Machine Learning Experts - Sasha Luccioni

Summary

Research scientist Sasha Luccioni discusses her work on evaluating the environmental, societal, and ethical impacts of machine learning models. Her research includes measuring and reducing artificial intelligence emissions through initiatives such as CodeCarbon, regional grid selection, and the BigScience carbon footprint working group. BigScience aims to make large language models transparent by curating multilingual datasets, assessing manufacturing and compute costs, and analyzing participant workloads. Luccioni also emphasizes expanding model evaluation criteria beyond standard accuracy metrics to encompass efficiency, emissions, and subset-level fairness across demographics and data features. Overall, these efforts aim to democratize artificial intelligence and encourage deliberate trade-offs between model performance and computational resource demands.

Context

Large language models often operate as black boxes with high environmental impacts, uncurated training data, and narrow evaluation benchmarks that prioritize accuracy while overlooking efficiency and fairness.

Approach / What changed

Initiatives such as the BigScience project, the CodeCarbon real-time emissions tracking package, and proactive compute scheduling leverage cleaner regional energy grids and multidimensional model evaluations to address environmental and ethical concerns.

Takeaways

• Selecting cloud computing regions powered by hydroelectric grids rather than coal-based grids can reduce training emissions approximately one hundredfold.
• The CodeCarbon package runs in parallel with model training workloads to provide real-time carbon emission estimates.
• Trading minor accuracy losses for greater computational efficiency can reduce hardware requirements and democratize artificial intelligence.

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