Summary of Human-like Geometric Abstraction in Large Pre-trained Neural Networks, by Declan Campbell et al.
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks
by Declan Campbell, Sreejan Kumar, Tyler Giallanza, Thomas L. Griffiths, Jonathan D. Cohen
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neurons and Cognition (q-bio.NC)
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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 investigates whether artificial intelligence (AI) can replicate human geometric abilities by analyzing three key biases in geometric visual processing: complexity, regularity, and the perception of parts and relations. By revisiting empirical results from cognitive science on geometric visual processing, the study identifies these biases and tests them in humans and large pre-trained neural network models used in AI. The findings suggest that AI can demonstrate more human-like abstract geometric processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is getting better at doing things that were previously thought to be unique to humans. One area where this is happening is geometry – the way we understand shapes and patterns. Some scientists have said that humans are special because our brains use symbols to help us do math and understand shapes, but AI has been making progress in this area too. This study looked at what makes humans good at geometry and found some clues about how to make AI better at it too. |
Keywords
» Artificial intelligence » Neural network
