How much can we learn from a time series when models are unavailable and data is limited? Surprisingly, quite a lot. In the first half of this talk, Yuanzhao Zhang will introduce a model-free approach to infer causal hypergraphs directly from time series data. This method can provide new insights into the importance of nonpairwise interactions in the brain, as demonstrated through its application to neurophysiological data. In the second half of the talk, he will discuss the ability of time-series foundation models to zero-shot forecast dynamical systems. They find that LLMs utilize context parroting as a key mechanism to forecast chaotic systems. Exploring zero-shot forecasting strategies beyond parroting can provide insights into how artificial and natural intelligence learn from limited data.