Hey Alexa! Sorry I fooled you …

A human can likely tell the difference between a turtle and a rifle. Two years ago, Google’s AI wasn’t so sure. For quite some time, a subset of computer science research has been dedicated to better understanding how machine-learning models handle these “adversarial” attacks, which are inputs deliberately created to trick or fool machine-learning algorithms.  While much of this work has focused on speech and images, recently, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) tested the boundaries of text. They came up with “TextFooler,” a general framework that can successfully attack natural language processing (NLP) systems — the types of systems that let us interact with our Siri and Alexa voice assistants — and “fool” them into making the wrong predictions.  One could imagine using TextFooler for many applications related to internet safety, such as email spam filtering, hate speech flagging, or “sensitive” political speech text Continue reading Hey Alexa! Sorry I fooled you …