Imagine an AI that could instantly solve simple problems while carefully deliberating on the more complex ones. Researchers are working on just that, and it could change the way we interact with AI. Large language models (LLMs) like ChatGPT are great at generating text, but their reasoning abilities are still developing. Current methods like 'Chain-of-Thought' prompting can help LLMs work through logical problems step by step, but they're not always efficient. Easy problems get overthought, and complex ones might be solved too quickly. This is where DynaThink comes in. This new framework allows LLMs to dynamically switch between fast and slow thinking modes. How does it work? DynaThink assesses the problem's complexity. If it seems straightforward, the LLM quickly generates a few solutions and picks the one it's most confident in. But if the problem is tricky, DynaThink activates a more thorough, slower mode. Here, the LLM explores multiple reasoning pathways and verifies its steps more carefully, much like a human carefully double-checking their work. This 'thinking fast and slow' approach improves both accuracy and efficiency. The researchers tested DynaThink with several LLMs like GPT-3.5-Turbo, GPT-4, and Gemini on a variety of reasoning tasks, and it consistently outperformed the standard methods. DynaThink was faster on simpler tasks and more accurate on harder ones – a win-win. While DynaThink is a significant step forward, it's not without limitations. Currently, it categorizes tasks into just two levels: fast and slow. Future work will explore more nuanced approaches, allowing LLMs to fine-tune their thinking process for even greater efficiency and accuracy. This research is a vital step towards more intelligent, adaptable AI that can better handle the complexities of real-world problems, potentially revolutionizing fields like education, coding, and complex decision-making.