Summary of Tumbug: a Pictorial, Universal Knowledge Representation Method, by Mark A. Atkins
Tumbug: A pictorial, universal knowledge representation method
by Mark A. Atkins
First submitted to arxiv on: 22 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Tumbug, a pictorial knowledge representation method designed for commonsense reasoning in artificial general intelligence (AGI). The authors draw parallels with Roger Schank’s Conceptual Dependency theory, but emphasize the pictorial nature of Tumbug and its 30 components based on scientific concepts. The proposed SCOVA framework consists of five Basic Building Blocks that seem to be a universal foundation for all knowledge representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial general intelligence is getting smarter! Researchers are trying to figure out how humans think, so we can make machines that can too. One key to making super-smart machines is to help them understand common sense, like “if you drop something, it will fall.” This paper shares a new way to represent knowledge using pictures and simple concepts. It’s kind of like a special language for computers to understand the world. |
