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?

Well, you could do inference with a small amount of data.  We just have concepts like statistical power to tell us when our results would be reliable and when they would not be.
In general, lots of data is needed in machine learning to overcome the variance in estimators/models.  Trees, as an example, are incredibly high variance estimators.  The only real way to combat that is to add more data since the variance shrinks proportional to $1/n$.
