Part of the Special ECE Seminar Series
Modern Artificial Intelligence
The AI Trinity: Data + Algorithms + Infrastructure
AI at scale requires a perfect storm of data, algorithms and cloud infrastructure. Modern deep learning has relied on large labeled datasets for training. However, such datasets are not easily available in all domains, and are expensive/difficult to collect. By building intelligence into data collection and aggregation, we can drastically reduce data requirements. Additionally, algorithmic and infrastructure innovations now make it possible to train models at scale. In the future, we will see more integration of these three pillars to advance AI.
Anima Anandkumar is a Bren professor at Computing + Mathematical Sciences department at Caltech. Anima Anandkumar's research interests span theory and practice of large-scale machine learning. In particular, she has been spearheading the development and analysis of tensor algorithms for machine learning. She is the recipient of several awards such as the Bren endowed chair professorship at Caltech, Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, and several best paper awards. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her Ph.D. from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U.C. Irvine between 2010 and 2016, a visiting researcher at Microsoft Research New England in 2012 and 2014, and a Principal Scientist at Amazon Web Services between 2016-2018.