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Internet Packet and Service Orchestration Platform RFI Released

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.