Recap: ML@GT Fall Seminar Series

Throughout the fall semester, the Machine Learning Center at Georgia Tech was fortunate to bring world-class researchers, academics, and industry professionals to Georgia Tech’s campus as a part of our Fall Seminar Series. From using machine learning in retail strategy to few-shot learning to understanding the limitations of artificial intelligence, our guest speakers inspired our students to think differently and keep pushing boundaries.

To see a recording of each talk from this semester, select a talk below.

Deep Learning to Learn 

Pieter Abbeel, UC Berkeley


The Natural Language Decathlon: Multitask Learning as Question Answering

Bryan McCann, Salesforce 


Understanding the limitations of AI: When Algorithms Fail

Timnit Gebru, Stanford University


Reaching Beyond Human Accuracy With AI Datacenters

Gregory Daimos, Baidu’s Silicon Valley AI Lab (SVAIL)


Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead

Hugo Larochelle, Google Brain


Practical Applications of Signal Processing and Machine Learning in a Dynamic Retail Environment

Graham Poliner, Macy’s


The Statistical Foundations of Learning to Control

Benjamin Recht, UC Berkeley


Multimodal, Personable, and Knowledgeable Language Generation

Mohit Bansal, UNC-Chapel Hill


Automated Perception in the Real World: The Problem of Scarce Data

Jan Ernst, Siemens Corporate Research


The Spring Seminar Series begins in January 2019. See dates and speakers here. Additional dates and speakers will be added at a later date.

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