Abstract: Some embodiments determine whether an entity engaging in online conversations comprises a chatbot. A challenge message is constructed according to a current context of an ongoing conversation, by employing a generative language model to determine a plausible conversation sequel and subsequently distorting the respective sequel. The challenge is added as a new message to the respective conversation. The chatbot detector may then determine whether a conversation partner is a chatbot according to a similarity between a response to the challenge received from the respective conversation partner and an artificially generated, plausible response to the undistorted conversation sequel. Distortions applied to construct the challenge are deliberately crafted to send a chatbot on a markedly different trajectory.

Abstract: Some embodiments employ a novel procedure of training an artificial intelligence system (e.g., set of deep neural networks) for anomaly detection in applications such as natural language processing and computer security. Token sequences selected from a training corpus are distorted according to at least one of a plurality of pre-determined sequence transformations, before being fed to a sequence analyzer. In turn, the sequence analyzer is trained to correctly guess which transformation was used to generate the respective input token sequence.