Machine learning is a type of artificial intelligence (AI) that enables computers to learn automatically without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves. The learning process begins with making observations in our data, identifying patterns and making better decisions in the future based on the examples that we provide. In its more advanced form, machine learning could enable computers to look for new physics without being explicitly told what to look for.
The past several years have seen a revolution in machine learning. New hardware, especially Graphics Processing Units (GPUs), and the algorithms associated with “deep learning” have enabled computers to surpass humans in certain pattern recognition exercises for the first time. Deep learning now dominates research in artificial intelligence and has found wide application across many problem domains. These techniques have the potential to greatly amplify our ability to do science and, indeed, have already begun to impact experiments at Fermilab. In addition, these algorithms are broadly applicable to various nonlinear optimization problems, including (for an institution like Fermilab) accelerator operations.
In the Scientific Computing Division, we work to empower the research program to further the laboratory’s mission by providing centralized access to expertise and resources. Our goal is to enable scientists at the lab to deploy machine learning solutions through consulting and training. We are a resource for groups just getting started in machine learning and also for experts who understand the techniques and just need help understanding the infrastructure available to them. We also build bridges to experts outside of the lab through seminar series and by representing the Fermilab community at machine learning conferences and workshops.