Neural network programming to become more relevant

Perspective — I recognize the usefulness of genetic algorithms and neural networks, but I am also a severe critic against them. To put things simply, you need to hard-code the criteria that will let these algorithms decide whether their mutations were successful or not. This makes no sense at all, AI-wise.

I was seeking a method, a few years back, to let a program experiment with its own mutations so as to let it decide on its own — including what its own criteria should be. I never went on to coding this, but the general idea I had was the following: The algorithm should somehow temporarily store information on its past states, in a way that lets it compare now and then by interacting with its environment, and decide to go forward or roll back as necessary.

This would be consistent with biologists’ observations that populations sometimes sprout a new gene in as little as two generations, which is more consistent with Jean-Baptiste Lamarck‘s theories than with Charles Darwin‘s. Note, as an aside, that history kept the idea the two scientists were competing; whereas both were fighting for the same idea — the human species evolves — and didn’t necessarily think bad of each other’s ideas. That to say that time will reveal one day that the two theories are no more incompatible than top-down and bottom-up reasoning: They interact and feed one another.

As for the reason I am mentioning all this from seemingly nowhere, New Scientist is running an article with an interesting insight on how to program smart mutations.

Comments on Neural network programming to become more relevant

  1. Excellent blog, I’ve been following in the shadows for a few weeks now.

    Just wanted to comment that I’ve had a similar thought about self-training networks based on temporal knowledge of previous trainings. Basically treating previous states as an additional dimension of data for calculating fitness.

    But this is tangential to the problem of self-directed learning, and self-chosen fitness.

  2. Thanks a lot for your support. Hopefully, computer scientists won’t be scared by the lack of code around here.

    To develop a bit, the idea I liked in the New Scientist story is the RNA as short term biomemory and test enabler. It is consistent with a paper I bumped into in the past. In it, scientists were mentioning the appearance of genes in populations over as little as two generations in polynesian islands (I’m afraid the exact reference is long forgotten).

    Basically, the explanation the researchers were putting forward involved cell mutations through RNA, which would favor appropriate DNA rearrangements when fitness was adequate. Surely there is a way to translate this to neural networks and genetic algorithms. It would make so much more sense than the more or less random mutate() methods — btw: has this changed?

  3. i need the way to implemt neural network & emotion. I have problem to run neural network learning using emotion parameter. I need help pls.