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?

An L or a 7 alone are fragile shapes, but combined they form a sturdy square. This is an allegory for human learning and machine learning. L7 captures that symmetry about the diagonal. The 7 in L7 counts the letters after the L in Learning.

About the author, Curtis G. Northcutt

I am a sixth-year PhD candidate in EECS at MIT advised by Isaac Chuang, and supported by an NSF Fellowship and a MITx Digital Learning Research Fellowship. For details, my personal website is curtisnorthcutt.com.

My research focuses on two goals: (1) dataset uncertainty estimation, (2) the synergy of artificial intelligence to augment human intelligence. To this end, I established confident learning, a family of theory and algorithms for characterizing, finding, and learning with label errors in datasets, and cleanlab, the official Python framework for machine learning and deep learning with noisy labels in datasets. For an overview of my published research, please visit Google Scholar.

In addition to my MIT research, I am Chief AI Scientist at Knowledge AI, the principal author of the L7 machine learning blog, PomDP the PhD rapper, and a contingent research scientist at Oculus Research.

In my spare time, I help researchers build affordable state-of-the-art deep learning machines and enjoy competitive mountaineering, hiking, and cycling. My favorite rapper is PomDP the PhD rapper.

Some awards I’ve received include the MIT Morris Joseph Levin Masters Thesis Award, the NSF GRFP Fellowship, the Barry M. Goldwater National Scholarship, and the Vanderbilt Founder’s Medal (Valedictorian). I created the cheating detection system used by MITx and HarvardX online course teams, particularly in MicroMasters courses. At MIT, I TA’d 6.867, a 400 person advanced graduate ML course.

Research Manifesto

  • Use machine learning to augment human learning, especially for social good.
  • Understand and learn in spite of uncertainty and noise in labeled datasets.

While these two ideas appear disparate, they are mutually dependent. Humans often have false notions about the world and encounter misinformation, yet we still learn well in noisy environments. Augmenting human learning with machine learning necessitates a deeper understanding of learning in noisy environments. Across healthcare, agriculture, politics, economics, transportation… our future as a species relies on an increasing synergy between machine learning and human learning: it is paramount that we have the tools to deal with real-world uncertainty, while maintaining the foresight to focus our advances in machine intelligence towards social good.

Industry and Institutional Research

I am fortunate to have had the opportunity to work or intern at:

  • Google AI Research in New York, NY (2019)
  • Knowledge AI Startup in Boston, MA (2019-)
  • Oculus Research / FRL in Redmond, WA (2018-2019)
  • Amazon AI Research in Cambridge, MA (2017)
  • Facebook AI Research in New York, NY (2016)
  • Microsoft Research India in Bangalore (2014)
  • MIT Lincoln Laboratory in Lexington, MA (2013)
  • Microsoft in Redmond, WA (2012)
  • National Science Foundation NSF REU at Notre Dame (2011)
  • General Electric Engineering in Louisville, KY (2010)
  • NASA Langley Research Center in Hampton, VA (2009)

as well as academic collaborations and visiting research with MIT, Harvard, Vanderbilt, Notre Dame, and the University of Kentucky. Details here.

If you’d like to author a post on the L7 blog, send me a message. Thanks for reading :)