Learning to Learn

One really interesting thing about node map learning, it it's flexibility. Given the right functions, and enough time, the node map can be theoretically capable of learning to preform any task. Though this can be very slow, and very daunting process. So why not teach itself?

A quick Google search will show how powerful learning algorithms are. Amazing tasks can be taught to the AI. Some preforming calculations far more powerful then previously thought possible. Other things can be taught to. Such as creating the fastest car, or the best fighting stick figure. Well, that's awesome. But I'm sure it took a while to get to that state using only self learning methods. And it does. And when thrown into a new environment, it can take a very, very long time to make the correct adaptations. Our algorithms are good, but not that good.

So let's let the AI figure it out. AI algorithms are already able to define the perfect algorithms for specific tasks, to a point that far surpases that of any human. Why not let the AI find a perfect learning algorithm?

The concept behind this is to have a "master" node map which is constantly learning and expanding it's knowledge of the functions it has, and how to use them. And use that to generate possible learning algorithm maps. Then send these maps out into a large series of varying tests. (There must be a large number of them, and they must be very diverse in order to minimize pattern finding and exploiting.) It wouldn't do every test, but a random selection of them. This helps to prevent the node map from excelling in a specific test to boost it's score. Then return the results over time for each test. Take all of the results from each test, and find the average learning curve. The idea for the master node map is find and create an algorithm that generates the highest learning curve. This process is very time consuming, and takes many, many, many more tests and passes then usual. That's because all of the learning is done ahead of time. More tests will have to constantly be added, and do to randomness, and test weaknesses, learning should not cease. This new learning algorithm would be constantly getting better over time, until it can finally surpases all of our current learning algorithms by far.