From Mind to Information – the Role of AI in Knowledge Encoding The Evolution of Knowledge Encoding

Artificial Intelligence and the Quest for Knowledge

The Postproduction Paradigm – AI and the Art of Remixing

Knowledge exists in our minds, and traditional methods of encoding this knowledge into information face significant challenges. However, AI tools now offer innovative solutions for recording, transcribing, and processing both explicit and tacit knowledge, revolutionizing how organizations preserve and leverage their intellectual capital.

Defining Knowledge

Knowledge exists in our minds, but it’s important to remember that our minds extend beyond our brains. Knowledge can be categorized into two main types:

• Explicit Knowledge is easily articulated, codified, and shared.
• Tacit Knowledge: is difficult to express and is often based on personal experience and intuition.

Information is not Knowledge

Encoding knowledge into information involves converting insights and experiences into various structured formats. Textual information includes written documents, reports, and digital content. Audio information encompasses recorded conversations, podcasts, and voice notes. Visual information captures both visual and auditory elements, such as instructional videos and recorded presentations. Each format allows for different ways to document, share, and analyze knowledge, catering to diverse communication preferences and needs.

When we take knowledge that resides within the human mind and record it in written form, it is transformed into information.

This information does not constitute knowledge.

It requires a person - a "knower" - to interpret and process the written words, converting the information back into knowledge within their own mind.

Credit: David Gurteen

Traditional Methods of Knowledge Capture

Historically, knowledge encoding has relied on:

• Writing: Manual transcription on paper.
• Typing: Digital input into computers.
• Audio Recording: Less common due to several challenges:
  • Transcription Challenges: Transcribing audio into text has traditionally been difficult and often expensive, requiring skilled transcriptionists.
  • Browsing and Searching Limitations: Audio recordings are not easily searchable. Unlike text, where keywords can be quickly found using search functions, audio requires sequential listening to locate specific information.
  • Serial Listening Requirement: To extract specific information, listeners must go through the recording linearly, which is time-consuming.
  • Error-Prone Transcription: Manual transcription is susceptible to errors, especially with complex terminologies or poor audio quality.
• Video Recording: Capturing visual and auditory information provides a richer context but traditionally comes with its own set of challenges similar to audio recordings:
  • Transcription and Analysis Difficulties: Transcribing video content into text is difficult and often expensive, requiring skilled transcriptionists to handle both audio and visual cues.
  • Browsing and Searching Limitations: Videos are not easily searchable. Unlike text, where keywords can be quickly located using search functions, video requires sequential viewing to find specific information.
  • Serial Viewing Requirement: To extract specific information, viewers must watch the recording linearly, which is time-consuming.
  • Error-Prone Transcription: Manual transcription of video content is susceptible to errors, especially with complex terminologies, background noise, or poor audio and video quality.
  • Storage Space and Indexing Needs: Video recordings require significant storage space and proper indexing for efficient retrieval, adding another layer of complexity to their management and use.

The GenAI Revolution in Knowledge Encoding

GenAI has dramatically transformed knowledge encoding:

• Speech-to-Text Technology: Enables easy and accurate transcription.
• Applications:
  • Personal dictation on devices like smartphones and computers.
  • Transcription of existing audio and video recordings (conversations, meetings, interviews, debates).
• Benefits:
  • Increased efficiency in encoding both explicit and tacit knowledge.
  • Significant cost reduction in the knowledge encoding process.
  • Enhanced accuracy and speed in transcription, reducing the error rate and making the information more reliable.
  • Improved searchability and browsability through AI-driven indexing and keyword extraction make it easier to locate specific information within audio and video files.

The Role of Generative AI in Knowledge Processing

Tools like ChatGPT and Claude offer advanced capabilities:

• Analysis of Transcribed Information: Understanding and interpreting the content.
• Expansion of Captured Information: Adding depth and breadth to the information.
• Restructuring of Information: Organizing content for clarity and coherence.
• Repurposing of Content: Adapting information for different uses and audiences.

A GenAI Approach to Expert Knowledge Encoding

Leveraging AI for comprehensive knowledge encoding involves:

1. Conducting Interviews or Conversations with Experts
   • Record the audio of these interactions.
2. Using AI to Transcribe the Audio into Text
   • Ensure accurate and efficient transcription.
3. Employing Generative AI Tools to Process the Transcribed Content
   • Analyze: Understand and interpret the information.
   • Expand: Add insights and details.
   • Restructure: Organize for clarity and coherence.
   • Repurpose: Adapt for various applications and audiences.

Advantages of This Approach

The challenges of encoding tacit knowledge have long been recognized, with explicit knowledge relatively easier to capture. However, the advent of GenAI has ushered in a new era of Knowledge Management. By transcribing interviews and conversations and then efficiently analyzing and repurposing this information, GenAI has revolutionized the process, offering a powerful tool for quickly and cost-effectively harnessing valuable insights.

• Nuanced Knowledge Capture: Captures both explicit and tacit knowledge.
• Collaborative Gathering: Enables multi-person interviews.
• Flexible Timing: Allows for asynchronous or synchronous interactions.
• Efficient Processing: Refines raw information into valuable content.
• Diverse Outputs: Creates various information products from a single source.

AI technology is improving how we encode and process knowledge, making it more efficient and accurate. These advancements enhance the accessibility and usability of information, supporting better knowledge sharing and collaboration. This progress helps us use explicit and tacit knowledge more effectively.

Detailed Resources

• Article: Embodied Cognition by Stanford Encyclopedia of Philosophy (2021)

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