🤖 The Shocking Weakness of AI: It Still Doesn't Understand the Word 'NO'! 🚨

Artificial Intelligence can beat humans in chess, diagnose diseases, and even write like Shakespeare… but guess what? It still cannot understand one of the most basic words in human language — 'NO' 😳. An innovative study from MIT reveals that vision-language models (AI that process both images and text) fail miserably when it comes to negation — words like 'no', 'not', or 'does not qualify'. This failure is not just embarrassing — it's dangerous, especially in high-stakes fields like healthcare and law ⚠️.

Imagine a doctor using AI to help diagnose patients based on X-rays. If a patient does not have an enlarged heart, the treatment path is completely different from that of a patient who does. But current AI models often ignore that small but critical word — 'no' — and assume that the patient has an enlarged heart anyway. 😱 Why? Because these models were not trained to reason through logic — they were trained to mimic language patterns. And most of the images used in training simply do not mention what is not in them — who captions a photo with 'no helicopters in the sky'? 🙄

MIT researchers tested these AIs with complicated image questions using negation. What were the results? Disastrous. ❌ Most models performed worse than random guessing, especially when captions included negative words. What is the root problem? An unpleasant habit called affirmation bias: the AI assumes that things are present unless explicitly told otherwise (and even then, it might ignore it). 😬 Even when researchers adjusted the models using synthetic data that included negations, performance only improved slightly.