Imagine if AI could analyze data, form hypotheses, and discover new knowledge, all on its own. That's the tantalizing promise of automated data-driven discovery, a field that aims to revolutionize scientific research by using artificial intelligence to find patterns and insights hidden within datasets. A groundbreaking new benchmark called DiscoveryBench is putting today's most powerful Large Language Models (LLMs) to the test, challenging them to perform data-driven discovery tasks across diverse scientific domains. The results reveal just how far we have to go before AI can truly take the reins of scientific discovery. DiscoveryBench consists of hundreds of tasks, derived from real published papers and synthetically generated data, reflecting the messy realities of scientific research. LLMs are tasked with analyzing datasets, interpreting natural language research goals, and formulating hypotheses that explain the relationships between variables within a given context. This involves not just statistical analysis, but also the ability to reason like a scientist – choosing the right analysis methods, cleaning data, and connecting concepts across different domains. The benchmark goes beyond simply analyzing data; it evaluates LLMs' ability to design entire discovery workflows, mimicking the complex process undertaken by human researchers. So, how did the LLMs perform? The results are sobering. Even the most powerful LLMs like GPT-4 achieved scores of only 25%, showing that fully autonomous data-driven discovery remains a formidable challenge. The research revealed that while LLMs can succeed at individual steps like generating code for statistical tests, stitching these steps together into a coherent discovery workflow is where they stumble. This highlights the importance of context; understanding the nuances of the data and the research questions is crucial for successful discovery. LLMs struggled especially in domains requiring specialized knowledge or complex analysis techniques. The benchmark also revealed a significant gap between LLMs' ability to perform simple correlation analyses versus more sophisticated techniques like spatial analysis or ecological modeling. One promising finding emerged: When given feedback, some LLMs demonstrated the ability to reflect and refine their approach, hinting at the potential for iterative learning in future AI discovery agents. The research reinforces the importance of domain expertise; providing LLMs with additional knowledge relevant to specific domains can lead to significant performance gains. This suggests that integrating LLMs with domain-specific knowledge bases could be a key to unlocking their discovery potential. DiscoveryBench provides a crucial testing ground for future AI researchers. It highlights the need for new approaches that move beyond simple code generation and empower LLMs to reason more deeply about scientific problems. The benchmark lays the groundwork for developing AI tools that could augment, not replace, human scientists, assisting with tedious tasks, suggesting new lines of inquiry, and ultimately accelerating the pace of discovery.