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hugging facethe fast-growing New York-based startup that has become a central hub for open-source code and models, cemented its status as a leading voice in the AI community on Friday, attracting more than 5 000 people at a local gathering celebrating open source technology at the Exploratorium in downtown San Francisco.
The gathering was born by chance three weeks ago, when the charismatic co-founder and CEO of Hugging Face, Clement Delanguetweeted that he planned to be in San Francisco and wanted to meet others interested in developing open source AI.
Within days, interest in the informal gathering snowballed. Registrations have multiplied by the thousands. In the last week before the event, Delangue booked the Exploratory museum, one of the few places still available that can accommodate thousands of people.
He turned the informal gathering into a massive showcase and networking opportunity for those fascinated by artificial intelligencefrom real-world researchers and programmers to investors, entrepreneurs and the simply curious.
“We just passed the 1,500 registration mark for the Open-Source AI Meetup! Delangue said in a text blast to the RSVP list just days before the event. “What started with a tweet could lead to the biggest AI encounter in history.”
The event took place in the context of a Growing Debate on Large Language Models (LLMs) and their applications. Critics have expressed concerns about the potential monopolization and commodification of closed LLMs by Open AI and other companies, such as Google and Microsoft.
In contrast, open LLMs are trained on general web data and serve as a substrate for downstream applications to build upon. The open source community views LLMs as a public good or commons resource, rather than a private product or service.
Open-source AI is having a breakout moment
Attendees started pouring into the Exploratorium around 6 p.m. Friday and kept coming for hours. They were a striking mix of ages, races and backgrounds, including retirees, parents, engineers and large groups of 20s dressed in a wide range of clothes – from ball gowns to baggy jeans – a wide mix of high fashion and streetwear. The atmosphere was full of energy and the crowd was buzzing with excitement, similar to a music festival.
In brief remarks, Delangue addressed the attendees and said the turnout was a testament to the growing interest and enthusiasm of the general public around open-source AI development. He said Hugging Face’s mission is to make cutting-edge AI accessible to as wide an audience as possible, and in doing so, increase transparency across the ecosystem.
“We were expecting maybe some 100 people to show up,” Delangue said in a speech to attendees. “We have 5,000 people tonight. It’s incredible. People call it the “Woodstock of AI”.
“I think this event is a celebration of the power of open science and open source,” Delangue said. “I think it’s really important for us to remember in AI that we are where we are through open science and open source.”
“If it wasn’t for the ‘Attention Is All You Need’ paper, for the ‘The Birth’ paper and for the ‘Latent Diffusion’ paper, we could be 20, 30, 40 or 50 years from where we we are today in terms of capabilities and possibilities for AI,” he said. “If there hadn’t been open source libraries or languages, if there weren’t Had frameworks like PyTorch, TensorFlow, Keras, Hugging Face, transformers and broadcasters, we wouldn’t be where we are today.”
“Open science and open source [are ways] build a more inclusive future, with less concentration of power in the hands of a few, more contribution from underrepresented populations to fight prejudice, and overall a much safer future with the involvement of civil society, non-profit organizations, regulators to bring all the positive impact we can have with AI and machine learning,” added Delangue. “And that’s what we saw on Hugging Face: the impact of open source open science. All of you in the room have contributed over 100,000 open models to the platform.
The battle between open and closed LLMs
In recent weeks, a high-stakes debate has unfolded over whether big new AI models should remain proprietary and commercialized or rather be released as open-source technologies.
On the one hand, the researchers argue that transparency reduces risks and commercial pressures to deploy AI before it is ready; on the other hand, companies claim that secrecy is necessary to profit from and control their technology. The issue has gained momentum in recent weeks as LLMs are starting to sound the alarmbut there is still no consensus on whether open science or commercialized AI will produce more reliable systems.
Wednesday, three days before the event on open source AI, a very controversial event open letter calling for a six-month pause on large-scale AI development has made the rounds in the AI community. The letter was signed by the likes of Elon Musk, Steve Wozniak, Yoshua Bengio, Gary Marcus, and several thousand other AI experts, researchers, and industry leaders.
“I think OpenAI has done an amazing job of advancing the state of the art. I think they’re advancing big language models first through GPT-2 and GPT-3 and then the instructGPT or ChatGPT style that follows the instructions. So I think that’s at least two major breakthroughs that OpenAI is responsible for”, Andre Ngone of the most influential voices in machine learning over the past decade, said in an interview with VentureBeat.
“At the same time, I feel like I’m also excited about all the open language models that are released,” he added. “But I think it’s very reasonable if, for different reasons, different companies choose to have different policies. I’m excited about very open models and grateful for all researchers who publish open models, but I’m also grateful for all the work OpenAI has done to move this forward.
The way to ethical AI probably depends on the balance between scientific openness and corporate secrecy. But that balance clearly remains elusive, and the future of AI hangs in the balance. How tech companies and researchers collaborate — or not — will determine whether AI elevates or endangers our lives. The stakes are immense, but so are the challenges of navigating this debate.
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