The increased availability of large data sets and advancements in AI algorithms have revolutionized the role of data in both commercial industries and academic research. Today, AI permeates multiple industries, from self-driving vehicles and entertainment choices to cancer-detection and criminal justice. Moreover, in the last few years, it has had substantial impacts in molecular chemistry, particle physics, and more recently astronomy. AI, and it’s sub-fields, like machine learning, are more than likely here to stay. But, what are these algorithms really doing, and are they ethically implemented?

We'll discuss these topics, as well as the theory of deep learning, and its application to modern astronomical surveys, which are providing data sets that are unprecedented in size, precision, and complexity. Recent work with convolutional neural networks and strong gravitational lensing intimate the long-term potential for deep learning and its application to larger challenges in cosmology. However, AI is not without its own shortcomings. We'll discuss the barriers to deep learning having its highest impact on science.