AI still can't do ‘basic tasks' such as tell the time or understand a calendar
AI still can't do 'basic tasks' such as tell the time or understand a calendar, researchers have found.
State-of-the-art AI models are unable to reliably interpret clock-hand positions or correctly answer questions about dates on a calendar, according to the team from the University of Edinburgh.
Understanding analogue clocks and calendars requires a combination of spatial awareness, context and basic maths - something that remains challenging for AI, the team says.
Researchers say overcoming this could enable AI systems to power time-sensitive applications like scheduling assistants, autonomous robots and tools for people with visual impairments.
The team tested whether AI systems that process text and images – known as multimodal large language models (MLLMs) – can answer time-related questions by looking at a picture of a clock or a calendar.
They looked at various clock designs, including some with Roman numerals, with and without second hands, and different coloured dials.
Their findings show that AI systems, at best, got clock-hand positions right less than a quarter of the time.
Mistakes were more common when clocks had Roman numerals or stylised clock hands.
AI systems also did not perform any better when the second hand was removed, suggesting there are deep-seated issues with hand detection and angle interpretation, the team says.
The researchers asked AI models to answer a range of calendar-based questions, such as identifying holidays and working out past and future dates.
The team found that even the best-performing AI model got date calculations wrong one-fifth of the time.
'Significant gap in ability'
Rohit Saxena, of the University of Edinburgh's School of Informatics, who led the study, said there was a 'significant gap in the ability of AI to carry out what are quite basic skills for people'.
'Most people can tell the time and use calendars from an early age,' she said.
'These shortfalls must be addressed if AI systems are to be successfully integrated into time-sensitive, real-world applications, such as scheduling, automation and assistive technologies.'
Aryo Gema, also of the School of Informatics, said AI 'emphasises complex reasoning tasks, but ironically, many systems still struggle when it comes to simpler, everyday tasks'.
'Our findings suggest it's high time we addressed these fundamental gaps. Otherwise, integrating AI into real-world, time-sensitive applications might remain stuck at the eleventh hour.'
The findings are reported in a peer-reviewed paper that will be presented at the Reasoning and Planning for Large Language Models workshop in Singapore on April 28.
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