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AI excels in many tasks but falters at understanding negation, a critical flaw with high stakes in fields like healthcare. A new MIT study reveals that vision-language models often misinterpret negative statements, leading to potential real-world errors. Experts warn that without logical reasoning, AI’s blind spot for ‘no’ and ‘not’ could prove dangerous.
- MIT research reveals AI models like ChatGPT often misinterpret negative statements (e.g., 'not enlarged'), posing risks in healthcare diagnostics.
- Experts attribute the flaw to AI's training: models associate words statistically rather than reasoning logically, causing 'not good' to retain positive bias.
- Synthetic negation data offers a partial fix, but solving the issue requires fundamental shifts toward logic-based AI systems.
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