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11 articles
This article explains why current AI systems lack temporal awareness, how this affects their performance in autonomous tasks, and why temporal reasoning is crucial for reliable AI deployment.
This article explores the distinction between computational proficiency and creative thinking in large language models, particularly in the context of mathematical discovery. It explains why LLMs are strong calculators but lack the intuitive insight required for genuine mathematical breakthroughs.
A new study challenges claims by Anthropic and OpenAI that AI can independently conduct research, finding that while AI can execute tasks, it lacks critical judgment and creativity.
This explainer examines Ford's experience with AI-Augmented Engineering, exploring how AI systems struggle to replicate human expertise in complex engineering contexts and why human oversight remains essential.
Even the best AI models struggle with real knowledge work, successfully completing just 3 percent of complex tasks in a new benchmark.
Learn what AI hallucination means, how it happens, and why it matters for users and professionals. This beginner-friendly explainer covers the key concept behind recent AI reliability concerns.
A new AI benchmark reveals that models confidently solve math problems that have no solution, exposing a key gap in their reasoning capabilities.
A 13-billion-parameter language model trained only on texts before 1931 imagines 2026 as a world of steamships and penny novels, highlighting the risks of AI systems trained on outdated data.
Large Language Models excel at coding and math but often fail on casual questions, revealing a fundamental limitation in current AI systems.
Millions are using AI chatbots for financial advice, but experts warn of significant limitations and risks.
Learn to build a video reasoning pipeline using Python and OpenCV that demonstrates the limitations of current video AI models in complex reasoning tasks.
