If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Zhang, Jiliang, Jiang, Xiaoxiong
With the advent of the era of artificial intelligence(AI), deep neural networks (DNNs) have shown huge superiority over human in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs) which are designed by attackers to fool deep learning models. Different from real examples, AEs can hardly be distinguished from human eyes, but mislead the model to predict incorrect outputs and therefore threaten security critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of AI security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristic and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that we review the existing defenses and discuss their limitations. Finally, the future research opportunities and challenges of AEs are prospected.