AI News Monitor

At the Future of Intelligent Computing Conference (FICC) in Berlin, Sean Williams, CEO of AutogenAI, presented a paper highlighting a curious flaw in large language models (LLMs): they often fail at simple reasoning tasks that humans find trivial.

Williams, whose background in philosophy of language and mathematical logic informs his work, described how even the most advanced AI models struggle with basic contextual reasoning. His research, titled “Easy Problems That Large Language Models Get Wrong,” poses 50 straightforward questions—such as determining the fastest horse in a race or measuring exact quantities—that trip up AI systems.

“These models score well on benchmarks, but that doesn’t mean they’re truly intelligent,” Williams said in an interview. “Our test shows that while humans get about 85-90% of these questions right, the best LLMs currently score only 60-65%.”

The paper has already spurred an “arms race,” Williams noted, as AI developers optimize their models to pass his benchmark. To let users test themselves against AI, his team built llm-quiz.com, where visitors can compare their reasoning skills to leading models from OpenAI and Google.

Despite the gaps, Williams acknowledged surprising progress in AI’s ability to parse nuanced language—including understanding a logic professor’s joke about double negatives, which stumped earlier models.

Looking ahead, Williams’ work focuses on improving AI’s contextual reasoning for commercial applications, such as AutogenAI’s bid-writing software. “The challenge,” he said, “is giving models the same depth of context a seasoned professional would have.”

When asked about AI consciousness, Williams was blunt: “No.” But he pointed to Wittgenstein’s Philosophical Investigations as essential reading for grappling with AI’s linguistic limits.