Large language models excel in generating realistic text, which emphasizes the need for systems that detect whether a text is generated or written by a human. Detecting generated text is crucial in many applications such as identifying fake news, filtering product reviews, and assessing student assignments. In this talk, I will discuss typical differences between generated and human text, and overview the prominent state-of-the-art text detection approaches, including watermarking, supervised and zero-shot methods. We will also touch on limitations of the existing detectors and discuss emerging approaches to evade them.