If there is an algorithm for intelligence…

Posted on October 25, 2007October 25, 2007 by robadmin

Then we could run it in about 50 atoms worth of space. That is assuming that we could build the smallest state machine possible in that amount of space and then actually wire it up to some tiny interface. The smallest possible universal state machine was proven to exist a couple days ago by Alex Smith of Birmingham, UK.

This really does have some significant impact. While I don’t think we would run the algorithm for intelligence on this particular state machine, we could. We could in fact run any program at all on this state machine and have it input and output any possible string of information. You can think of this as the smallest possible independent microprocessor. This could be a significant step in the advancement of massively parallel sensor networks.

Think of this, you construct a piece of e-paper made up of these tiny little state machines. You connect them in all eight directions to their neighbours using carbon nanotube circuitry as well as connecting the top layer to a set of output machines, something like a pixel in an LCD. Now put a few of these together into a magazine and put some somewhat more complex circuitry into the spine of the book along with a power source (probably an external layer of solar energy molecules) and you have yourself an extremely powerful parallel computer that looks something like a book but is capable of far more. Completely dynamic content all controlled through a massively distributed network of basic microprocessor state machines. I can’t take credit for this idea, it is from “The Diamond Age” a very good novel about nanotechnology but what is fantastic in that book is a step closer to reality due to this proof and several recent advances in nanotechnology. Another common theme from the same book is how ubiquitous these massively parallel systems could be. They could consist of countless billions of tiny sensor nodes distributed through the air. You could breathe them in without destroying significant portions of the network because they are so small (about the size of dust). Yet the processing power in each is universal and the power of the entire system is extraordinary. Each of the nodes could perform complex sensing tasks and transmit their information through the network back to their home base. These truly would be automatons adrift!

Of course the algorithms to do that efficiently don’t really exist yet, but they are being worked on.

I know it seems pretty wild, but that is just one example. When you distribute the processing of a program across millions of tiny universal state machines you can drop the time to process down to a much smaller value. This would require a completely new direction in programming but it is entirely possible. The original posting of this was found on slashdot, thanks guys!

Whew… Blogging Breakdown

Posted on October 17, 2007October 17, 2007 by robadmin

What happens when you work a full time job, get lots of overtime at work and have a major deadline in your thesis work that requires you to code your butt off? You get a breakdown in the amount of cool blog posts you get to put up.

I am working out some loose ends in my integration of NEAT into PicoEvo and Simbad. I am to the point where I have to integrate the genetic operators and the evolution epoch into the algorithm and putting it together the right way is tricky. I suspect I will wind up just slamming it together so it works then I can pull it apart and put it back together the right way after I meet my deadline!

Neurotic Agents

Posted on October 10, 2007October 10, 2007 by robadmin

I got this from Slashdot a couple days ago and I wanted to share it.

When you are playing a real time strategy game (or any video game for that matter) the artificial intelligence you are playing against is usually a form of rule based system. The AI is given large amounts of game information, has a complex set of rules it follows and really would kick your butt every time if the game makers didn’t dumb it down. Some recent research into emotional AI with game playing shows that a Neurotic personality does best at playing a real time strategy it even beats the AI that is tuned to be difficult for humans.

I wonder if a evolutionary agent could learn the emotions of this AI? Perhaps it could evolve an efficient Neural Network structure for Neurotic game play. Could we separate the neurosis from the game rules in the NN? Some interesting questions, the article is from New Scientist (which is an awesome magazine).

Turkey Weekend

Posted on October 9, 2007October 9, 2007 by robadmin

I hope everyone had a nice Thanksgiving weekend. I enjoyed two dinners, one on Sunday and one on Monday. I have many leftovers.

I also spent a lot of time working on my implementation of NEAT into Picoevo. It is a very intricate process integrating NEAT into an evolutionary systems like Picoevo. It wasn’t really designed to handle an algorithm like NEAT though it is quite capable of it. I think there is more than one way to implement it, and I am following the approach that I think works right now. In the future I may revise the design to bring it more inline with the design of Picoevo.

One of the more interesting tasks of this project is deciding where each portion of NEAT belongs in the Picoevo environment. Picoevo heavily uses inheritance to be flexible and it has been designed to work with almost any type of Genome. One just has to decide how to extend each element to support what you require. So for implementing NEAT I have each gene in the genome implemented as an Element, each genome is an individual composed of multiple types of Elements and each Population has multiple NEAT genomes. Crossover, Speciation and Innovation are controlled at the Population level, mutating genomes by adding links and nodes is controlled at the Individual level and mutation of weights is controlled at the Element level.

Picoevo was designed well and is indeed flexible but it wasn’t really designed with the idea that each Individual or genome might have more than one type of gene or Element. So I had to be creative when it comes to extending the Individual class in Picoevo. I think the solution will work out fine. When I post the Neuroevolutionary Solver for public consumption I will talk about some of these design ideas more.