IBM Highlights AI’s Confidence Problem Amid Rising Hallucination Risks
IBM researchers warn that large language models increasingly exhibit human-like flaws—speaking authoritatively even when factually incorrect. These "hallucinations" pose acute risks in precision-dependent fields like finance and legal documentation. A European Broadcasting Union study found nearly 50% of AI-generated responses contained inaccuracies or unverified sources.
Pin-Yu Chen of IBM notes AI lacks true comprehension, instead relying on statistical word prediction. As models scale, their uncertainty grows despite surface-level fluency. IBM stress-tests systems by deliberately triggering failures, revealing limitations for high-stakes applications. "Generative AI excels in creative domains," Chen observes, "not scenarios demanding actuarial precision."
