In episode 120 of The Gradient Podcast, Daniel Bashir speaks to Sasha Luccioni.
Sasha is the AI and Climate Lead at HuggingFace, where she spearheads research, consulting, and capacity-building to elevate the sustainability of AI systems. A founding member of Climate Change AI (CCAI) and a board member of Women in Machine Learning (WiML), Sasha is passionate about catalyzing impactful change, organizing events and serving as a mentor to under-represented minorities within the AI community.
Have suggestions for future podcast guests (or other feedback)? Let us know here or reach Daniel at editor@thegradient.pub
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Outline:
(00:00) Intro
(00:43) Sasha’s background
(01:52) How Sasha became interested in sociotechnical work
(03:08) Larger models and theory of change for AI/climate work
(07:18) Quantifying emissions for ML systems
(09:40) Aggregate inference vs training costs
(10:22) Hardware and data center locations
(15:10) More efficient hardware vs. bigger models — Jevons paradox
(17:55) Uninformative experiments, takeaways for individual scientists, knowledge sharing, failure reports
(27:10) Power Hungry Processing: systematic comparisons of ongoing inference costs
(28:22) General vs. task-specific models
(31:20) Architectures and efficiency
(33:45) Sequence-to-sequence architectures vs. decoder-only
(36:35) Hardware efficiency/utilization
(37:52) Estimating the carbon footprint of Bloom and lifecycle assessment
(40:50) Stable Bias
(46:45) Understanding model biases and representations
(52:07) Future work
(53:45) Metaethical perspectives on benchmarking for AI ethics
(54:30) “Moral benchmarks”
(56:50) Reflecting on “ethicality” of systems
(59:00) Transparency and ethics
(1:00:05) Advice for picking research directions
(1:02:58) Outro
Links:
Sasha’s homepage and Twitter
Papers read/discussed
Climate Change / Carbon Emissions of AI Models
Quantifying the Carbon Emissions of Machine Learning
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Tackling Climate Change with Machine Learning
Responsible AI
Stable Bias: Analyzing Societal Representations in Diffusion Models
Metaethical Perspectives on ‘Benchmarking’ AI Ethics
Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice
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