Imagine an AI that doesn't just answer questions but asks them, delving into images, text, and tables to generate insightful queries. That's the potential of multimodal question generation, a field explored by researchers in the paper "Synthetic Multimodal Question Generation" (SMMQG). This study introduces a framework for creating synthetic datasets to test and refine this emerging capability. Currently, evaluating AI's ability to understand information across multiple modalities is tricky due to a shortage of diverse datasets. SMMQG aims to solve this by building synthetic data that mimics real-world scenarios, allowing researchers to test how well AI models reason and extract knowledge from a mix of sources like images, text passages, and tables. The researchers' system works by first sampling a "seed source" from various inputs. It then identifies a key entity within this source, using it as a starting point. Next, the system retrieves related information from other sources, ensuring thematic unity for the generated questions. Finally, an AI model, such as GPT-4-Turbo, crafts questions in a specific style (e.g., comparison, mathematical) based on the collected information. Another step validates the generated questions and answers for accuracy and relevance. The quality of this synthetic data is assessed by comparing it to existing benchmarks and evaluating how well various AI models perform on it. Initial results are promising. Not only can SMMQG produce diverse questions tailored to specific styles and modalities, but the quality of this synthetic data appears comparable to, and in some aspects better than, manually created datasets. Notably, AI models perform differently depending on the question's style and the types of sources used, demonstrating the need for tailored evaluation. However, there are limitations. The system currently relies on powerful language models like GPT-4-Turbo, and it's unclear how well the framework will work with less sophisticated models or very different datasets. There's also the challenge of ensuring the AI-generated questions and answers remain unbiased and avoid harmful content. While more work is needed, SMMQG offers a promising path towards creating and evaluating truly insightful, multimodal AI systems.