Hugging Face CEO Clement Delangue, in testimony to the full U.S. House Science Committee this morning for a hearing on Artificial Intelligence: Advancing Innovation Towards the National Interest, said in his opening statement that open science and open-source AI “are critical to incentivize and are extremely aligned with the American values and interests.”
Others testifying at the hearing included Jason Matheny, president and CEO of RAND Corporation; Shahin Farshchi, general partner at Lux Capital; Rumman Chowdhury, responsible AI fellow at Harvard Universityl and Dewey Murdick, executive director for Georgetown University’s Center for Security and Emerging Technology.
Delangue noted that today’s AI progress has been powered by open science and open source, and without open-source PyTorch, Tensorflow, Keras, transformers and diffusers, all invented in the U.S., the U.S. “might not be the leading country for AI.”
Delangue’s testimony comes two weeks after Senators sent a letter questioning Mark Zuckerberg over the leak of Meta’s popular open-source LLM LLaMA, a letter that expressed concerns about the “potential for [the model’s] misuse in spam, fraud, malware, privacy violations, harassment, and other wrongdoing and harms.”
The open dissemination of LLaMA, the letter said, “represents a significant increase in the sophistication of the AI models available to the general public, and raises serious questions about the potential for misuse or abuse.”
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Hugging Face, a fast-growing New York-based startup that enjoyed a $2 billion valuation last year, has become a hub for open-source code and models and emerged as a leading voice in the open-source AI community. In April, Delangue drew more than 5,000 people to a local meetup celebrating open-source technology at the Exploratorium in downtown San Francisco.
In his opening statement, Delangue also said that open science and open source foster tens of thousands of startups building with AI.
“Thanks to ethical openness, [they create] a safer path for development of the technology by giving civil society, nonprofits, academia and policymakers the capabilities they need to counterbalance the power of big private companies,” he said. “Open science and open source prevent black-box systems, make companies more accountable and help [solve] today’s challenges like mitigating biases, reducing misinformation, promoting copyrights and rewarding all stakeholders including artists and content creators in the value creation process.”
He added that Hugging Face’s approach to ethical openness “combines institutional policies, such as documentation with model cards, pioneered by our own Dr. Margaret Mitchell; technical safeguards, such as gating access to artifacts and staged releases; and community incentives like moderation and opt-in and opt-out datasets.”
Hugging Face’s comments are part and parcel of the current red-hot debate around open-source AI, which has been having a “moment” over the past few months — following a wave of recent large language model (LLM) releases and an effort by startups, collectives and academics to push back on the shift in AI to closed, proprietary LLMs.
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Hugging Face CEO Clement Delangue, in testimony to the full U.S. House Science Committee this morning for a hearing on Artificial Intelligence: Advancing Innovation Towards the National Interest, said in his opening statement that open science and open-source AI “are critical to incentivize and are extremely aligned with the American values and interests.”
Others testifying at the hearing included Jason Matheny, president and CEO of RAND Corporation; Shahin Farshchi, general partner at Lux Capital; Rumman Chowdhury, responsible AI fellow at Harvard Universityl and Dewey Murdick, executive director for Georgetown University’s Center for Security and Emerging Technology.
Delangue noted that today’s AI progress has been powered by open science and open source, and without open-source PyTorch, Tensorflow, Keras, transformers and diffusers, all invented in the U.S., the U.S. “might not be the leading country for AI.”
Delangue’s testimony comes two weeks after Senators sent a letter questioning Mark Zuckerberg over the leak of Meta’s popular open-source LLM LLaMA, a letter that expressed concerns about the “potential for [the model’s] misuse in spam, fraud, malware, privacy violations, harassment, and other wrongdoing and harms.”
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The open dissemination of LLaMA, the letter said, “represents a significant increase in the sophistication of the AI models available to the general public, and raises serious questions about the potential for misuse or abuse.”
>>Follow VentureBeat’s ongoing generative AI coverage<<
Hugging Face, a fast-growing New York-based startup that enjoyed a $2 billion valuation last year, has become a hub for open-source code and models and emerged as a leading voice in the open-source AI community. In April, Delangue drew more than 5,000 people to a local meetup celebrating open-source technology at the Exploratorium in downtown San Francisco.
In his opening statement, Delangue also said that open science and open source foster tens of thousands of startups building with AI.
“Thanks to ethical openness, [they create] a safer path for development of the technology by giving civil society, nonprofits, academia and policymakers the capabilities they need to counterbalance the power of big private companies,” he said. “Open science and open source prevent black-box systems, make companies more accountable and help [solve] today’s challenges like mitigating biases, reducing misinformation, promoting copyrights and rewarding all stakeholders including artists and content creators in the value creation process.”
He added that Hugging Face’s approach to ethical openness “combines institutional policies, such as documentation with model cards, pioneered by our own Dr. Margaret Mitchell; technical safeguards, such as gating access to artifacts and staged releases; and community incentives like moderation and opt-in and opt-out datasets.”
Hugging Face’s comments are part and parcel of the current red-hot debate around open-source AI, which has been having a “moment” over the past few months — following a wave of recent large language model (LLM) releases and an effort by startups, collectives and academics to push back on the shift in AI to closed, proprietary LLMs.
>>Don’t miss our special issue: Building the foundation for customer data quality.<<
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Author: Sharon Goldman
Source: Venturebeat
