereoscopic webcam from Minoru) but they are far from ubiquitous. Likewise, there’s been some good buzz surrounding gesture TVs and Project Natal’s video games but neither is actually on sale yet. This is an emerging market, and while the possibilities for gesture controls are very promising there’s no guarantee they’ll be popular. Omek could be caught as the middleman between two types of products that never get off the ground.

I must admit that part of my skepticism stems from the fact that gesture controls are not my favorite of the technologies contending to be the next major human-computer interface. As fun as it may be to play a movie with the flip of a wrist, or use your entire body to play a virtual boxing match, these applications lack tactile feedback. There’s nothing to hold. Nothing physical to let you know that you’re actually interacting with something. To me, for gesture controls to really succeed they’ll need some sort of haptics. I’d be totally cool with flailing my limbs through the open air if I could actually feel when my virtual self was hitting something.

Still, my personal preferences aside, the entire body monitoring control scheme seems to be grabbing a lot of attention. Omek Interactive is making a great move by racing to become the definitive middleware solution in the field. If the public does become interested in gesture technology, the Shadow SDK could get some major use. It would let companies that are good at making hardware, and companies that are good at making applications (i.e. games) focus on their strengths while Omek knits them together. That’s a smart strategy and a sure way to enable innovation. It will likely take several years before we know whether gesture controls are here to stay, but Omek is certainly a name to watch while we figure it all out.

Japan's Space Oriented Higashiosaka Leading Association (SOHLA) expects to spend an estimated 1 billion yen (US$10.5 million) in getting the robot onto the lunar surface. Named Maido-kun after the satellite launched a aboard a Japan Aerospace Exploration Agency (JAXA) HII-A rocket in 2009, there appears to be no clearly defined mission for the robot (apart from getting there).

It's hoped that Maido-kun will travel to the moon on a JAXA mission planned for around 2015.

Why not stick to wheels? “Humanoid robots are glamorous, and they tend to get people fired up,” said SOHLA board member Noriyuki Yoshida. “We hope to develop a charming robot to fulfill the dream of going to space.”

Achieving the feat would certainly be another feather in the cap of Japan's world-leading robotics industry.

With the phrase "web 2.0" falling out of vogue, the most exciting new uses of the internet are now all about the cloud, a term for servers invisibly doing smart, fast things for net users who may be on the other side of the world.

But it's not just humans that stand to gain, as a recent corporate acquisition by cloud pioneer Google demonstrates. Google has snapped up British start-up Plink, which has devised a cellphone app that can identify virtually any work of art from a photograph. Plink's app will bolster Google's Goggles service, which uses a cellphone camera to recognise objects or eventranslate text. Unlike most cloud start-ups, Plink sprang from a robotics lab, not a Californian garage. Its story demonstrates how the cloud has as much to offer confused robots as it does humans looking for smarter web apps. 

Source: New Computer Interface Goes Beyond Just Touch (http://www.technologyreview.com/blog/editors/25038/?a=f)

Electroencephalography (EEG), which measures electrical activity through the scalp, was previously considered too insensitive to relay the neural activity involved in complex movements of the hands. Nevertheless, Trent Bradberry and colleagues at the University of Maryland, College Park, thought the idea worth investigating.

The team used EEG to measure the brain activity of five volunteers as they moved their hands in three dimensions, and also recorded the movement detected by motion sensors attached to the volunteers' hands. They then correlated the two sets of readings to create a mathematical model that converts one into the other.

In additional trials, this model allowed Bradberry's team to use the EEG readings to accurately monitor the speed and position of each participant's hand in three dimensions.

If EEG can, contrary to past expectation, be used to monitor complex hand movements, it might also be used to control a prosthetic arm, Bradberry suggests. EEG is less invasive and less expensive than the implanted electrodes, which have previously been used to control robotic arms and computer cursors by thought alone, he says.

Boosting the frequency of light pulses is a key requirement to increasing data transfer speeds in optical communication, as this means more information can be packed into a given unit of time. The device designed by the engineers at UC Davis divides the incoming signal into slices of frequency spectrum, processes the slices in parallel and then integrates them into a single signal, a process also known as frequency domain multiplexing (FDM).

The system can work at much higher frequencies than normal because it can simultaneously measure both the intensity and the phase of a light pulse, boosting bandwidths from the typical tens of gigahertz, which is the ultimate limit that electronics alone can reach, into the 100 terahertz range.

"We have found a way to measure a very high capacity waveform with a combination of standard electronics and optics," explained S.J. Ben Yoo, professor of electrical and computer engineering who was part of the research group.

Apart from faster communication, the team's work could also find application in light detection and ranging (LiDAR) systems, which employ rapid successive pulses of laser light to scan a three-dimensional surface and produce highly detailed images of the Earth's surface. The next step, according to the researchers, is now to try and fit the entire device into a silicon chip.

The research was funded by grants from the U.S. Defense Advanced Research Projects Agency. A paper describing the technology was published Feb. 28 in the journal Nature Photonics.

They are quick to add that it may lead to powerful benefits, however. These include gaining a better understanding of the brain and improved communication with people who can't speak or write, such as stroke victims or people with neurodegenerative diseases. There is also excitement over the possibility of being able to visualise something highly graphical that someone healthy, perhaps an artist, is thinking.

So how does neural decoding work? Gallant's team drew international attention last year by showing that brain imaging could predict which of a group of pictures someone was looking at, based on activity in their visual cortex. But simply decoding still images alone won't do, says Nishimoto. "Our natural visual experience is more like movies."

Nishimoto and Gallant started their most recent experiment by showing two lab members 2 hours of video clips culled from DVD trailers, while scanning their brains. A computer program then mapped different patterns of activity in the visual cortex to different visual aspects of the movies such as shape, colour and movement. The program was then fed over 200 days' worth of YouTube clips, and used the mappings it had gathered from the DVD trailers to predict the brain activity that each YouTube clip would produce in the viewers.

Finally, the same two lab members watched a third, fresh set of clips which were never seen by the computer program, while their brains were scanned. The computer program compared these newly captured brain scans with the patterns of predicted brain activity it had produced from the YouTube clips. For each second of brain scan, it chose the 100 YouTube clips it considered would produce the most similar brain activity - and then merged them. The result was continuous, very blurry footage, corresponding to a crude "brain read-out" of the clip that the person was watching.

In some cases, this was more successful than others. When one lab member was watching a clip of the actor Steve Martin in a white shirt, the computer program produced a clip that looked like a moving, human-shaped smudge, with a white "torso", but the blob bears little resemblance to Martin, with nothing corresponding to the moustache he was sporting.

Another clip revealed a quirk of Gallant and Nishimoto's approach: a reconstruction of an aircraft flying directly towards the camera - and so barely seeming to move - with a city skyline in the background omitted the plane but produced something akin to a skyline. That's because the algorithm is more adept at reading off brain patterns evoked by watching movement than those produced by watching apparently stationary objects.

"It's going to get a lot better," says Gallant. The pair plan to improve the reconstruction of movies by providing the program with additional information about the content of the videos.

Team member Thomas Naselaris demonstrated the power of this approach on still images at the conference. For every pixel in a set of images shown to a viewer and used to train the program, researchers indicated whether it was part of a human, an animal, an artificial object or a natural one. The software could then predict where in a new set of images these classes of objects were located, based on brain scans of the picture viewers.

Movies and pictures aren't the only things that can be discerned from brain activity, however. A team led by Eleanor Maguire and Martin Chadwick at University College London presented results at the Chicago meeting showing that our memory isn't beyond the reach of brain scanners.

Movies and pictures aren't the only things that can be discerned from brain activity

A brain structure called the hippocampus is critical for forming memories, so Maguire's team focused its scanner on this area while 10 volunteers recalled videos they had watched of different women performing three banal tasks, such as throwing away a cup of coffee or posting a letter. When Maguire's team got the volunteers to recall one of these three memories, the researchers could tell which the volunteer was recalling with an accuracy of about 50 per cent.

That's well above chance, says Maguire, but it is not mind reading because the program can't decode memories that it hasn't already been trained on. "You can't stick somebody in a scanner and know what they're thinking." Rather, she sees neural decoding as a way to understand how the hippocampus and other brain regions form and recall a memory.

Maguire could tackle this by varying key aspects of the clips - the location or the identity of the protagonist, for instance - and see how those changes affect their ability to decode the memory. She is also keen to determine how memory encoding changes over the weeks, months or years after memories are first formed.

Meanwhile, decoding how people plan for the future is the hot topic for John-Dylan Haynes at the Bernstein Center for Computational Neuroscience in Berlin, Germany. In work presented at the conference, he and colleague Ida Momennejad found they could use brain scans to predict intentions in subjects planning and performing simple tasks. What's more, by showing people, including some with eating disorders, images of food, Haynes's team could determine which suffered from anorexia or bulimia via brain activity in one of the brain's "reward centres".

Another focus of neural decoding is language. Marcel Just at Carnegie Melon University in Pittsburgh, Pennsylvania, and his colleague Tom Mitchell reported last year that they could predict which of two nouns - such as "celery" and "airplane" - a subject is thinking of, at rates well above chance. They are now working on two-word phrases.

Their ultimate goal of turning brain scans into short sentences is distant, perhaps impossible. But as with the other decoding work, it's an idea that's as tantalising as it is creepy.

What do you think? Heh...

Source: Brain scanners can tell what you're thinking about (http://www.newscientist.com/article/mg20427323.500-brain-scanners-can-tell-what-youre-thinking-about.html?full=true) by Ewen Callaway