AI’s Drawbacks: Environmental Damage, Bad Benchmarks, Outsourcing Thinking — With Emily M. Bender and Alex Hanna
Emily Bender is a computational linguistics professor at the University of Washington. Alex Hanna is the Director of Research at the Distributed AI Research Institute. Bender and Hanna join Big Technology to discuss what their new book, “The AI‑Con," which they describe as the layered ways today’s language‑model boom obscures environmental costs, labor harms, and shaky science. Tune in to hear a lively back‑and‑forth on whether chatbots are useful tools or polished parlor tricks. We also cover benchmark gaming, data‑center water use, doomerism, and more. Hit play for a candid debate that will leave you smarter about where generative AI really stands — and what comes next.
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Emily M. Bender — Language Models and Linguistics
In this episode, Emily and Lukas dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and why it's important to name the languages we study.
Show notes (links to papers and transcript): http://wandb.me/gd-emily-m-bender
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Emily M. Bender is a Professor of Linguistics at and Faculty Director of the Master's Program in Computational Linguistics at University of Washington. Her research areas include multilingual grammar engineering, variation (within and across languages), the relationship between linguistics and computational linguistics, and societal issues in NLP.
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Timestamps:
0:00 Sneak peek, intro
1:03 Stochastic Parrots
9:57 The societal impact of big language models
16:49 How language models can be harmful
26:00 The important difference between linguistic form and meaning
34:40 The octopus thought experiment
42:11 Language acquisition and the future of language models
49:47 Why benchmarks are limited
54:38 Ways of complementing benchmarks
1:01:20 The #BenderRule
1:03:50 Language diversity and linguistics
1:12:49 Outro
Can Language Models Be Too Big? 🦜 with Emily Bender and Margaret Mitchell - #467
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.
Is Linguistics Missing from NLP Research? w/ Emily M. Bender - #376 🦜
Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington.
Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?
