Google’s star ethics researcher highlighted the risks of large language models, which are key to the bigtech’s business model, writes MIT Technology Review. Timnit Gebru, the co-lead of Google’s ethical AI team, has been making headlines after she was ousted in early December. A series of tweets, leaked emails, and media articles showed that Gebru’s exit was the culmination of a conflict over another paper she co-authored.
MIT Technology Review obtained a copy of the research paper from one of the co-authors, Emily Bender, a professor of computational linguistics at the University of Washington. Though Bender asked MIT Technology Review not to publish the paper itself because the authors didn’t want such an early draft circulating online, it gives some insight into the questions Gebru and her colleagues were raising about AI that might be causing Google concern.
Titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” the paper lays out the risks of large language models — AIs trained on staggering amounts of text data. These have grown increasingly popular — and increasingly large — in the last three years. They are now extraordinarily good, under the right conditions, at producing what looks like convincing, meaningful new text and sometimes at estimating meaning from language. But, says the introduction to the paper, “we ask whether enough thought has been put into the potential risks associated with developing them and strategies to mitigate these risks.”
The paper, which builds off the work of other researchers, presents the history of natural language processing, an overview of four main risks of large language models, and suggestions for further research. Since the conflict with Google seems to be over the risks, MIT Technology Review focused on summarizing those: environmental and financial costs; massive data, inscrutable models; research opportunity costs; and illusions of meaning.
The paper’s goal, Bender says, was to take stock of the landscape of current research in natural language processing. “We are working at a scale where the people building the things can’t actually get their arms around the data,” she said to MIT Technology Review. “And because the upsides are so obvious, it’s particularly important to step back and ask ourselves, what are the possible downsides? … How do we get the benefits of this while mitigating the risk?”