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About this paper
Arseny Moskvichev, Victor Vikram Odouard, Melanie Mitchell
ConceptARC, a new visual reasoning benchmark from the Santa Fe Institute, systematically evaluates AI systems' ability to generalize abstract concepts by grouping tasks around 16 fundamental concepts. Evaluations revealed a substantial performance gap: humans achieved over 80% accuracy across all concepts, while the best AI systems (top Kaggle winners, GPT-4) performed significantly lower, never exceeding 80% and often below 30% accuracy.
Key insights
- ConceptARC provides a diagnostic evaluation tool for conceptual abstraction, designed to prevent systems from succeeding via shortcut learning by testing generalization across concept-grouped tasks.
- A substantial gap of over 40 percentage points exists between human and machine performance in abstract visual reasoning, highlighting current AI systems' limitations.
- The poor performance of both program synthesis and large language models suggests that current AI paradigms may be fundamentally limited in achieving human-like conceptual abstraction and generalization.
Summary by alphaXiv.
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