I have recently got into AI and I am eager to learn about its concepts. In some of the information I saw about AI, there was a lot about neural networks. Neural networks seem to be (something along the lines of) a type of algorithm that creates a graph which works based on a theory about how neurons interact, in order to create self-learning programs.
I'd like more information about this theory and model, as there is a lot that I don't understand.
Firstly, the diagram I keep seeing (I'm assuming it's a graph). It shows a set of nodes (which is apparently the input), each directed to each of another set of nodes, each of which is then directed to each of another set of nodes, etc. until it reaches what is apparently the output.
How can something like an image, or a complex piece of data be represented by those input nodes? What goes on at each set of nodes? Does every node from each set have to always be connected to every node from the next set? Do they always have to be directed to the next set, or can they go back and fourth as well? Can I have some code given in relation to one of these diagrams?
(A node is a variable shown in a graph.)
Secondly, I read that these sort of algorithms could eventually create a conscious program if "enough neurons work together". I do look forward to when people start to manage to do this, but it seems like no one is trying to rethink/expand-on that theory as much as they should. I'd expect people to try to look for new human behaviours to represent with AI. For example: a new-born would take their first breath despite the pain, while usually the mind tries to avoid pain. This causes crying.
Has anyone tried to mimic this sort of behaviour scenario (or any behaviour scenario) in a program? If not, why not? How close are we to creating a conscious mind in a program? Are people challenging the current theory that created our neural network model? Is it lightly for this community to propose a better theory and model?