Artificial intelligence has a decades-long history that exhibits alternating enthusiasm and disillusionment for the field's scientific insights, technical accomplishments, and socioeconomic impact. Recent achievements have seen renewed claims for the transformative and disruptive effects of AI. Reviewing the history and current state of the art reveals a broad repertoire of methods and techniques developed by AI researchers. In particular, modern machine learning methods have enabled a series of AI systems to achieve superhuman performance. The exponential increases in computing power, open-source software, available data, and embedded services have been crucial to this success. At the same time, there is growing unease around whether the behavior of these systems can be rendered transparent, explainable, unbiased, and accountable. One consequence of recent AI accomplishments is a renaissance of interest around the ethics of such systems. More generally, our AI systems remain singular task-achieving architectures, often termed narrow AI. I will argue that artificial general intelligence-able to range across widely differing tasks and contexts-is unlikely to be developed, or emerge, any time soon.