Summary of On the Consistent Reasoning Paradox Of Intelligence and Optimal Trust in Ai: the Power Of ‘i Don’t Know’, by Alexander Bastounis et al.
On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of ‘I don’t know’
by Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar, Ben Adcock, Anders C. Hansen
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC); Probability (math.PR)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Consistent Reasoning Paradox introduces a fundamental limitation on human-like intelligence in AI, demonstrating that any AI striving to mimic human reasoning will inevitably hallucinate (produce incorrect yet plausible answers) infinitely often. This paradox highlights the trade-off between consistency and fallibility, showing that detecting these hallucinations is strictly harder than solving the original problems. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The Consistent Reasoning Paradox shows that if an AI always answers and tries to reason like humans, it will make mistakes infinitely often. It also shows that there are problems where an AI can give a correct answer but not explain how it got the answer. This means that any trustworthy AI must be able to say “I don’t know” sometimes. |