Machine Learning Street Talk (MLST) cover image

Machine Learning Street Talk (MLST)

#92 - SARA HOOKER - Fairness, Interpretability, Language Models

Dec 23, 2022
Sara Hooker, founder of Cohere For AI and a leader in machine learning research, discusses pivotal topics in the field. She explores the 'hardware lottery' concept, emphasizing how hardware compatibility affects ideas' success. The conversation delves into fairness, highlighting challenges like annotator bias and the need for fairness objectives in model training. Hooker also tackles model efficiency versus size, self-supervised learning's capabilities, and the nuances of prompting in language models, offering insights into making machine learning more accessible and trustworthy.
51:31

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