The Gradient: Perspectives on AI

Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts

Apr 18, 2024

Sasha Luccioni, AI and Climate Lead, discusses the environmental impact of AI systems, quantifying emissions, efficient hardware, power-hungry processing, and biases in AI models. The podcast explores the challenges of mitigating carbon footprint in AI experimentation, hardware utilization, energy consumption, and lifecycle assessment of AI models, as well as the difficulties in measuring AI ethics and societal impacts.

01:03:07

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Sasha Luccioni

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Quick takeaways

Larger AI models contribute to increased carbon emissions, necessitating a focus on environmental impacts and measurements.

Transitioning to AI ethics from finance emphasizes the importance of understanding societal implications and real-world applications of technical solutions.

Efforts to quantify energy costs of AI models highlight challenges in measuring embodied emissions, transparency in environmental impact disclosure, and considerations for reducing carbon emissions.

Deep dives

Impact of ML Systems on Climate and Carbon Emissions

ML systems have grown in size, leading to increased carbon emissions. The discussion with Sasho Luccioni from Hugging Face focuses on understanding how models of varying sizes affect the environment and how their impacts are measured. Attention is drawn to the link between ML capabilities, model sizes, and their climate effects. Additionally, the conversation delves into meta-ethical perspectives on AI ethics beyond climate considerations.

The Jevons Paradox and AI

01:39

Exploring Bias in AI Models through Technical Analysis

02:38

Exploring Values, Biases, and Ethics in AI Research

02:20

Introduction

2min

Navigating the Intersection of AI, Climate Change, and Environmental Impact

13min

Exploring the Impacts of GPU Performance and Efficient Hardware in AI

3min

Mitigating Carbon Footprint in AI Experimentation

19min

Exploring Hardware Utilization, Energy Consumption, and Lifecycle Assessment of AI Models

4min

Exploring Biases in AI Models and Image Text Outputs

13min

Challenges in Quantifying AI Ethics

6min

Navigating Research Directions and Societal Impacts in AI

3min

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

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

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

* CodeCarbon

* Responsible AI

* Stable Bias: Analyzing Societal Representations in Diffusion Models

* Metaethical Perspectives on ‘Benchmarking’ AI Ethics

* Measuring Data

* Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice

Get full access to The Gradient at thegradientpub.substack.com/subscribe

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The Gradient: Perspectives on AI

Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Impact of ML Systems on Climate and Carbon Emissions

Personal Motivations Driving Research in AI and Climate Change

Quantifying Emissions, Focus on Inference Costs, and Need for Transparency

Impact of Lifecycle Assessment on AI Models

Biases in AI Models and Ethical Considerations

The Jevons Paradox and AI

Exploring Bias in AI Models through Technical Analysis

Exploring Values, Biases, and Ethics in AI Research

Get the Snipd
podcast app

AI-powered
podcast player

Discover
highlights

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The Gradient: Perspectives on AI

Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Impact of ML Systems on Climate and Carbon Emissions

Personal Motivations Driving Research in AI and Climate Change

Quantifying Emissions, Focus on Inference Costs, and Need for Transparency

Impact of Lifecycle Assessment on AI Models

Biases in AI Models and Ethical Considerations

The Jevons Paradox and AI

Exploring Bias in AI Models through Technical Analysis

Exploring Values, Biases, and Ethics in AI Research

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