Imagine asking a computer complex questions about data in a table, like "How did team A's performance compare to team B's after event X?" Traditional AI struggles with this type of reasoning, often stumbling over calculations or misinterpreting the nuances of language. Now, researchers have developed a clever method called H-STAR, a hybrid approach that combines the strengths of two different AI reasoning styles. One style, symbolic reasoning, excels at precise calculations and logic, much like a spreadsheet. The other, semantic reasoning, understands the meaning and context of words, similar to how humans read. H-STAR uses a two-step process. First, it efficiently extracts the most relevant parts of the table based on the question. Think of it as highlighting the important rows and columns. This clever pre-processing step helps the AI focus on the right information, reducing errors. Then, H-STAR adaptively chooses the best reasoning strategy, using symbolic reasoning for mathematical questions and semantic reasoning for those requiring language understanding. This flexible approach makes it much more accurate than previous methods, especially with large, complex tables. Researchers tested H-STAR on various benchmarks involving fact verification and question answering, using different state-of-the-art AI models, including Gemini, GPT, and Llama. H-STAR consistently outperformed other AI methods, demonstrating its robustness and efficiency. While the current research focuses on English-language Wikipedia tables, the team sees exciting possibilities for expanding H-STAR to other languages and data sources, like hierarchical tables and relational databases. This work represents an important leap toward building AI that can truly understand and reason about the world's data, opening doors for advancements in data analysis, research, and decision-making.