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News articleIEEE Spectrum

AI Models Fail Miserably at This One Easy Task: Telling Time

View original at spectrum.ieee.org
AI Models Fail Miserably at This One Easy Task: Telling Time <img src="https://spectrum.ieee.org/media-library/a-digitally-structured-tree-with-a-melting-clock-hanging-off-one-of-its-branches-the-concept-resembles-salvador-dali-s-persist.jpg?id=62053134&width=1200&height=800&coordinates=0%2C133%2C0%2C134" /><br /><br /…
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  • If a MLLM struggles with one facet of image analysis, this can cause a cascading effect that impacts other aspects of its image analysis

    80% confidence
  • We cannot take model performance for granted and extensive training and testing with varied inputs is necessary to ensure models remain robust against diverse real-world scenarios

    80% confidence
  • If the MLLMs made an error in recognizing the clock hands, this in turn resulted in greater spatial errors

    80% confidence
  • Reading the time is not as simple a task as it may seem, since the model must identify the clock hands, determine their orientations, and combine these observations to infer the correct time

    80% confidence
  • While such variations pose little difficulty for humans, models often fail at this task

    80% confidence

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The observation date (2025-12-27) precedes Q1 2026, making it logically impossible to have actual Q1 2026 cash data at that point. Q1 2026 would not end until March 31, 2026. Additionally, the magnitude of the difference ($45.3B vs $132.42) is implausibly large even as a normal quarterly change for Apple. While different fiscal periods can show different values, the timing relationship here suggests a data integrity issue rather than legitimate period-over-period variation.
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