Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data? I am wondering why Machine Learning needs a lot of data compared to statistical inference. In statistics, we can use a small amount of data for a statistical inference but in Machine Learning, everybody says we need a lot of data. Why does Machine Learning needs tons of more data compared to Statistics Inference?

All/other things being equal (when?) machine learning models require similar quantities of data as statistical models. In general statistical models tend to have more assumptions than machine learning models and it is these additional assumptions that give you more power (assuming they are true/valid), which means that smaller samples are needed to obtain the same confidence. You can think of the difference between statistical/machine learning models as a difference between parametric and non-parametric models.
Complex models (which are more prevalent in machine learning) with many parameters do require more data (such as deep NN), but it has to do with the parameters and not the models themselves. If you built a complex statistical model with many interactions and polynomial terms you would similarly need large amounts of data to estimate all the parameters (unless you are Bayesian... then you do not even need data!).
