Meta-Knowledge and the Heritage of Knowledge

Studying the heritage of literature, scientific knowledge and invention is really about studying how the creative process works asynchronously over many generations upon generations to think and build knowledge … knowledge is not just the original insight, knowledge comes out of the interactions, tests, replications, debates and more tests and proof. It’s never just ONE brilliant idea.

Knowledge development is never an INDIVIDUAL thing anymore … it requires teams, competition and asynchronous collaboration that extends across the globe and over generations … knowledge comes from ideas as well as review and refinement … knowledge depends upon lots of annotations, discussions of annotations and continual debate/argument of the annoations.

If we want to contribute something to improving team creative processes [which is what the goal of annotify.app is about] we will need to consider and reconsider our assumptions on what the rough schematic of the team creative processes looks like.

Naturally, the creative processes of different inventors and engineering research and development teams will be radically different, but generally nowadays invention of complex products happens in teams. Thus annotify.app will necessarily be developed first as something that a team might use.This means that it might be more formally structured and methodical than an approach that an individual creative artist would use.

We would foresee that the annotify.app solution would probably be consumed as a cloneable Git repository, of analysis tools … perhap something like annotated curated collections or Jupyter noteboks to analyze lists of dataset, ie something that could run on an individual machine or in a collaborative environment like GitPod or BinderHub.

In order to offer a tool catering to collaborative annotation in different kinds of inventive or problem-solving teams, we need to start with a very rough schematic of approximately what the creative problem-solving process by teams will look like:

1) Identification of needs and scope of problem 2) Analysis of these needs and implicit underlying assumptions 3) A review of existing solutions and applicable information 4) Brainstorming or formulation of lists of all possible solutions 5) Prioritized analysis of likely solutions and study of difference 6) Birth of a new idea or new set of ideas to test 7) Designed experimentation to test factor levels and confirmation of concept 8) Technical implementation and testing of alpha minimal viable product (MVP) 9) Review of results of test of alpha MVP with either complete re-examination starting at the first step OR refinement of MVP for either another round of alpha MVP testing or more detailed technical implementation of beta minimal viable product with actual users 10) Review of results of test of beta MVP with re-examination starting at prior step OR refinement of MVP for either another round of beta MVP testing or production launch.