Monday, February 19, 2024

AI-Augmented Insight Mapping

''AI-Augmented Insight Mapping'' is an advanced application of technology in the field of knowledge management and decision support, leveraging artificial intelligence to enhance the process of creating, visualizing, and analyzing complex relationships and insights within data.  As far as I know, I am the first person to use that term.  Insight mapping isn't a common term to begin with.

You would need:

1. ''Machine Learning (ML)'': AI algorithms can analyze data, learn from patterns, and make predictions or recommendations. In insight mapping, ML can identify significant connections and trends that might not be evident through manual analysis.

2. ''Natural Language Processing (NLP)'': This involves the analysis of text to extract meaningful data. NLP can be used to interpret and categorize insights from unstructured data sources, such as academic papers, news articles, and social media posts.

3. ''Data Visualization'': Advanced visualization tools powered by AI can represent complex datasets in intuitive and interactive insight maps. These maps can help users explore and understand the intricacies of the data more effectively.

Right now I am just experimenting with and learning about Knowledge Graphs within the context of my own Personal Knowledge Management system, but the bigger picture of how all these rapidly evolving tools could be combined is quite exciting. The idea is that if I can have a deep understanding of how it works with data (especially unstructured data) that I am intimately familiar with, then I can figure out how it can be scaled to broader, organizational settings.

[Here is an example of a tenuous connection which would make me consider moving the next few lines to a different post.  For the sake of the post's clarity, I should stick to one key message.  For the sake of exploring broader connections and working on developing better articulations of these connections, I should keep it here.  Since I am more interested in exploring connections and complexity than delivering a simple message, the next paragraph stays].

This idea of exploring new concepts and tools at the individual level (within a PKM system) is connected (somehow) to another argument I have been making:  Individual knowledge workers need some foundational knowledge in Personal Knowledge Management before they are asked to engage in and contribute to corporate Knowledge Management. I think the KM profession missed the opportunity to make that connection more obvious and to leverage individual incentives as a prerequisite for corporate efforts to "manage" knowledge. 

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