Margaret Mitchell has spent her career founding bootstrap-style AI projects inside large tech companies. She helped create Microsoft Research’s “Cognition” group, which concentrated on AI advancement, before moving to Google and founding its Ethical AI team and cofounding its ML Fairness group.
Now, she’s left Big Tech behind for full-time startup life—leading data governance efforts at Hugging Face, a 60-person AI company founded in 2016 and based in NYC.
It’s a big change for Mitchell, following even bigger ones. Over a three-month span beginning in December 2020, Google fired both Mitchell and her Ethical AI team co-lead, Timnit Gebru, after disagreements over their research paper on the dangers of large language models. (Google disputes this version of events.) Though the powerful language algorithms increasingly underpin popular and useful services like Google Search and AutoComplete, their large-scale pattern recognition—trained on vast swaths of the internet—can replicate harmful human biases and multiply harms.
The most powerful language models are also concentrated in the hands of a few powerful companies. Typically, their training and development is limited to the Big Tech sphere, or to FAMGA-funded research groups, both in academia and outside of it (e.g, OpenAI).
Hugging Face wants to bring these powerful tools to more people. Its mission: Help companies build, train, and deploy AI models—specifically natural language processing (NLP)…