Summary of Inference Of Abstraction For a Unified Account Of Symbolic Reasoning From Data, by Hiroyuki Kido
Inference of Abstraction for a Unified Account of Symbolic Reasoning from Data
by Hiroyuki Kido
First submitted to arxiv on: 13 Feb 2024
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 The proposed paper presents a unified probabilistic framework for various types of symbolic reasoning from data, inspired by Bayesian approaches to brain function in neuroscience. The authors characterize these forms of reasoning using formal logic, specifically the classical consequence relation, empirical consequence relation, maximal consistent sets, maximal possible sets, and maximum likelihood estimation. This theory offers new insights into achieving human-like machine intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a single framework for understanding different types of symbolic reasoning from data. The researchers use ideas from brain function to develop a way to think about these forms of reasoning using logic. Their work gives us new ideas for making machines more intelligent like humans. |
Keywords
* Artificial intelligence * Likelihood
