The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning--essentially learning in complex systems comprised of multiple processing stages--is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. Deep learning systems are now deployed ubiquitously and used by billions of people every day for instance through cell phones and web search engines.

We will first provide a brief historical overview of artificial neural networks and deep learning, starting from their early origins in the 1940s and their connections to biological neural networks and learning, and ending with examples of some of the most recent successes in engineering applications. While we do not yet have a comprehensive theory of deep learning, we will also provide a brief overview of a growing body of theoretical results about deep learning highlighting some of the remaining gaps and open questions in the field. We will then present various applications of deep learning to problems in the natural sciences, such as the detection of exotic particles in high-energy physics, the prediction of molecular properties and reactions in chemistry, and the prediction of protein structures in biology.

Argonne Physics Division Colloquium Schedule