Latest news with #RohitSaxena
Yahoo
17-05-2025
- Science
- Yahoo
AI models can't tell time or read a calendar, study reveals
When you buy through links on our articles, Future and its syndication partners may earn a commission. New research has revealed another set of tasks most humans can do with ease that artificial intelligence (AI) stumbles over — reading an analogue clock or figuring out the day on which a date will fall. AI may be able to write code, generate lifelike images, create human-sounding text and even pass exams (to varying degrees of success) yet it routinely misinterprets the position of hands on everyday clocks and fails at the basic arithmetic needed for calendar dates. Researchers revealed these unexpected flaws in a presentation at the 2025 International Conference on Learning Representations (ICLR). They also published their findings March 18 on the preprint server arXiv, so they have not yet been peer-reviewed . "Most people can tell the time and use calendars from an early age. Our findings highlight a significant gap in the ability of AI to carry out what are quite basic skills for people," study lead author Rohit Saxena, a researcher at the University of Edinburgh, said in a statement. 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." To investigate AI's timekeeping abilities, the researchers fed a custom dataset of clock and calendar images into various multimodal large language models (MLLMs), which can process visual as well as textual information. The models used in the study include Meta's Llama 3.2-Vision, Anthropic's Claude-3.5 Sonnet, Google's Gemini 2.0 and OpenAI's GPT-4o. And the results were poor, with the models being unable to identify the correct time from an image of a clock or the day of the week for a sample date more than half the time. Related: Current AI models a 'dead end' for human-level intelligence, scientists agree However, the researchers have an explanation for AI's surprisingly poor time-reading abilities. "Early systems were trained based on labelled examples. Clock reading requires something different — spatial reasoning," Saxena said. "The model has to detect overlapping hands, measure angles and navigate diverse designs like Roman numerals or stylized dials. AI recognizing that 'this is a clock' is easier than actually reading it." Dates proved just as difficult. When given a challenge like "What day will the 153rd day of the year be?," the failure rate was similarly high: AI systems read clocks correctly only 38.7% and calendars only 26.3%. This shortcoming is similarly surprising because arithmetic is a fundamental cornerstone of computing, but as Saxena explained, AI uses something different. "Arithmetic is trivial for traditional computers but not for large language models. AI doesn't run math algorithms, it predicts the outputs based on patterns it sees in training data," he said. So while it may answer arithmetic questions correctly some of the time, its reasoning isn't consistent or rule-based, and our work highlights that gap." The project is the latest in a growing body of research that highlights the differences between the ways AI "understands" versus the way humans do. Models derive answers from familiar patterns and excel when there are enough examples in their training data, yet they fail when asked to generalize or use abstract reasoning. "What for us is a very simple task like reading a clock may be very hard for them, and vice versa," Saxena said. RELATED STORIES —Scientists discover major differences in how humans and AI 'think' — and the implications could be significant —If any AI became 'misaligned' then the system would hide it just long enough to cause harm — controlling it is a fallacy —Researchers gave AI an 'inner monologue' and it massively improved its performance The research also reveals the problem AI has when it's trained with limited data — in this case comparatively rare phenomena like leap years or obscure calendar calculations. Even though LLMs have plenty of examples that explain leap years as a concept, that doesn't mean they make the requisite connections required to complete a visual task. The research highlights both the need for more targeted examples in training data and the need to rethink how AI handles the combination of logical and spatial reasoning, especially in tasks it doesn't encounter often. Above all, it reveals one more area where entrusting AI output too much comes at our peril. "AI is powerful, but when tasks mix perception with precise reasoning, we still need rigorous testing, fallback logic, and in many cases, a human in the loop," Saxena said.
Time of India
08-05-2025
- Business
- Time of India
Alcobev startup Feline Spirits raises Rs 5.2 cr in pre-series A funding led by IPV
New Delhi: Feline Spirits , a craft alcoholic beverage startup, has raised Rs 5.2 crore in a pre-series A funding round led by Inflection Point Ventures (IPV), the company said in a press release on Thursday. The company said to utilize the capital to expand the company's product portfolio and facilitate entry into new markets. Also, the company plans to launch ' Nine Lives Premium Whisky ' to tap into the semi-premium segment. The startup has sold over 4.1 lakh bottles, generating more than Rs 25 crore in sales in FY25, it shared. Founded by Prabhat Sharma (CEO) and Rohit Saxena (COO), the company is currently operational in 10 markets. It holds licenses in eight markets, including five government-operated ones. "As we expand our footprint across the country and strengthen our partnerships, we're excited to lead a transformation in the way consumers engage with and enjoy craft alcohol," the company's CEO said.
Telegraph
14-03-2025
- Science
- Telegraph
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.
