Monday, March 18, 2024

KM and AI in the Workflow

We (KM professionals) often talk about embedding KM processes into the workflow so that KM isn't an additional burden on top of other processes.  And now we see a new push to embed AI in workflows. Beyond using a GenAI interface like ChatGPT, GenAI applications can be fully integrated within the tools employees use in their daily work. Microsoft's M365 Copilot is an example of that integration.  I also just saw how this integration works in Coda.

With all the excitement over the new GenAI capabilities and bells and whistles of potential integration, let's pause to figure out how to best combine human elements of KM that leverage the best of human intelligence, human critical thinking.  If we are going to dissect a process or set of processes in a workflow to integrate AI, we might as well spend some time thinking through where and how human intelligence will add value.  Let's not apply AI just to save time and increase productivity.  Let's revisit our workflows and integrate both AI and KM to give our brains more time to think.  

How can we both speed up (boring, tedious tasks) and slow down to think within the same workflow?

By carefully designing workflows and fostering a culture that values both AI efficiency AND human insight, organizations can create a powerful synergy.  A balanced approach would ultimately lead to more innovative and thoughtful outcomes. 

Saturday, March 16, 2024

From Montaigne's "Essais" to Knowledge Graphs

Pretty much everything leads to a thought related to knowledge graph these days. Here is today's train of thought:

I was considering reacquainting myself with Montaigne's essays for a number of reasons.  

  1. The style and how it relates (or not) to the blogging of today
  2. The humanism/humanistic aspect of his writing and how it relates (or not) to today's conversations around humans and AI.  
  3. His knowledge skepticism, introspection, questioning of his own knowledge, asking "Que Sais-je?"/What do I know?

 Digression Warning!

Montaigne was one of the authors I needed to study deeply in high school (French High School) to prepare for one of the end of high school exams.  In fact, the French Literature exam was not at the end of the last year of high school but at the end of the second-to-last year.  This involved very intense literary text analysis (for a 16-year-old) and an oral exam that required both presentation of a specific text and answering questions about the text from an examiner. You had to prepare a number of texts, come to the oral exam with a list, and the examiner would pick one and start drilling you.   I remember that our teacher preparing us for this exam was very demanding and therefore prepared us very thoroughly.  I bet that if by some miracle my list of prepared texts was put in front of me, I would suddenly remember a lot about each of them. Well, no great miracle needed. I found all my high school exams in the basement -- where all matters of interesting knowledge artifacts can be found.  I also have some of my handwritten (cursive), in-class philosophy exam essays, but I digress even within the digression, a sure sign that this should be a separate post. 

A couple of years later, I would find myself in English 101 in college in the US, totally lost trying to analyze Shakespeare and other English language literature not only because English was still challenging for me, but because the type of text analysis expected of students seemed so different.  I didn't "get" the assignment and struggled in English 101.  Perhaps this was an early lesson in how language, literature, and culture are so interconnected and part of what makes us so uniquely human.

End of Digression

I went down to my basement book collection and while I don't seem to have any Montaigne on hand, I did find a "Dictionnaire de Citations Francaises," 1978 edition. Luckily, quotes from long-deceased authors are reliably static, so this isn't a book that would age with time.  In fact, it's probably more accurate than most web-based collection of quotes.  I wanted to dig into some Montaigne quotes.  


There are multiple pages of Montaigne quotes, all from his "Essais". 

It's a heavy book, like most physical dictionaries, with a narrow page.  It's also a beautiful example of organized knowledge, with multiple indexes and numbered references.  I can search by topic, by author, by historical period.  So, immediately, I think... this needs to be turned into a knowledge graph.  I want to be able to visually SEE how these 16,460 quotations are connected.  Would it tell me something I can't possibly see by reading the dictionary?  I would think so. Perhaps I should try on a small scale.  

That being said, focusing on individual quotes extracting from essays could really fail to convey the context and full breadth of meaning and nuances that you would get from reading the full essays.  If I were asked to explain the meaning of a quote, wouldn't I want to know what was written before and after the specific quote?  So, while a knowledge graph based on individual quotes might be interesting as a small scale experiment, I can already see how it would have significant flaws, unless it could be paired with access to the full text for sensemaking purposes. 

Monday, March 11, 2024

Two Layers of Knowledge Architecture

I've come across two different approaches or definitions of "knowledge architecture", and by extension, "knowledge architect".  I'm not sure whether talking about them as two layers is accurate, but these two approaches are not mutually exclusive.  In fact, they complement each other.  

#1: Strategic Framework:
Knowledge architecture as the framework for knowledge management, which could be the foundations for a knowledge management strategy and would include the traditional people, process, technology, and governance.  This is a domain much more closely associated with organization development and learning, integrating elements to leverage both tacit and explicit knowledge. 

#2: Organizational Schema: Knowledge architecture as the rules and schemas for organizing knowledge, which focuses on explicit knowledge and/or data (structured and unstructured). This is a domain much more closely associated with information management and now with AI, big data, etc.  It's the domain of taxonomies, ontologies, and knowledge graphs.

This is not a new story in knowledge management, but with each wave of new technology, we need to be reminded of the need for a basic knowledge management framework, a Strategic Framework (#1 Knowledge Architecture), preferably before diving into the exciting depth of the Organizational Schema (#2 Knowledge Architecture). For one, it would help organizations approach technology vendors and assess technology solutions.

Thursday, March 07, 2024

Thoughts around Leveraging Credibility Perception Theory

 This is another early morning (useful) rabbit hole which started with a post on LinkedIn about a recently published paper that "examines how individuals perceive the credibility of content originating from human authors versus content generated by large language models, like the GPT language model that powers ChatGPT, in different user interface versions."  (See "Do You Trust ChatGPT" for the original paper)

I was intrigued by the theoretical foundations for this type of research rather than the results of the specific study, so I went looking up information about credibility perception theory.  Obviously, I'm not going to catch up on all the relevant theoretical perspectives in a couple of hours of early morning explorations, but this initial dive generated some questions?

First round of questions:  Is this issue with credibility perception specific to technology-generated or technology-mediated information and our digital world?  How much of it is as old as human have applied, or failed to apply critical thinking?  How much of it is based on cognitive biases and the complexities of the human brain that exist regardless of technology's impact?  Conversely, how much of it is impacted by technology and especially the latest technologies that are so persuasive at times.

Second round of questions:  Are there variations or nuances in how credibility perception theory applies to textual information vs. visual information?  I was thinking about PowerBi dashboards and other types of quantitative data visualizations that people love.  How would this apply to concept maps and then more broadly, to knowledge graphs?  

Third round of questions: Based on answers to all of the above, how would the development of an ontology which would be the foundation for a knowledge graph by impacted by these insights around credibility and trust?  In other words, how could we leverage insights from credibility perception theory to develop and apply good practices in the development of ontologies and associated knowledge graphs?