Herself’s Artificial Intelligence

New insight into how brains process visual information is a double edged sword. It will make for much better vision engines but with that will come the failure of our most popular human test at the moment — captcha.

Using a fly, whose brain is heavily coded for visual information, Nemenman and his colleagues were able to show information is passed along the spikes in the fly’s brain neurons.

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Nemenman and his colleagues’ research is significant because it re-examines fundamental assumptions that became the basis of neuromimetic approaches to artificial intelligence, such as artificial neural networks. These assumptions have developed networks based on reacting to a number of impulses within a given time period rather than the precise timing of those impulses.

“This may be one of the main reasons why artificial neural networks do not perform anywhere comparable to a mammalian visual brain,” said Nemenman, who is a member of Los Alamos’ Computer, Computational and Statistical Sciences Division. “In fact, the National Science Foundation has recognized the importance of this distinction and has recently funded a project, led by Garrett Kenyon of the Laboratory’s Physics Division, to enable creation of large, next-generation neural networks.”

New understanding of neural function in the design of computers could assist in analyses of satellite images and facial-pattern recognition in high-security environments, and could help solve other national and global security problems. [ read more Language of a fly proves surprising ]

Papers:
PLoS: Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

More information:
Is Captcha’s moment passing?

Ah, but do we really want our computers to understand us? Anybody remember ‘Clippy’?

Computer: “You seem depressed today, should I Google Dr Kevorkian for you?”

Or will the clerks at the local retail store start wearing cameras with emotion recognizing software? A bit of customer understanding by the help would go a long way in many a business.

Researchers at the Department of Artificial Intelligence (DIA) of the Universidad Politécnica de Madrid’s School of Computing (FIUPM) have, in conjunction with Madrid’s Universidad Rey Juan Carlos, developed an algorithm that is capable of processing 30 images per second to recognize a person’s facial expressions in real time and categorize them as one of six prototype expressions: anger, disgust, fear, happiness, sadness and surprise.

Applying the facial expression recognition algorithm, the developed prototype is capable of processing a sequence of frontal images of moving faces and recognizing the person’s facial expression. The software can be applied to video sequences in realistic situations and can identify the facial expression of a person seated in front of a computer screen. Although still only a prototype, the software is capable of working on a desktop computer or even on a laptop. [ read more Facial Expression Recognition Software ]

How ever emotion recognition software gets used in the future this software is bound to be fun.

More information:
Video of software in action

Papers:
Jose Miguel papers ( $ )
Facial Gesture Recognition Using Correlation and Mahalanobis Distance

See also:
Software recognizes anxiety in people

The Japanese are really going to make it much more fun to age. What is really cool is the technology for these glasses is well known and already available.

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Simply tell the glasses what you are looking for and it will play into your eye a video of the last few seconds you saw that item.

Built on to the glasses is a tiny camera which makes a constant record of everything the wearer sees: the tiny display inside the glasses identifies what is being scanned and a small readout instantly announces what the computer thinks the object probably is. For some things that look different from a range of angles, however, the glasses offer only a “best guess” – they are better at identifying a guitar and a chair than a coathanger or battery.
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The hardware itself is not extraordinary: what has taken Professor Kuniyoshi several years to perfect is the computer algorithm that allows the goggles to know immediately what they are seeing. It is, he says, a problem that has always vexed the fields of robotics and artificial intelligence.

But working in a team with Tatsuya Harada, one of Japan’s masters of the science of “fuzzy logic”, Mr Kuniyoshi believes he has cracked the problem. Behind the goggles is possibly the world’s most advanced object recognition software and a computer that can learn the identity of new objects within seconds.

So if the user wanders round the house for about an hour telling the goggles the name of everything from that coathanger to the kitchen sink, they will remember. Then if, at some point in the future, you ask them where you last saw a particular item, they will play the appropriate footage.

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[ read more The glasses that can help you find anything ]

See also:
Robots for old boomers (UMass project aims to assist aging population )

I told you AI would be coming to your cell phone soon. Not only do cell phones come with powerful processors now but there are special circumstances that make cell phone AI both more practical and more interesting.

Cell phone cameras now auto tag the date and often GPS coordinates of pictures you take. The cell cameras also usually recognize when the photo contains a face. This is used to help with exposure and auto settings built into the camera.Because people photograph the same 30 or so people with their cell phones the face recognition software doesn’t have to learn many faces.

. . . With autotagging, the camera attaches tags as the pictures are taken. Today, cameras embed timestamps in photos, which makes it possible to sift through pictures by date. But be honest here–how reliably can you remember exactly when you took that picture of your darling daughter a year or two ago that you’d like to e-mail to her grandparents? Being able to screen for photos only of a particular person could dramatically speed up the search process.Face recognition requires computational horsepower that is hard to fit into the confines of a digital camera, but one company likely to help make it a reality is Fotonation, which already supplies face-detection software for dozens of camera models from Samsung, Pentax, and others. [ read more Up Next: Cameras that know who you photographed ]

More information:

FaceTracker Demonstrated for Mobile Phones

Papers:
Automated sorting of consumer image collections using peripheral region image classifiers ( $ ieee pdf )
A review of face recognition techniques for in-camera applications ( $ ieee pdf )
Automated indexing of consumer image collections using person recognition techniques ( $ ieee pdf )

See also:
Mobile phone smart network warns of intruders

Facet is planned to be released as open source software. It allows phones to alert you when an object enters or leaves an area. So if you are camping with friends you can set up your cells around the edge of the camp and it will figure out where the other phones are positioned and let you know when a person or animal approaches your camp.

Software that turns groups of ordinary camera cellphones into a “smart” surveillance network has been developed by Swiss researchers. The team says it will release the software for programmers and users to experiment with.

The software employs Bluetooth, a short-range wireless technology included in many modern phones, to automatically share information and let the phones collectively analyse events that they record. This provides a platform for a group of phones to act as smart network capable of, for example, spotting intruders or identifying wildlife. [ read more Cellphones team up to become smart CCTV swarm]

More information:
Philipp Bolliger, home page and publications
Facet: Towards a smart camera network of mobile phones ( pdf )

See also:
AI coming to a cell phone near you