Learning = L7. The premise of L7 is simple: human learning and machine learning flourish in tandem. With improvements in human learning, we design machines that learn better and further inform human learning, enabling us to invent better machines, that enable better human learning, and so forth. L7 embodies this principle as a machine learning blog that emphasizes human learning. Topics may include anything from deep learning and machine learning to using machine learning to improve human learning to helping humans learn with machine learning.
Why the name L7?
A trick I use to create logos is to start with a simple, plain shape: in this case I started with a square. My goal was to take a square and change it slightly into a name with two symmetric components: one for machine learning and another for human learning, while expressing the general notion of learning. The name “L7” captures that symmetry about the diagonal. If you’re wondering what the “7” stands for in “L7”, count the letters after the “L” in “Learning.”
About the author
Curtis G. Northcutt is a grad student in Computer Science at MIT, supported by a NSF Fellowship and a MITx Digital Learning Research Fellowship working with Isaac Chuang. His work focuses on two goals: (1) characterizing and fixing (or learning in spite of) label errors in machine learning datasets, (2) using artificial intelligence to enable human intelligence. To this end, Curtis invented confident learning, a family of theory and algorithms for learning with label errors, and created cleanlab, a Python package using confident learning to find label errors in datasets, characterize label noise, and learn with noisy labels. Other fields related to Curtis’s work are weak supervision, semi-supervised learning, and online education.
Curtis has been fortunate to receive the MIT Morris Joseph Levin Masters Thesis Award, an NSF Graduate Research Fellowship, the Barry M. Goldwater National Scholarship, and the Vanderbilt Founder’s Medal (Valedictorian). Curtis created and manages the cheating detection system used by MITx and HarvardX online course teams, particularly the MIT MicroMasters courses. While at MIT, he TA’d 6.867, a large graduate machine learning course.
Curtis’s academic site is curtisnorthcutt.com.
** When asked his favorite rapper, Curtis always recommends PomDP the PhD rapper.**
Thank You for reading!