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Earlier today, community-powered machine learning (ML) platform Hugging Face announced $100 million in new funding, raised in just one week, to continue building what many, including CEO Clement Delangue, , refer to it as the “GitHub of machine learning”.
“I think that’s an exact analogy,” he told VentureBeat. “With every new technology, there is a new category-defining platform that builds it. GitHub was for software and looks set to become the machine learning platform.”
Founded in 2016, Hugging Face has evolved from a developer of natural language processing (NPL) technologies into an open source library and community platform where popular NLP models such as BERT, GPT-2, T5, and DistilBERT are available are. Now it has moved beyond NLP and has evolved into a hub and community for ML models – Hugging Face works closely with companies that might be seen as competitors, as companies like Meta’s AI division, Amazon Web Services, Microsoft and Google AI use the platform.
“We’ve seen the emergence of a new generation of machine learning architectures called Transformers, based on transfer learning,” Delangue said. “Most users of this new generation of models use them through our platform – it all started with text, but now it’s starting to find its way into all areas of machine learning, which is a new development for machine learning tools.”
A focus on ethical AI
Hugging Face has made some notable additions to the ethical AI space recently, which Delangue says is a key priority. Margaret Mitchell, previously head of Google’s AI ethical research group, came on board in August 2021. and Giada Pistilli, who has a Ph.D. in philosophy and specializing in conversational AI ethics, just started Hugging Face today.
“It’s good timing — someone with a Ph.D. in philosophy is a pretty unusual take for a tech company, but I think it’s a testament to our commitment to making the field of machine learning more value-based, which Margaret Mitchell is fond of saying,” said Delangue.
Delangue added that Hugging Face has a “strong view” of the future of AI and ML. “Just as science has always made the field open and collaborative, we believe there is a huge risk of concentrating the power of machine learning very heavily in the hands of a few players, especially when those players don’t have a track record of doing it.” Right for the community,” he said. “By building more openly and collaboratively within the ecosystem, we can make machine learning a positive technology for everyone and work on some of the near-term challenges that we see.”
An “open and collaborative” ML evolution
Delangue said Hugging Face plans to continue expanding its team from diverse backgrounds for all positions and skills, from science and engineering to product and business side. “This is a big development for us,” he said. “We also hope that the number of models and datasets on the platform will grow.”
The company is also excited about Big Science, a year-long research project on large multilingual models and datasets. “It’s the largest machine learning collaboration we’ve had with over a thousand scientists and 200 organizations, inspired by other large science collaborations like in physics,” said Delangue. “We wanted to create something like this for machine learning.”
But it’s Hugging Face’s emphasis on an open, collaborative approach that Delangue says has kept investors confident in its $2 billion valuation. “This is really important to us, makes us successful and differentiates us from others in the industry.”
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