The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Can Language Models Be Too Big? 🦜 with Emily Bender and Margaret Mitchell - #467

Mar 24, 2021

Join linguist Emily M. Bender and AI researcher Margaret Mitchell as they unravel the complex implications of large language models. They discuss the environmental cost of training these models and the biases they perpetuate, highlighting the need for ethical AI practices. The duo emphasizes the importance of addressing language's impact on identity and the risks of misconceptions in AI interactions. With a focus on transparency and the importance of documentation, Bender and Mitchell advocate for a thoughtful approach to building responsible AI systems.

54:02

Creator website

Episode guests

Margaret Mitchell

Emily M. Bender

AI Chapters

Episode notes

Intro

2min

Exploring the Risks of Language Models

12min

Environmental Impact and Bias in Language Models

19min

The Impact of Language on Identity and Othering

2min

Perception and Misunderstanding in AI Interaction

8min

Importance of Documentation in AI Development

8min

Emphasizing Specificity and the Challenges of Bias Mitigation in Language Models

4min

Today we’re joined by Emily M. Bender, Professor at the University of Washington, and AI Researcher, Margaret Mitchell.

Emily and Meg, as well as Timnit Gebru and Angelina McMillan-Major, are co-authors on the paper On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. As most of you undoubtedly know by now, there has been much controversy surrounding, and fallout from, this paper. In this conversation, our main priority was to focus on the message of the paper itself. We spend some time discussing the historical context for the paper, then turn to the goals of the paper, discussing the many reasons why the ever-growing datasets and models are not necessarily the direction we should be going.

We explore the cost of these training datasets, both literal and environmental, as well as the bias implications of these models, and of course the perpetual debate about responsibility when building and deploying ML systems. Finally, we discuss the thin line between AI hype and useful AI systems, and the importance of doing pre-mortems to truly flesh out any issues you could potentially come across prior to building models, and much much more.

The complete show notes for this episode can be found at twimlai.com/go/467.

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