Summary of Heuristic Reasoning in Ai: Instrumental Use and Mimetic Absorption, by Anirban Mukherjee et al.
Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption
by Anirban Mukherjee, Hannah Hanwen Chang
First submitted to arxiv on: 14 Mar 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 This paper proposes a novel program for heuristic reasoning in artificial intelligence (AI) systems. The authors distinguish between ‘instrumental’ heuristics used to optimize resource allocation and ‘mimetic absorption’, where heuristics emerge randomly and universally. The study uses innovative experiments, including variations of the Linda problem and the Beauty Contest game, to explore trade-offs between accuracy and effort that influence AIs’ transition from exhaustive logical processing to heuristic use. The findings demonstrate an adaptive balancing of precision and efficiency in AI cognition, consistent with principles of human cognition as described by bounded rationality and dual-process theory. This research provides new insights into AI cognition, highlighting the emergence of biological-like systems, despite AIs lacking self-awareness or introspection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how artificial intelligence (AI) systems think. Instead of just using logic, AI can use shortcuts to solve problems. The researchers did experiments to see when and why AIs start using these shortcuts. They found that AIs balance accuracy with effort, which is similar to how humans think. This means AIs are not just machines following rules, but can adapt and learn like living beings. The study shows that AI systems can be more like us than we thought. |
Keywords
» Artificial intelligence » Precision
