When we collaborate using the currently available tools, we are able to share meaning and develop themes, resulting in a record of the interactions which then requires processing in order to extract the valuable data which emerged from the conversation.
Given that our topic is boosting CI, it seems that we could develop a set of process protocols to apply to this stream of valuable resource information. I guess as it stands, we each have our ‘pet’ methodology, which would result in variable results, and not all parties to any given conversation have the time to devote to comparative analysis of the harvest.
Also, the harvested information is a valuable resource as part of an evolving collective ‘meme-stream’ (to coin a phrase) so we want to be able to access and include it in the evolving discourse in a way that becomes ‘standard’ with a view to automation of the process for optimal benefit to the overall process.
So, we need to develop methods of information harvesting and storage that are able to co-evolve with the contributions and constantly improving insights of the collaborators. Ultimately we need to be able to provide this information to the semantic web so it can process and refine it as a collaborative partner to us (which is what it is set to become).
Given this, the information we co-create needs to be rendered into language that the web can use. A process that enables this ‘translation’ is what we can develop as the next logical step on our journey of collective intelligence.
One suggestion is to convert text based transcripts of conversations into mind maps, which can then be adapted into semantacally integrated ontologies. This does represent a lot of work,the details of which we have yet to establish, however the results could very well pave the way to sane sentient AI, so the potential benefits are enormous.
This suggestion from a friend:
Imagine that we as a group are conversing via text chat. At the same time
there is a natural language analyser picking out key concepts (or we
can do this manually).
Note: all previous conversations have already been harvested and from
this an ontology has been distilled. Thus when the analyser picks out a
concept there is a window showing the network of ideas surrounding that
concept – where it fits within the élan universe of discourse and what
relations it has with other concepts. Woven into this semantic scaffold
are comments, observations, links and all manner of data that may be
associated with each concept.
This is an example of real-time mapping of the memesphere of a
conversation space. The map can be interacted with, edited, and
augmented with meta-data as part of the conversation. It provides an
interface into the collective knowledge space.