Unpacking AI’s Clock-Telling Conundrum
In an intriguing study featured in IEEE Internet Computing, researchers led by Javier Conde explore why multimodal large language models (MLLMs) struggle with reading analog clocks—tasks that even novice humans can excel in.
Key Findings:
- Dataset Construction: The team created a dataset of over 43,000 synthetic analog clock images.
- Model Performance: All tested MLLMs initially struggled, showing difficulties in discerning short from long hands.
- Training Impacts: Performance was enhanced with additional training, but models faltered on unfamiliar clock styles.
- Spatial Orientation: Errors in recognizing clock hands led to significant mistakes in time interpretation.
These shortcomings highlight the crucial need for MLLMs to undergo extensive, varied training—not just simple pattern recognition.
Will AI models ever master the art of telling time accurately? Only time will tell!
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