Generative artificial intelligence

Abstract

The closed systems of contemporary Artificial Intelligence do not seem to lead to intelligent machines in the near future. What is needed are open-ended systems with non-linear properties in order to create interesting properties for the scaffolding of an artificial mind. Using post-structuralistic theories of possibility spaces combined with neo-cybernetic mechanisms such as feedback allows to actively manipulate the phase space of possibilities. This is the field of Generative Artificial Intelligence and it is implementing mechanisms and setting up experiments with the goal of the creation of open-ended systems. It sidesteps the traditional argumentation of top-down versus bottom-up by using both mechanisms. Bottom-up procedures are used to generate possibility spaces and top-down methods sort out the structures that are functioning the worst. Top-down mechanisms can be the environment, but also humans who steer the development processes.