Seeking Deep Understanding in Machine Learning with Una-May O'Reilly

In this episode

PRODUCED BY: Nate Caldwell

Una-May discusses the difference between deep learning and deep understanding and some of the challenges humans face when writing as well reading code. Learning code is like learning a new language which is not necessarily intuitive, it is hard to read and error prone. O’Reilly’s group AnyScale Learning for All (ALFA) develops new data-driven analyses of online coding courses, deep learning techniques for program representations, and adversarial attacks on machine learning models.

The podcast transcript can be found here.

About the speakers

Principal Research Scientist, MIT CSAIL

Una-May O’Reilly holds a BSc from the University of Calgary, and a MCS and PhD (1995) from Carleton University (Ottawa, Canada). O’Reilly joined MIT Computer Science Artificial Intelligence Laboratory (CSAIL) as a Post-Doctoral Associate in 1996. Now, she is the leader of the AnyScale Learning For All (ALFA) group, editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines, editor for Evolutionary Computation, and action editor for the Journal of Machine Learning Research at MIT. Some of her achievements include: the EvoStar Award for Outstanding Contribution of Evolutionary Computation (2013), Fellow of the International Society of Genetic and Evolutionary Computation, and ACM sig-EVO.

Industry Impact
O’Reilly focuses her research on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. The vision is to develop data-driven machine learning systems that advance the quality of healthcare, the understanding of cyber arm races, and the delivery of online education.

As the healthcare industry is transforming by the ability to record massive amounts of information about individual patients, the enormous volume of data being collected is almost impossible to analyze. Machine learning provides ways to instantly find patterns and reason about data, which enables healthcare professionals to move to personalized care. For example, healthcare providers can take advantage in being able to foresee hospital re-admission for chronically ill patients. Being able to recognize those patients that have the probability of being re-admitted can have superior support after being discharged.