Herself’s Artificial Intelligence

Dr. Craig Tovey at the George Institute of Technology studied bees for years waiting for the right problem to come along that would use bee load balancing technology. Sunil Nakrani came to Dr. Tovey hoping to use his expertise in algorithm heuristics to help solve network load balancing problems.

. . . “But the bees aren’t performing a computation or strategy, they ARE the computation,” Tovey added.

Internet servers, on the other hand, are theoretically optimized for “normal” conditions, which are frequently challenged by fickle human nature. By assigning certain servers to a certain Web site, Internet hosts are establishing a system that works well under normal conditions and poorly under conditions that strain demand. When demand for one site swells, many servers sit idly by as the assigned servers reach capacity and begin shifting potential users to a lengthening queue that tries their patience and turns away potential customers.

Tovey and Nakrani set to work translating the bee strategy for these idle Internet servers. They developed a virtual “dance floor” for a network of servers. When one server receives a user request for a certain Web site, an internal advertisement (standing in a little less colorfully for the dance) is placed on the dance floor to attract any available servers. The ad’s duration depends on the demand on the site and how much revenue its users may generate. The longer an ad remains on the dance floor, the more power available servers devote to serving the Web site requests advertised.[read more Bee Strategy Helps Servers Run More Sweetly]

The servers act as either foragers or scouts. An advertisement board is used for communications. The scouts post which foragers need bandwidth on the advertisement board. Priority is given to the highest earners in the same way bees give the most attention to the flower patches with the most nectar. Importantly, it is the close nature of the problem of server load and of bees obtaining nectar that allows the algorithm to work so well.

Papers:

2nd International Workshop on the Mathematics and Algorithms of Social Insects ( excellent resource several papers are included in pdf )

From Honeybees to Internet servers: biomimicry for distributed management of internet hosting ( pdf – very readable )

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

Cockroaches are prefect for testing swarm behavior, they are pack members and behave as a swarm. They group together especially when hiding. They find a cool, dark place and pile into it as a group.

Four cockroaches built by Jose Halloy were introduced into a cockroach group. At first the roaches ignored the robots. They were then given sex hormone odors and accepted into the roaches group. The roaches were given two dark hiding places, one darker than the other. At first they and the robot preferred the dark hiding place. The robot was reprogrammed to prefer the lighter of the two dark hideouts and 60% of the other roaches followed suit.

Simpler methods such as hunting decoys have had success influencing animal behavior. But this method doesn’t have a way for the decoy to further interact with the animal. Robots gives us that opportunity. The things we learn from such studies will also give us insight in to ways to better control robot swarms.

See also:
Swarm intelligence reaches a new level

More information:
Led by Robots, Roaches Abandon Insticts
Robot ‘pied piper leads roaches
Leurre – Artificial life control in mixed societies
Slashdot ‘Robots Assimilate into Cockroach Society

Papers
Building mixed societies of animals and robots ( has pictures and details of the robot – cockroach experiment – excellent beginner’s paper )
Swis Track: A tracking tool for multi-unit robotic and biological systems ( more details on the cockroach experiment, also easily readable)