Published Oct 20, 2020

Sara Hooker - The Hardware Lottery, Sparsity and Fairness

Join Sara Hooker of Google Brain as she delves into the transformative concept of the 'hardware lottery,' advocating for more sustainable machine learning practices by addressing hardware dependencies, model efficiency, and fairness in AI development.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Machine Learning Street Talk (MLST)

Sara Hooker - The Hardware Lottery, Sparsity and Fairness

Topics covered

Popular Clips

Unpacking Model Bias

Enhancing Deep Learning

Hardware's Impact

AI Evolution Insights

Optimizing Model Fairness

Uncovering Data Bias

Bias in Compact Models

Future Hardware Directions

Model Interpretability Challenges

Advancing Sparse Matrices

GPU's to TPU's Transition

Bias Amplification Dilemma

Diverse ML Perspectives

Hardware Lottery Insights

Episode Highlights

Diverse ML Perspectives

Hardware Lottery

Model Optimization

Related Episodes

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

Sara Hooker - Why US AI Act Compute Thresholds Are Misguided

The Lottery Ticket Hypothesis with Jonathan Frankle

Dr. Paul Lessard - Categorical/Structured Deep Learning

Kernels!

#032- Simon Kornblith / GoogleAI - SimCLR and Paper Haul!

The Elegant Math Behind Machine Learning - Anil Ananthaswamy

#046 The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)

047 Interpretable Machine Learning - Christoph Molnar

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Bold AI Predictions From Cohere Co-founder

#97 SREEJAN KUMAR - Human Inductive Biases in Machines from Language

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Explainability, Reasoning, Priors and GPT-3

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Sara Hooker - The Hardware Lottery, Sparsity and Fairness

Topics covered

Popular Clips

Episode Highlights

Diverse ML PerspectivesThe episode explores philosophical debates and model complexity in machine learning, with Sara Hooker challenging the sustainability of current practices. She advocates for a shift towards more efficient models and a reevaluation of how examples are treated.

Diverse ML Perspectives

Hardware Lottery

Model OptimizationSara Hooker explores the implications of model pruning, interpretability challenges, and bias amplification in machine learning. She emphasizes the need for innovative approaches to optimize resource use and address fairness concerns.

Model Optimization

Related Episodes