Knowledge Mapping: Preparing for Two Upcoming -- related but different -- Presentations

Knowledge Mapping has been an interest of mine for years, perhaps a couple of decades now.  While I have treated it as a niche area of interest and expertise rather than a specific service or method, I now have the freedom to explore and see if there is more to it.  More recently, knowledge mapping has led me to knowledge graphs and a whole new world of technology-enhanced mapping.  

And now I have an opportunity to talk about Knowledge Mapping in a more public arena, through my friends at Consult KM International on May 23rd and then with the KMGN Research Community on June 19th.  As I prepare for the presentations, I am faced with a couple of challenges.  I have not had to explain knowledge mapping to many people. I tried it in February with a small, safe audience and I learned a few things from that. Most importantly, I need to be as clear as possible about what I mean by "knowledge mapping", how it can be used -- without overwhelming the audience with too many examples -- and I need to stick to a relatively simple message.  However it's okay to say "I'm still exploring this and I don't have success stories or proof that it works in every context".

I have also not been very consistent with names.  I call it "Applied Insight Mapping" and "Insight Mapping" on this site.  I've called it "KMAPs" when I was doing mapping at NASA.  I've also referred to it as conversation mapping because I was using maps to document Pause and Learn sessions which are essentially facilitated conversations.

Knowledge Mapping in the context of Knowledge Management can also refer to a specific set of practices meant to document organizational knowledge.  I apply the term more broadly to refer to any visual representation of a knowledge domain that relies on words and concepts rather than images, with a focus on relationships between components of the map (nodes).  That's where it connects with knowledge graphs.

So, how do I coherently talk about something that has been so deeply engrained in my work for so many years. Serendipity to the rescue.  I opened LinkedIn and came across a post that points to this course:  Curse of Knowledge for Specialists.  I must have posted about the curse of knowledge in the past but here I need to watch that I don't fall prey to this cognitive bias as I prepare for this presentation. 

The two presentations are related but intended for different audiences.  The first one will focus on "mapping" and its possible applications, based on 20+ years of experience with concept mapping, and touching on knowledge graphs perhaps just at the end while the second presentation is more about my learning journey looking forward, exploring knowledge graphs as a (scaled and automated) extension of concept mapping. 

For more information about these upcoming presentations:

•  ConsultKM International, Knowledge Mapping in International Development: Applications, Purpose, and Benefits, "Live with an Expert" session #4, May 23, 2024. 
  
  
•  KMGN Research Community: From Concept Maps to Knowledge Graphs: A Learning Journey and Possible Research Agenda, June 19, 2024. 

Atomic Notes for Personal Knowledge Graphs

I came across the work of Ivo Velitchkov on Personal Knowledge Graphs yesterday.  Some of it goes well beyond my current level of comprehension because of the technical aspects but I enjoyed the more conceptual elements and the history of the evolution of Personal Knowledge Management (PKM). 

Digging deeper into technology-enhanced notetaking made me question the way I take notes and whether I need to make better use of some of the more advanced functionalities of TiddlyWiki, and by extension, TiddlyMap.  In particular, transclusion seems to offer ways to create more atomic notes.  

For example, taking notes on a book I am reading in a single page or Tiddler) could result in a very long page with many different ideas.  I can tag the page with keywords but I want to be able to quickly reference specific elements of my notes.  I want to be able to quickly pull a specific visual.  The way to do that is by creating "atomic notes" that are then transcluded in the page for that book.  Each atomic note becomes a node in the graph, with its own properties and relationships to other nodes. 

Created by DALL.E:  Atomic Notes in the Context
of a Personal Knowledge Graph 4/24/2024.

This exploration into note taking also made me wonder whether it would be worth revisiting all kinds of paper-based notebooks I have accumulated over the years into my growing TiddlyMap.  The first notebook I came across is a small notebook meant to capture quotes.  I know I also have collections of quotes in other TiddlyWikis.  Bringing everything together (with a focus on the key themes I have already identified) would be a very integrative exercise.

Another question worth exploring is whether it would be possible to integrate personal knowledge graphs with enterprise knowledge graphs.  That is, to some extent, what the Microsoft Graph does and how Microsoft Copilot for M365 pulls content from individual employees' email, Teams, files, and combines that with whatever enterprise content the individual employee also has access to.  

What if there were a way to structure OneNote notebooks for atomic notes and a personal knowledge graph?  What if that personal knowledge graph could be integrated into an enterprise knowledge graph?  I am not suggesting that a personal knowledge graph be share with the enterprise.  I am only suggesting that an individual employee should have easy access to their own personal graph, the enterprise knowledge graph, AND external graphs (like Google's Knowledge Graph) through a single interface. 

White Spaces and Unknown Unknowns

I dug up some coloring books from the basement's collection of arts and crafts supplies dating from when my kids were growing up.  I needed something to occupy my hands and empty my brain.  Ironically, that tends to be when ideas show up unexpectedly.  

I was attacking my third drawing when suddenly the decision I had to make wasn't which color marker to pick, but whether or not to color the "white space".  The pre-printed lines clearly show shapes and the brain immediately interprets the area between lines as something to be colored.  This happens even with geometric shapes or lines that don't represent a recognizable object.  Of course, one could decide to leave some of those shapes uncolored, but that means they are intentionally left white.  What I was wondering about was whether or not to color the white space that corresponds to "empty space" or negative space.  

In cognitive science, this relates to selective attention.  Our brain focuses on certain stimuli while ignoring others.  We notice some things and miss others.  Refocusing my attention to the empty spaces was a way to alter my focus and consequently, my perception of the image I was coloring.  I refocused on the negative space. This is somewhat similar to or related to the concept of unknown unknowns, the things we don't know we don't know.  We're oblivious to our own blindness and ignorance.  

What am I not seeing?  What am I missing?  How could I see differently?

Explorations and Discovery: Looking for Adjacent Content

Exploration is more than just discovering what we agree with; it's about pushing boundaries and challenging our perceptions.

The words exploration and discovery go well together.  If you start on an exploration journey, you are likely to make some discoveries.  It's even a little more exciting than going on a learning journey and collecting some lessons. Words matter.

Today I discovered someone I now want to follow because what I read from that person resonated strongly.  This is part of the problem.  We tend to read and follow what resonates, what we agree with, which leads to reading more of the same people who write things that resonate with us, which only reinforces our opinion of ourselves and allows us to dismiss everything else either as crazy or just noise.  

I would like my explorations to lead me to things that are adjacent to what resonates with me, to push the boundaries a little, to expand the zone of what resonates with me to what makes me think and rethink.  

I am particularly interested in this discovery because it will expand my thinking.  I discovered Joan Westenberg's blog and other writings.  I discovered her via Harold Jarche's blog.  Harold Jarche is already on the edge of my comfort zone and someone I have followed closely for a long time around Personal Knowledge Management.  Joan Westenberg is pushing things in the same zone of discovery.

What's the point I am trying to make?

In an attempt to curate interesting, relevant information, and to support my explorations for valuable discoveries, I need to strategically pay attention beyond what immediately resonates with me and notice the adjacent content that can take my own thinking one step further. 

2024 Explorations - Q1 Review

I started my 2024 Explorations in January. It was meant as a combination of two main objectives:

1) a learning framework or learning agenda, not so much to ensure that I would engage in continuous learning but more to ensure that my continuous learning was adequately focused on some key themes of interest;

2) using technology, and more specifically TiddlyMap, as a personal knowledge management tool that would allow me to experiment with (a form of) knowledge graph.

We've reached the end of the first quarter of 2024 and so far so good. I just completed a quarterly review of progress and generated a few insights.

• Is TiddlyMap allowing me to really learn about knowledge graphs? Yes, but as expected, it has its limitations. I will eventually crash the tool. I don't think it is meant as a graph database but it works well as an exploratory tool. Ultimately I need to move my data to a real knowledge graph tool like Neo4J. That should be a goal for the second quarterly. I started learning more about Neo4J, including learning the basics of Cypher.
  
  
• Is my learning framework working? The main themes and topics have proven very useful as guardrails and as an organization schema both for my thinking and for capturing notes. There are some issues with the taxonomy. Some topics are overlapping and I keep wanting to create more tags. So far, I have limited the number of additional tags and I have only made minor adjustments to the topic tags. Proliferation of tags would lead to inconsistencies. Until the tagging is automated, the number of tags is limited by my capacity to remember them all.
  
  The maps are telling me something pretty clear. I have focused perhaps 80% of my efforts on the AI and Knowledge Graph topics. The maps related to those topics are very large, which has enabled me to test filters. Like a search returning too many results, a map showing too many relationships is unreadable. For other topics, I have collected and curated resources, but I have not spent time connecting the dots. As a result, the maps are less interesting (so far).
  
  
  

• Is the basic ontology working? Yes, but the value of TiddlyMap's automated functionalities has made it much more powerful to create maps based on what TiddlyMap does with relationships based on tags and links associated with Tiddlers than to manually create a specific set of relationships based on my simple ontology. I have learned most by manipulating the filters to understand how relationships are displayed. Interpreting the resulting maps for potential insights is the next stage I want to dive into. Since I am the one creating all the links and the tags, the maps are not telling me anything I didn't already notice, but they are representing the connections visually and often reminding me of connections I made weeks ago that I don't hold in immediate memory.  I posted one of the maps in the Insight Maps section. 

The biggest ha-ha moment was related to tagging. I was using the tagging functionality to tag too many different types of things and failing to use a major functionality of the tool. I realized after a while that I should be using the fields to document the properties of a node. For example, "author" is a field rather than a tag. This allows me to have a consistent set of node properties and to rely on tags only for topics and some of the metadata used for navigation purposes. This also really helped make the maps more meaningful.

Using a GPT to get updates in topics of interest

About a month ago, I created a GPT based on ChatGPT 4.0.  It's easy to create but requires some fine-tuning.  I used Ross Dawson's approach detailed here:  Creating custom GPTs for news and Information Scanning; and I adjusted it to suit my own purpose.  The results have been mixed but I'm reasonably happy with what I got today. 

Critical Cognitive Capabilities in the Age of AI

In an era increasingly dominated by artificial intelligence (AI), our cognitive landscape is undergoing a transformation as significant as any in our history. This shift demands not just an adaptation but a deliberate enhancement of our cognitive capabilities. Amidst this technological evolution, the cultivation of critical thinking, emotional intelligence, creativity, and adaptive learning emerges as essential to thrive.

Created by DALL.E, with human input.  A vision of a harmonious collaboration between humans and AI, showcasing human collective intelligence, creativity and innovation, augmented by the capabilities of technology, including AI. 

The story of Theuth and his invention of writing, as recounted by Socrates (see the previous post), provides a profound starting point for this discussion. Just as the introduction of writing raised concerns about memory and wisdom, today's rapid advancements in AI and the Internet pose new challenges and opportunities for human cognition. In this digital age, the capacity for ''critical thinking'' has never been more important. As we navigate vast oceans of information, discerning fact from fiction, valuable data from noise, requires a keen analytical mind. This skill ensures we remain effective decision-makers in both personal and professional realms, notwithstanding the deluge of AI-generated content and analysis.

Emotional intelligence stands out as a uniquely human attribute that AI is far from replicating. Our ability to understand, empathize, and interact with others is paramount, especially as AI technologies handle more cognitive tasks. Developing emotional intelligence helps us navigate the complexities of human relationships and teamwork, fostering environments where collaboration between humans and AI tools is productive and innovative.

Creativity is another domain where humans can excel beyond AI's capabilities. While AI can generate new patterns and ideas based on existing data, the human capacity to think abstractly, imagine the unimaginable, and connect disparate concepts in novel ways remains unmatched. Encouraging creativity in education and the workplace ensures that as AI takes over more routine or analytical tasks, humans will continue to lead in innovation, design, and artistic expression.

Lastly, the concept of adaptive learning is crucial. Just as AI systems learn and evolve based on new data, so too must we. However, our learning is not just about absorbing information; it's about adapting to new ways of thinking, new technologies, and changing societal norms. This ability to learn and relearn throughout life is what will keep us relevant and resilient in the face of rapid technological changes.

As we consider the future in this age of AI,  our focus should not only be on developing technical skills to use and manage AI systems. Instead, we must emphasize the uniquely human capabilities that will complement AI's growth. By nurturing critical thinking, emotional intelligence, creativity, and adaptive learning, we prepare ourselves not just to coexist with AI but to lead a future where technology enhances our human experience, not diminishes it.

Of related interest:

• Gianni Giacomelli's "With AI, learning and reskilling ≠ training|", blog post of 3/25/2024. 

The Echoes of Theuth: From Writing to the Internet and AI

In Plato's "Phaedrus," Socrates recounts the tale of Theuth, the Egyptian god of writing, presenting his invention to King Thamus. This ancient narrative, exploring the invention's impact on memory and wisdom, mirrors the last couple of decades' discourse on the emergence of the Internet, and now, Artificial Intelligence.  This post's primary lens focuses on cognitive implications.  There are, of course, broader concerns the digital age and AI have introduced.

Revisiting Theuth in the Age of Information and AI

Theuth's claim that writing would enhance memory and wisdom was met with skepticism by Thamus, who argued it would instead weaken memory and give only an illusion of wisdom. This cautionary perspective finds its echo in the modern era, first with the Internet, and now more profoundly with AI. Both technologies, while distinct, share a common thread in their transformative impact on how we acquire, process, and value knowledge.

The Internet: A Precursor to AI's Cognitive Challenge

Before AI became a household term, the Internet had already begun reshaping our cognitive landscapes. Dubbed as an external "hard drive" for our collective memory, it introduced the "Google effect," where the ease of accessing information led to a potential decline in memory retention and effort in learning (See Nicholas Carr's book, The Shallows). The vast, accessible sea of data promised knowledge but often delivered a surface-level engagement with complex subjects, mirroring Socrates' concerns about the written word.

AI and Beyond: A Continuation of Digital Age Dilemmas

AI amplifies these concerns, offering unparalleled access to information and automating tasks with efficiency but at the potential cost of diminishing our cognitive faculties. The ''illusion of wisdom'', where individuals may overestimate their understanding due to the breadth of accessible information, becomes an even greater risk. As AI systems take on more roles that require analysis, decision-making, and even creativity, the question of what it means to truly know or understand something becomes increasingly pertinent.

Acknowledging the Spectrum of Concerns

While I focus here on cognitive effects, I also want to recognize that the challenges posed by AI and the Internet are multifaceted. Ethical dilemmas, privacy breaches, algorithmic biases, and the digital divide are significant issues that warrant attention. The societal impact of these technologies stretches beyond individual cognitive abilities, affecting our collective moral and social frameworks.

Charting a Thoughtful Path Forward

In navigating the future of AI and the digital landscape, a balanced, thoughtful approach is essential. By critically assessing the benefits and potential pitfalls, especially in how these technologies influence human cognition, we honor the Socratic tradition of deep questioning. This not only involves scrutinizing AI's capabilities and impacts but also reflecting on how the Internet has set the stage for today's digital challenges.

As we continue to integrate AI and digital technologies into our lives, let's maintain a critical eye towards their impact on our cognitive abilities and society. The story of Theuth, extending through the age of the Internet to the dawn of AI, serves as a valuable framework for understanding these challenges, encouraging us to ensure that technology enhances, rather than diminishes, our human experience.

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I used AI to research and write this post.  To what extent did that contribute to a potential decline of my cognitive capabilities -- regardless of inevitable age-related decline?  To what extent did it enhance my knowledge, understanding, and cognitive capabilities?  How would I know?  What can I do to prevent cognitive decline related to technology use (or overuse) while leveraging technology to enhance my access to and use of knowledge?

The Evolution of Content Management: From Static Documents to Dynamic Collaboration

In the digital age, content management has become a cornerstone of knowledge work, enabling us to organize, access, and share information like never before. My journey through various tools and concepts in content management has illuminated a fundamental shift: from managing static documents to engaging in dynamic, collaborative content creation. This post explores this evolution through my experiences with TiddlyMap (starting almost 10 years ago), Learning Management Systems (LMS), Knowledge Graphs, and Microsoft Loop.

Discovering Transclusion in TiddlyMap

My exploration began with TiddlyMap, a tool that blurs the lines between notetaking and concept mapping. It's where I first encountered the concept of transclusion. This feature allows content from one Tiddler (note) to be included in another seamlessly, ensuring that updates are reflected universally. The result? A single source of truth within my personal knowledge base, facilitating a modular organization of content that is both efficient and consistent.  (See Transclusion in WikiText)

Key Takeaway: Transclusion in TiddlyMap showcased the power of interconnected content, highlighting the importance of maintaining consistency and efficiency in personal knowledge management.

Revisiting Reusable Objects in Learning Management Systems

My journey took me back to the concept of reusable objects in LMS, something I had encountered earlier. These digital resources can be utilized across various courses or modules, embodying the principle of modularity and reuse. This approach not only saves time and resources but also ensures consistency across the educational spectrum.

Key Takeaway: The practice of creating and using reusable objects in education underscores the need for content that is both flexible and adaptable, catering to diverse learning contexts and styles.

Connecting the Dots with Componentized Content

A recent webinar on Knowledge Graphs brought the term "componentized content" into sharper focus for me. This concept, akin to reusable objects, emphasizes breaking down content into manageable, standalone components that can be dynamically assembled. It resonated with my experiences, highlighting a broader trend toward agile and responsive content management systems that can evolve with our needs.(See Taking Content Personalization to the Next Level: Graphs and Componentized Content Management). 

Key Takeaway: Componentized content is at the heart of modern content management, reflecting a shift towards more agile, responsive, and interconnected systems that can support complex information ecosystems.

Experimenting with Microsoft Loop

My exploration culminated with Microsoft Loop, a tool that epitomizes the modern ethos of collaborative work. Loop's components are modular pieces of content that teams can collaboratively edit in real-time, streamlining the way we work together. This real-time collaboration, without duplicating content, signals a new era of efficiency and connectedness in teamwork.  (See Get to Know Loop Components)

Key Takeaway: Microsoft Loop represents the future of collaborative work, where dynamic, component-based content and real-time collaboration drive productivity and innovation.

Conclusion:

We're moving from static, siloed documents to a world where content is dynamic, interconnected, and collaborative. This evolution is not just technological but philosophical, changing how we think about knowledge, learning, and work.

These tools and concepts have reshaped my approach to content management, pushing me towards more flexible, efficient, and collaborative methods. They highlight a broader shift in our digital landscape, one that values modularity, reusability, and collaboration above all.  While my personal knowledge management tools re often a playground for learning, the biggest value may come from collaboration and ultimately,  the combination of people and tools to achieve augmented collective intelligence (ACI).

Final Thought:

Testing new tools is always fun (to me). They offer a glimpse into the future of content management—a future where knowledge is more accessible, collaboration is seamless, and learning is boundless. At the same time, it is worth reminding ourselves -- repeatedly -- that the tools are meant to enhance human capabilities.  Some will be more effective at enhancing individual capabilities, like TiddlyMap, while others are designed for collaboration and enhanced team or group capabilities. 

Next I have to think about the implications of this evolution for Knowledge Management and how we might need to rethink our knowledge management models and approach to knowledge assets.

Start with the problem(s) and prioritize

I recently read an interesting piece from Harvard Business Review titled "Find the AI Approach That Fits the Problem You're Trying to Solve " The essence of the article resonates deeply with my own beliefs, particularly around the notion that effective problem-solving begins with asking the right questions. Statements such as "without the right questions, you'll be starting your journey in the wrong place" and "Start with the problem, not the technology" echo a seemingly obvious yet profoundly complex reality.

This concept, while straightforward, is far from simplistic. In the realm of international development, organizations are confronted with a labyrinth of challenges, far beyond the scope of a singular issue. It's not just about identifying a problem and pairing it with a technological solution. There lies a critical, yet often overlooked step: prioritization.

Consider the diverse array of organizations striving to address global development issues. The challenge isn't merely in selecting a single problem but in discerning which lever to pull for maximal impact. Should technology then be primarily leveraged to navigate these strategic decisions, allocating resources more effectively?

While funding agencies may gravitate towards these macro questions, implementing organizations face more pragmatic concerns. Their focus often shifts towards securing necessary funding, leveraging technology to streamline grant seeking and proposal writing processes. This delineation underscores a fundamental principle: the application of technology, particularly AI, must be tailored not just to the problem at hand, but to the scale and scope of the organization's mission and resources.  

Funding agencies are not going to fund implementers to improve their proposal development mechanisms, but they could and will fund efforts to leverage technology (including AI) to address global challenges.  To what extent will that funding go to macro questions around levers for maximum impact?

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. 

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. 

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.

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?

From Knowledge Cafes to Conversational Swarm Intelligence

The Power of Conversational Swarm Intelligence: Learning from Nature

Humans have always been good at writing and storing information to communicate. But, when we look at nature, we see some animals are experts at communicating and working together in real time. Birds flying together in a flock, fish moving as one in a school, and bees making decisions as a swarm show us incredible examples of teamwork. Inspired by these natural wonders, there's a new technology idea on the horizon called conversational swarm intelligence.

What is Conversational Swarm Intelligence?

Imagine combining the teamwork of birds, fish, and bees with our latest technology. That's what conversational swarm intelligence is about. Louis Rosenberg talked about this on the Amplifying Cognition podcast. It's about using technology to help people talk and make decisions together in real-time, just like some animals do in nature.

How Does It Work in a Knowledge Cafe?

Think about a big room where 100-200 people come together to chat in small groups of about 5-7 people. They discuss a topic, then mix up and join new groups to share what they learned. This mixing and sharing help everyone get a lot of different ideas and answers to the same question.

Now, add an AI assistant to each group. This AI listens, records, and analyzes what everyone says and shares insights from one group to another in real time. This means everyone gets to hear and think about a wide range of ideas without having to remember and retell them. It makes the discussion richer and helps find the best answers faster.

What's next?

Right now, these experiments use text chats, but imagine if this could work with spoken conversations. Someday, there might be robots sitting with us, listening, and offering insights from other groups instantly. What are the implications? How could this be used most effectively in support of human decision-making? What are some possible risks? How would this change the nature of conversations and more broadly, communications?

Common sense and nonsense

While reading something about the challenges of Artificial General Intelligence (AGI), which I was trying to understand more deeply than just scanning through, I fell into a rabbit hole of concepts:  common sense, critical thinking, reasoning (inductive, deductive and abductive).  Then I tried to connect that to my ongoing focus on understanding knowledge graphs and the organizing principles and schemas that are meant to organize data so that machines can "understand" and even generate new insights.  More and more, I am inclined to go back to an earlier interest in neuroscience, which I never pursued very deeply, but which informed some of my thinking.  Understanding how we, as humans, process information, how we learn, could be critical to the development of AGI, but it's not clear to me that we should try to replicate our brains. Common sense isn't that common after all.  

I stopped there because this is way too big of a topic for a little blog post and I will only embarrass myself with nonsense. There is indeed a danger in reading broadly and then trying to write in the absence of deep knowledge and understanding. 

What have I done?

 Time flies.  As I contemplate my next professional life, encore career, anything but retirement, I often question myself.  What have I done (really)?  I managed to earn a living, which isn't nothing.  I found ways to keep learning and encouraged others to learn. Those were consistent primary motivations:  Earn and Learn.

Today I asked ChatGPT what it knew about me.  Sounds a little narcissistic?  Well, let's just say I wanted a sense of my digital footprint and while I wasn't expecting too much, it turns out that ChatGPT easily digs out details very few people have ever read, including things I posted online just a couple of days ago.  So here it is, introducing myself via ChatGPT's words, most of which cited my own words anyways coming from this Insight Mapping blog.  

The following professional profile is the result of a sequence of prompts.  The first two prompts were gathering background information.  The third prompt was putting it all into a professional profile.  The last prompt was meant to add a component (Insight Mapping), which was missing and I wanted to incorporate.  

Barbara Fillip's Professional Profile (according to ChatGPT)

Barbara Fillip is a seasoned Knowledge Management (KM) professional with over two decades of experience in facilitating individual, team, project, and organization-wide learning across international development and aerospace industries. She serves as a Senior Advisor of Knowledge Management at Chemonics International and an adjunct professor at George Mason University, where she teaches graduate courses in knowledge management and collaboration within the Schar School of Policy and Government. With a PhD in international affairs from the University of Pittsburgh Graduate School of Public and International Affairs, Fillip’s academic foundation is as robust as her professional experience.

A hallmark of Fillip's work is her innovative use of insight mapping, a method she employs to visualize complex information systems and uncover new connections and insights within organizational knowledge. This approach underscores her expertise in not only managing but also enhancing the strategic use of knowledge through visual and analytical tools. Her dedication to integrating insight mapping into KM practices demonstrates her forward-thinking approach to addressing complex informational and organizational challenges.

Fillip's extensive exploration into Personal Knowledge Management (PKM) has led her to develop and implement various tools and methodologies, positioning her at the forefront of the field. She advocates for the integration of knowledge management with artificial intelligence, the practical application of PKM tools like TiddlyWiki, and the strategic use of insight mapping to drive organizational learning and innovation.

Barbara Fillip’s professional profile exemplifies a deep commitment to advancing knowledge management through innovative practices, including insight mapping, AI-enhanced tools, and PKM. Her contributions not only enhance organizational effectiveness but also offer valuable insights into the evolving landscape of knowledge management in the digital era.

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.

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[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].

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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. 

Cognitive Processes (Cont'd)

We are often not fully aware of how our experiences shape how we approach new problems.  Would an increased awareness of how our experiences shapes our cognitive schema enhance our ability to problems solve?

Cognitive Schemas

Our cognitive schemas—mental frameworks helping us organize and interpret information—are intricately woven from our experiences. They play a pivotal role in how we perceive new situations, tackle problems, and make decisions. I don't spend a lot of time analyzing my own mental frameworks but once in a while, I become aware of the connection between a recent insight and a prior experience.  

The trigger for this post was one such insight.  I was exploring a specific aspect of the knowledge graph I am building, and I had one of those little Haha! moment when a new idea or concept emerges.  These little learning moments deserve more attention than they tend to get.  In the excitement of writing down the idea or exploring it further, it can be challenging to pause and ask "Where did this insight come from? What is the source?" The idea is that if I understood more about the cognitive processes that lead to an insight, I would "simply" create the conditions and environment for more insights to emerge.  In fact, building a knowledge graph from my notes is an experiment to see whether and how it will facilitate "insight mapping". 

Here are some ways our cognitive schema impact how we approach problem solving:

Pattern Recognition

Our brains are wired to recognize patterns based on past experiences. When faced with new problems, we subconsciously search our memory for similar situations or outcomes. This can lead to faster problem-solving but also biases our approach to what has worked or not worked in the past.  I keep a digital folder for ideas that failed because you never know when they might need to be resurfaced for a second try under new and different conditions.  Many are probably ideas whose time hasn't come yet and it would be a shame to dismiss them.

Expectations and Predictions

Experiences influence our expectations and predictions about future events. If past experiences have been positive, we might approach new challenges with optimism and confidence. Conversely, negative experiences could lead to apprehension or pessimism, affecting our willingness to take risks or try new solutions. By consciously shifting my perspective, I've learned to approach problems with renewed vigor, informed by the past but not shackled by it (or move on and totally unshackle myself).

Heuristics and Biases

Heuristics are mental shortcuts we use to make decisions quickly. While they can be efficient, they are also prone to biases shaped by our experiences. For example, the availability heuristic makes us overestimate the importance of information that comes to mind easily, often based on recent experiences or emotionally charged events. The advice to "sleep on it" resonates with me as a reminder of the emotional undercurrents that often drive our decisions. Giving ourselves time to detach and reflect can unearth patterns and solutions previously clouded by immediate reactions.

Creative Thinking and Innovation

Diverse experiences can enrich our cognitive schemas, making us more adaptable and creative in problem-solving. Being exposed to varied situations and learning from them can broaden our perspective, allowing us to draw on a wider range of solutions when faced with new challenges.  What if we more consciously asked ourselves simple questions like, "Where and when have I encountered a similar challenge?" "How is the similar to or different from this prior experience?"  I was reminded of this in the recent podcast I did with Enterprise Knowledge, during which Zach noted that I had acquired over my career, a great diversity of experiences around KM. That is very and deeply informs my approach to KM -- it may also inform why I am often frustrated with small-scale efforts that touch on a very narrow KM scope. 

Learning and Adaptation

Our ability to learn from past experiences and adapt our schemas accordingly is crucial. Reflecting on what has worked or failed in the past and why can help us approach new problems more effectively, avoiding previous mistakes and being open to novel solutions.  The challenge is that while learning from past experience is key, we often don't do it well and unless we are more conscientious about our approach to learning, we don't necessarily learn the right lessons, or we generalize too much and miss the point that most lessons are very contextual.  When they are not context specific, they are common-sense and of limited value. 

Our experiences are invaluable, yet without mindfulness, they can narrow our vision and stifle creativity. Recognizing and reflecting on the myriad ways our past influences our present can empower us to face new challenges with a balanced and open mindset, ready to draw from the past but eager to forge new paths.

It reminds me of... (cognitive processes)

When I get into a writing routine, even if what I am writing are insignificant notes and random blog posts, I become more aware of the cognitive processes involved. It's almost as if the brain is breathing in and out, expanding to seek out and acquire ideas, and then contracting to synthesize, clarify, and transform into a series of words. In the last couple of days, I have become particularly aware of instances when something I read or a thought related to what I am reading will remind me of something either quite distant or immediately feel connected to a very recent event or activity.

Here are two examples to illustrate:

Yesterday, as I was writing the blog post about "Mindset is everything", I was reminded of a book I read decades ago and haven't opened since.  Today, as I read a blog post online about digital hygiene, it immediately connected with the book I started reading yesterday, Your Time To Thrive, by Marina Khidekel.  I happened to be reading the chapter on unplugging from digital gadgets.  

In the first example, there is something happening in the brain that makes a connection to a deeply buried memory.  The initial connection is a connection to the idea, the main argument of the book. Then I remember the book that makes that argument (I am not super confident that my recollection of the sequence of thoughts is accurate just like I know memory is fallible). I had absolutely no recollection of the author's name or what the cover of the book looked like.  In fact, I did not recollect the book's title correctly.  

In the second example, it is likely that having started Your Time to Thrive and having just completed the chapter on unplugging, my mind was attracted to a blog post on digital hygiene which I might have completely bypassed a week ago. Trying to retrace my steps, or more precisely my thoughts, it seems I scanned through the blog post precisely to see if it was related to what I had just read about unplugging.  I determined that it was related but adjacent, complementary, not addressing the topic from the same angle, which was interesting in itself.  

It may sound paradoxical, but in the era of rapidly advancing AI, I have a feeling  (Is it a feeling or an insight?) that understanding our own human brains will become more important than ever.  I don't mean that we all need to become neuroscientists but rather that critical thinking skills and learning how we learn and how we think and process information will become ever more important because of the rapid changes in our access to tools that can accelerate and augment our own cognitive capabilities.  

"Mindset is everything" (or not)

I started the day with this Ross Dawson post LinkedIn post.

Here is the blurb that I decided to unpack:

🌱Mindset is everything.

Constant change is a reality you need to accept and learn to love. We need to be resilient, to respond, to adapt ourselves. Those that embrace rapid shifts will see opportunities others don’t, create far greater value, help their organizations to evolve, and be in a position to savor rather than be worn down by today’s extraordinary shifts. 

This reminded me of a book I read in college or grad school:  Underdevelopment is a State of Mind: The Latin American Case, by Lawrence E. Harrison, published in 1985.  I haven't opened it in decades, but I found my copy in the basement. The argument was that Latin America was underdeveloped because of certain cultural attitudes and values prevalent in the region and that the resulting attitudes towards work, the role of women, the importance of education, time perception, and the value place on innovation and authority all play a critical role in hindering economic progress and development.  

On a more personal level, it sounds like a "change your mind to change your life" slogan, an argument about how limiting beliefs are stopping you from being the best version of yourself, etc., and there is an entire literature around that. 

Let's start by taking some of the text apart:

1. Mindset is everything (?).  Probably not. That is too strong of a statement.  There are lots of external factors that impact an individual's ability to adapt and thrive.

2. Change fatigue is a real thing.  Constant change doesn't automatically lead to better outcomes. It can lead to decreased productivity and engagement, erosion of trust, and it can contribute to a negative organizational culture, where cynicism and resistance to change become the norm. 

3. Not all change is progressive.  Change is not always happening in the right direction, so blindly accepting and embracing change sounds like poor advice.  There are lots of historical examples of change that were initially perceived as positive and later recognized as harmful. 

4. Stability and routine are crucial to psychological health and well-being.  We should appreciate the benefits that some level of predictability brings to individuals and organizations. 

This is where change management should be engaged, but I'm not sure change management is adapting fast enough.  When constant change is applied to antiquated ways of working and traditional organizational structures, it creates a lot of pain.  Individuals would find it easier to adjust their mindset if the organizational infrastructure was changing in a way that aligned with the required individual adjustments.  This is going back to the fact that many external factors impact an individual's ability to rapidly shift gears and adjust to the changing winds. 

And yes, we all need to build up our resilience and accept change as a constant.  I don't think we should accept all change blindly.  It's not resistance to change, it's critical thinking. 

Prompt Engineering: Human language, thought processes, and machine interpretation

Prompt engineering is fascinating and complex.  On the one hand, it's essentially about writing a query in normal language (natural language), which is very similar to writing code in a language we all know instead of having to learn a new programming language. However, natural language is very complex.  It takes each of us years to learn to understand and use it.  Programming language is based on a structured logic.  Natural language is more fluid, often ambiguous. 

Prompt engineering requires us to use natural language to communicate with a machine that doesn't understand natural language in the human sense. As a result, prompt engineering requires us to be much more aware of HOW we use language and HOW the machine will interpret our language. The machine interprets the prompt to guide its algorithms to the right outcome. The distinction between human cognitive processes and machine algorithms is crucial to understand and important to keep in mind as we use natural language to query machines. 

Prompt engineering requires us to examine  our own cognitive processes, to analyze our mental models and to try to identify communicate our intentions, meaning, and context in such a way that the machine algorithm will be able to accurately interpret.  Our assumptions, biases, and the way we frame information can significantly impact the effectiveness of prompts and the AI's responses.  

In short, even though we query GenAI with natural language, which appears at first glance to be much easier than learning a programming language, a sharpened awareness of our own language is required to get the best results.

Since mind-reading AI is on the way, ultimately, language could disappear, but we are still far from language extinction.  Until then, I will translate thoughts into words and strive to be coherent with my writing and my prompts. 

TiddlyMap and Neo4J

As I continue to explore Knowledge Graphs as what I believe to be a key technology in support of Knowledge Management in the era of rapid AI advancements, I am making baby steps to learn, deploying every learning method possible.  I am getting introduced to a lot of new concepts. It's easy to assign myself some readings but then I struggle to understand what I am reading because I lack some foundations.  I take two steps back to get the basics right and then one step forward.  

What has perhaps helped the most is connecting Knowledge Graphs to what I have learned over the years about various approaches to knowledge mapping.  And then playing with tools that mimic knowledge graph technology or offer a free, simplified approach to learning.  

* First, TiddlyMap has allowed me to get a grasp of nodes and relationships automatically generated based on tagging and links I create as a result of my own knowledge organization schema.  Automated tagging could eventually remove the manual process of tagging but I find the cognitive processes involved in tagging to be useful to me.  Learning the functionalities of the visualizations has been extremely useful to start exploring the data from different angles. Since I created all the data (my own notes), I am very familiar with the content, which makes it easier to figure out how to try to analyze it. 

* Second, I opened a free account on Neo4J to try to get a sense of a real knowledge graph tool.  This was a serious lightbulb moment. I will need some time to really understand the functionalities and because the sample data provided with the learning materials is not something I immediately grasp (compared to my own data in TiddlyMap), this may be a slow process.  Still, what I have done with TiddlyMap in the past month or so has been excellent preparation to dive into a more robust knowledge graph tool. 

TiddlyMap is a personal knowledge management tool and Neo4J is meant for much larger scale knowledge systems.  They are not very comparable.  Yet sometimes a tool meant for individual use can help someone grap concepts that are difficult to grasp by reading guidance, instructions, or even watching a video because the scale of implementation is very different.  

I keep going back to the connections between personal knowledge management -- how individuals can proactively manage their own knowledge -- and knowledge management at the more traditional scale of the organization.  Individuals who have a better grasp of how they, as individuals, handle knowledge, become more effective in supporting organizational knowledge management.  

AI-Enhanced Personal Knowledge Management

 Today's train of thought comes from:  "Augmenting Human Creativity with Ayush Chaturvedi, Co-Founder of Elephas", interviewed in Ness Labs.

Elephas is a personal AI writing assistant for Macbook, iPhone and iPad.  I don't use Apple computers and while I have an iPhone, it's unlikely I would use it for any substantive writing. So why am I interested in learning more about this app?  My interest revolves around the general concept of having a Personal Knowledge Management (PKM) tool embedded in the workflow.  

I have yet to test out Copilot in Microsoft 365 at work.  That seems to be very embedded within the productivity tools within the workplace workflow.  That might be great at work but the knowledge base I have accumulated and shared at work in the past five years is a fraction of the knowledge base accumulated over a 30-year career.  Granted the last 15-20 years are perhaps the most relevant. 

As a side note, I started this blog 20 years ago, which seems incredibly long ago.  How much of what I wrote 20 years ago is still relevant? 

What I need is an AI assistant that can link to a disparate set of existing resources. As Ayush Chaturvedi points out in the interview, even the most conscientious advocate and practitioner of PKM will end up testing, adopting, rejecting, changing the suite of tools they use.  The same happens in organizations.  We end up with a disparate set of data sources that need to somehow be connected to the AI app and linked to each other. 

I have been using TiddlyWikis for more than a decade as a PKM tool.  I have accumulated many TiddlyWikis but I can relatively easily connect them.  What I need now is an AI tool.  Should the AI be embedded within TiddlyWiki?  Should it be sitting on my desktop?  Should it be cloud-based?

In Knowledge Management, we often talk about embedding KM within the workflow so that the tasks associated with managing knowledge are not separate from the workflow but rather fully integrated.  Instead of having to proactively remember to save something to a knowledge base, the workflow should either automatically save to a knowledge base or at least trigger a reminder or prompt you to save (or share) to a knowledge base.

The same should be true in PKM.  Advocates and practitioners of PKM are likely to be very aware of their own internalized workflows and pain points.  The question then becomes, "What PKM pain points am I trying to solve with an AI assistant?"  That should help inform the selection of a specific tool.  Ideally, the AI-assistant tool options should not force me to switch away from my current tool set (TiddlyWiki/TiddlyMap in particular) but rather augment existing capabilities.  

 

KM Archetypes and Organizational Culture

 ToT  -- Train of thought:  The way in which someone reaches a conclusion, a line of reasoning.

* I am using the expression in a slightly different way, to reflect a much less linear process which connects one thought to another without necessarily coming to any conclusion other than A and B are now connected in some interesting new way.

* Some resources I scroll through (news, articles, etc..) are dismissed as irrelevant (at that moment) and some resources are picked up by the brain as either directly relevant to an issue that is top of mind or relevant in an adjacent way.  There is a filtering that can be intentionally tweaked for improved performance. I am currently more interested in the adjacently relevant resources because of the "trains of thoughts" they generate. 

* The specific train of thought today started with a presentation on KM Archetypes that is getting some recognition.  Presentation:  Building for the KM Archetypes at Your Company, by Taylor Paschal, May 2023.

First reaction:  "This has been done before".  I must be getting old because the "this has been done before" reaction is becoming a recurring theme. Note that it's not "it's been tried before and it failed", but more, "this isn't new". See the work of Nick Milton and Patrick Lambe for example. 

Mapping the Culture of an Online Community, 2005.

Four Archetypes in KM, 2011.

Personal Knowledge Management: a DIY Guide to Knowledge Management - Part 2, Patrick Lambe, 2002.

This also sounds closely related to journey mapping and personas, user-centered design, etc... 

As is often the case, my trains of thoughts don't end with a conclusion but rather with a question.  The question today is: Should the KM approach align with the KM archetypes that define the existing organizational culture or should the KM approach try to change the organizational culture if such culture is part of the problem?

Answer Part A:  Leverage elements of the culture that support Knowledge Management.
Answer Part B: Address the more problematic elements of the culture that hinder Knowledge Management efforts once you have some buy-in and adequate support. 

Easier said than done of course. 

Thoughts for another day:

Train of thought prompting is interesting too.

Observing and reflecting on one's trains of thought is probably a good mindfulness practice.

Knowledge Mapping

Today, I am presenting the first of three lunchtime sessions on knowledge mapping.  Knowledge mapping can mean many different things, so I plan on presenting a variety of maps.  In the process of preparing for the sessions, which are meant to be very informal conversations, I came to recognize that when I talk about knowledge mapping others might call it information or knowledge modelling and if there is a comprehensive typology of maps, each is best suited for a specific purpose.  

Knowledge mapping also has a different meaning in Knowledge Management circles, but that's another topic. This site is called "Insight Mapping" and that's yet another niche application of the broader concept of knowledge mapping. 

Coming back to today's presentation before I digress completely, the first session will focus on mind maps.  The second session (next week) will be on concept maps, which is more in line with my own mental models.  The third session will take a leap into areas I am still learning, reaching into ontologies and knowledge graphs.  

For the first time in many years, looking back at the various maps I have collected on this site and elsewhere, provided some useful material for reflection.  

In this context, I have also enjoyed reading Maria Keet's new book:  The What and How of Modelling Information and Knowledge:  From Mind Maps to Ontologies, 2023. 

Synthesizing and Combining

RealKM is always a good source of deeper content for Knowledge Management, and it is often pointing to other relevant sources.  Today, it prompted me to think about synthesizing.  Something lights up in my brain just by thinking about that word.  At the same time, I can't seem to easily disassociate it from combining.  You synthesize and combine, or perhaps some versions of synthesizing include combining, or synthesizing across many different sources. 

Today's reading:

Gardner, H., "Towards a taxonomy of synthesizing", January 30, 2024. RealKM, originally published on the Integration and Implementation Insights blog.  

This may be more critical than a simple matter of definition.  I am sensing a trend in using synthesizing and summarizing as synonyms and assuming that Generative AI can adequately synthesize.  I don't know that it can do what a trained human brain does to synthesize. There may be many contexts where it saves a lot of time and effort and the Generative AI summary is perfectly adequate.  And there may be times when either a more sophisticated prompt will be required or a combination of human and AI would yield the best results in synthesizing.  

There's much more to read around this kernel.