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IMAGINATION ENGINEERING: 21 Foundational Thought Operations

29 Sunday Dec 2024

Posted by Ripsu-sama in AI theory, cognitive architecture, cognitive modeling, imagination engineering, philosophy, thought operations

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agi, ai, cognition, cognitive architecture, conceptual engineering, Creativity, imagination, philosophy, science, thought operations

Based on the author’s discussion with ChatGPT o1, below is a concise list of some foundational thought/imagination operations we’ve identified so far, each with a short description.

This list is one possible ordering of these operations, arranged from most foundational (simple transformations on what’s already present) to more complex (involving abstraction, context shifts, and temporal or evidential reasoning). Of course, any such ordering is subjective; different thinkers might sequence them differently based on how they conceptualize “foundational.” Still, this list gives a helpful progression from basic acts to more advanced cognitive maneuvers.


1. Addition or Subtraction

  • Definition: Including or removing elements or phases from a mental construct.
  • Example: Expanding a story by adding a new character, or simplifying a recipe by cutting out ingredients.

2. Partitioning or Unifying

  • Definition: Splitting elements or phases into distinct parts, or merging multiple elements or phases into a whole.
  • Example: Breaking a problem into smaller subproblems, or consolidating scattered notes into a single outline.

3. Enumerating

  • Definition: Systematically listing or mapping out all possible combinations or permutations to ensure none are missed.
  • Example: Pairing {red, blue, green} with {circle, square, triangle} to generate nine distinct design ideas.
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AGI SPECULATION: Architectural additions for LLMs to nearing AGI

04 Monday Dec 2023

Posted by Ripsu-sama in AI theory, cognitive architecture

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agi, ai, cognitive architecture

Sketchy work-in-progress:

☆☆☆

LLMs, to become human-level problem-solvers, need the same architecture of mind which the humans have. Most of all, I think LLMs need imagination. Imagination is the inner sense of sight. With imagination, and some operating principles of imagination, LLMs could become, for some more areas, human-level problem-solvers and more. Deep Learning Networks are capable of producing, with words, all the required intellectual operations of a problem-solving imagination: future steps should include giving them the imagination required to solve more tasks.

This architecture I imagine, in its simplest formulation, as follows:

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