AI still can't tell the time, and it's a bigger problem than it sounds

Despite its growing prowess in language, images and coding, AI timekeeping ability is stumped by the humble clock and calendar.

AI models struggle with tasks most humans master as children: reading analogue clocks and determining calendar dates. New research tested leading AI models and found they failed over 60% of the time. The findings raise questions about AI's readiness for real-world, time-sensitive applications.

AI can pass law exams but flubs a clock face

It’s hard not to marvel at the sophistication of large language models. They write passable essays, chat fluently in multiple languages, and generate everything from legal advice to song lyrics. But put one in front of a basic clock or ask it what day a date falls on, and it might as well be guessing.

At the recent International Conference on Learning Representations (ICLR), researchers unveiled a startling finding: even top-tier AI models such as GPT-4o, Claude-3.5 Sonnet, Gemini 2.0 and LLaMA 3.2 Vision struggle mightily with time-related tasks. In a study led by Rohit Saxena from the University of Edinburgh, these systems were tested on their ability to interpret images of clocks and respond to calendar queries. They failed more than half the time.

Reading the time: a surprisingly complex puzzle

To a human, clock reading feels instinctive. To a machine, it's a visual nightmare. Consider the elements involved:

Overlapping hands that require angle estimation, Diverse designs using Roman numerals or decorative dials, * Variability in colour, style, and size

While older AI systems relied on labelled datasets, clock reading demands spatial reasoning. As Saxena noted:

In testing, even the most advanced models correctly read the time from a clock image just 38.7% of the time. That’s worse than random chance on many tasks.

Calendar chaos: dates and days don’t add up

When asked, "What day is the 153rd day of the year?", humans reach for logic or a calendar. AI, by contrast, attempts to spot a pattern. This doesn’t always go well.

The study showed that calendar queries stumped the models even more than clocks, with just 26.3% accuracy. And it's not just a lack of memory — it's a fundamentally different approach. LLMs don’t execute algorithms like traditional computers; they predict outputs based on training patterns.

So while an AI might ace the question "Is 2028 a leap year?", it could completely fail at mapping that fact onto a real-world date. Training data often omits edge cases like leap years or obscure date calculations.

What it means for Asia's AI future

From India’s booming tech sector to Japan’s robotics leaders, AI applications are proliferating across Asia. Scheduling tools, autonomous systems, and assistive tech rely on accurate timekeeping — a weakness this research throws into sharp relief. For more insights into regional trends, explore our article on APAC AI in 2026: 4 Trends You Need To Know.

For companies deploying AI into customer service, logistics, or smart city infrastructure, such flaws aren’t trivial. If an AI can't reliably say what time it is, it’s hardly ready to manage hospital shift schedules or transport timetables. This issue is particularly relevant when considering the future of AI & Call Centres: Is The End Nigh? or the challenges faced by Southeast Asia: AI's Trust Deficit?.

These findings argue for hybrid models and tighter oversight. AI isn't useless here — but it may need more handholding than previously thought.

When logic and vision collide

This study underscores a deeper truth: AI isn’t just a faster brain. It's something else entirely. What humans do intuitively often mixes perception with logic. AI, however, processes one layer at a time.

Tasks like reading clocks or calculating dates demand a blend of visual interpretation, spatial understanding, and logical sequence — all areas where LLMs still struggle when combined.

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