Summary of Dual Thinking and Logical Processing — Are Multi-modal Large Language Models Closing the Gap with Human Vision ?, by Kailas Dayanandan et al.
Dual Thinking and Logical Processing – Are Multi-modal Large Language Models Closing the Gap with Human Vision ?
by Kailas Dayanandan, Nikhil Kumar, Anand Sinha, Brejesh Lall
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 In this paper, researchers propose a novel adversarial dataset to investigate the dual thinking framework in human vision. They demonstrate that deep learning models can correct errors in intuitive processing, but their logical processing capabilities have not kept pace with advancements in intuitive processing. The study highlights the importance of integrating logical processing capabilities into AI-based systems, particularly in safety-critical domains like autonomous driving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers found that humans process images using both fast, intuitive, and slower logical thinking. They developed a dataset to test this idea, showing that deep learning models can correct errors in intuitive processing, but struggle with logical processing tasks. This study highlights the need for AI systems to integrate logical thinking capabilities to improve performance and reliability. |
Keywords
» Artificial intelligence » Deep learning
