General software intelligences are still held to be outside our current capacity to build. While the definition of intelligence which we apply to machine learning and artificial intelligence generally has expanded over time as our practical computational scales increase, little exploration has been conducted around the other aspect of intelligence, which is the capacity to constantly learn and improve through interaction with the environment. If we are to define a software intelligence as an algorithm that is capable of interacting with its environment and adapting to it over time, then this exploration is critical to the development of such a system.
This body of research will attempt to make the first step into the area of continual feedback for a machine learning algorithm, evaluating it against an area which has traditionally been difficult for computers to emulate – Name Matching Analysis. If a machine learning algorithm can be used to ‘tune’ a soft-search name matching algorithm based on continual feedback generated from the results of that engine and the feedback provided by human experts, then this technique of constant feedback not only has immediate practical value but could be explored further in more ambitious research projects.