When I was quite young my mother gave me a grow your own crystal kit. It came with a bag of Alum and instructions. I first had to grow seed crystals in a super saturated solution of Alum and water. Then I placed the best seed crystal at the bottom of a jar and covered it in another saturated solution of Alum. After several weeks I had a fairly nice big crystal, formed from my seed crystal. It didn’t do anything except look pretty.

Scientists have really stepped that up a notch with these Self-Assembled Orgaic Circuits. In order to form these complex structures they create a silicon dioxide substrate with gold electrodes using conventional techniques. They then submerge the substrate in a solution containing the organic semiconductor and the molecules arrange themselves on the substrate in a densely packed single layer of molecular layer. Prior work has developed faster circuits but this method is extremely easy to deploy.

This type of technology will allow complex electronics to be embedded in items where it was previously not possible. It opens up the possibility of structurally flexible electronics that are cheap and easy to assemble. You may have a computer directly embedded into things like coffee cups, cereal boxes, newspapers, or have portable display devices that can roll up or fold up without complex engineering requirements.

Very cool future possibilities.

In a paper published today in PLoS Genetics the research supporting the value of so-called junk DNA gained more ground. As is stated in the article biologists have known about junk DNA for many years but it was felt that it was mostly extraneous data in the genetic code.

If you have read the research behind SDNEAT, you know that scientists are starting to change their perceptions of junk DNA. Segmental duplications seem to be critical in the evolution of species, they allow for high levels of genetic variation and mutation with a smaller chance of disabling the original genome all together.

This new study suggests that DNA ‘retrotransposons” are important to human evolution. One specific set of retrotransposons are called Alu elements:

“Alu elements are a major source of new exons. Because Alu is a primate-specific retrotransposon, creation of new exons from Alu may contribute to unique traits of primates”

Perhaps as an extension to SDNEAT the algorithm to merge a segmental duplication into the genome being mutated could be extended to allow for transposition of the identified segment within the genome. These higher order mutations could be exceptionally valuable when dealing with extremely complex solution spaces and genomes.

These automatons are certainly not adrift but they are definitely airborne and capable! Computer Scientists at Stanford have developed an autonomous helicopter that can learn from a human expert pilot to perform complex manoeuvres better than the original human expert!

The learning system does not just copy the controls for performing the manoeuvres it watches several built in sensors for the state of the environment around the helicopter and through several iterations develops an algorithm that can handle situations that were not part of the training but allow the autonomous pilot to complete the manoeuvre. These adapted agents could even keep more precise control over the aircraft than the original pilot could.

“For five minutes, the chopper, on its own, ran through a dizzying series of stunts beyond the capabilities of a full-scale piloted helicopter and other autonomous remote control helicopters. The artificial-intelligence helicopter performed a smorgasbord of difficult maneuvers: traveling flips, rolls, loops with pirouettes, stall-turns with pirouettes, a knife-edge, an Immelmann, a slapper, an inverted tail slide and a hurricane, described as a “fast backward funnel.”

The pièce de résistance may have been the “tic toc,” in which the helicopter, while pointed straight up, hovers with a side-to-side motion as if it were the pendulum of an upside down clock.”

I know I can’t do any of that and I certainly couldn’t learn it quickly. Could the learning system that Ng and his team have created be adapted to driving cars and flying planes? Possibly, as it depends on if it uses any assumptions about its environment. Flying around in a big open space is much easier than driving quickly through busy city streets. That doesn’t mean it can’t be done. The only downside to this research is it could be used to pilot Autonomous military planes to deploy weapons, completing the conversion of war to a video game.

Hopefully we see this deployed for peaceful use instead.

A group of researchers at NYU have been given funding to “discover the learning algorithm of the brain“.

This will be some exciting work, many scientists believe the easiest method to general artifical intelligence is through duplicating the human brain, that the sum of the parts is the soul so to speak. These researchers will be focusing on the visual system and how it manages to identify all the important bits in a photograph or any real world scene. They will then apply what they learn from these experiments to see if the same methodology works on similar brain structures.

I am not certain but this research may be related to the recent full simulation of the human visual cortex on Roadrunner currently the most powerful computer in the world. This full simulation of the visual cortex was a simulation system called PetaVision which actually was a full simulation of all the neurons that compose the human optic system and visual cortex that could be run in real time.

Slap some ears on it, a nose, and a neocortex and we have a beautiful baby AI.