StageSeries C | Alive
Last Raised$100M | 1 yr ago
About Hugging Face
Hugging Face operates as an artificial intelligence (AI) company. It offers an open-source library for users to build, train, and deploy artificial intelligence (AI) chat models. The company was founded in 2016 and is based in Brooklyn, New York.
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The generative AI — text-to-code market offers solutions that can automatically generate code from natural language descriptions. This technology can save time and increase efficiency for developers, as well as make coding more accessible to those without extensive programming knowledge. The market includes vendors with different value propositions, such as making it easier to extract insights or …
Hugging Face named as Leader among 8 other companies, including Dremio, Arria NLG, and Promethium.
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CB Insights Intelligence Analysts have mentioned Hugging Face in 2 CB Insights research briefs, most recently on May 10, 2023.
Jan 25, 2023The state of generative AI in 7 charts
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Latest Hugging Face News
May 19, 2023
The Verge The Senate’s hearing on AI regulation was dangerously friendly / As politicians and companies agree on the need to regulate AI, experts warn of regulatory capture — of letting corporations write lax rules that lead to public harm. By James Vincent , a senior reporter who has covered AI, robotics, and more for eight years at The Verge. May 19, 2023, 12:53 PM UTC | Share this story Photo by Win McNamee / Getty Images The most unusual thing about this week’s Senate hearing on AI was how affable it was. Industry reps — primarily OpenAI CEO Sam Altman — merrily agreed on the need to regulate new AI technologies, while politicians seemed happy to hand over responsibility for drafting rules to the companies themselves. As Senator Dick Durbin (D-IL) put it in his opening remarks: “I can’t recall when we’ve had people representing large corporations or private sector entities come before us and plead with us to regulate them.” This sort of chumminess makes people nervous. A number of experts and industry figures say the hearing suggests we may be headed into an era of industry capture in AI. If tech giants are allowed to write the rules governing this technology, they say, it could have a number of harms, from stifling smaller firms to introducing weak regulations. Industry capture could harm smaller firms and lead to weak regulations Experts at the hearing included IBM’s Christina Montgomery and noted AI critic Gary Marcus, who also raised the specter of regulatory capture. (The peril, said Marcus, is that “we make it appear as if we are doing something, but it’s more like greenwashing and nothing really happens, we just keep out the little players.”) And although no one from Microsoft or Google was present, the unofficial spokesperson for the tech industry was Altman. Although Altman’s OpenAI is still called a “startup” by some, it’s arguably the most influential AI company in the world. Its launch of image and text generation tools like ChatGPT and deals with Microsoft to remake Bing have sent shockwaves through the entire tech industry. Altman himself is well positioned: able to appeal to both the imaginations of the VC class and hardcore AI boosters with grand promises to build superintelligent AI and, maybe one day, in his own words , “capture the light cone of all future value in the universe.” At the hearing this week, he was not so grandiose. Altman, too, mentioned the problem of regulatory capture but was less clear about his thoughts on licensing smaller entities. “We don’t wanna slow down smaller startups. We don’t wanna slow down open source efforts,” he said, adding, “We still need them to comply with things.” Sarah Myers West, managing director of the AI Now institute, tells The Verge she was suspicious of the licensing system proposed by many speakers. “I think the harm will be that we end up with some sort of superficial checkbox exercise, where companies say ‘yep, we’re licensed, we know what the harms are and can proceed with business as usual,’ but don’t face any real liability when these systems go wrong,” she said. “Requiring a license to train models would ... further concentrate power in the hands of a few” Other critics — particularly those running their own AI companies — stressed the potential threat to competition. “Regulation invariably favours incumbents and can stifle innovation,” Emad Mostaque, founder and CEO of Stability AI, told The Verge. Clem Delangue, CEO of AI startup Hugging Face, tweeted a similar reaction: “Requiring a license to train models would be like requiring a license to write code. IMO, it would further concentrate power in the hands of a few & drastically slow down progress, fairness & transparency.” But some experts say some form of licensing could be effective. Margaret Mitchell, who was forced out of Google alongside Timnit Gebru after authoring a research paper on the potential harms of AI language models, describes herself as “a proponent of some amount of self-regulation, paired with top-down regulation.” She told The Verge that she could see the appeal of certification but perhaps for individuals rather than companies. “You could imagine that to train a model (above some thresholds) a developer would need a ‘commercial ML developer license,’” said Mitchell, who is now chief ethics scientist at Hugging Face. “This would be a straightforward way to bring ‘responsible AI’ into a legal structure.” Mitchell added that good regulation depends on setting standards that firms can’t easily bend to their advantage and that this requires a nuanced understanding of the technology being assessed. She gives the example of facial recognition firm Clearview AI, which sold itself to police forces by claiming its algorithms are “100 percent” accurate. This sounds reassuring, but experts say the company used skewed tests to produce these figures. Mitchell added that she generally does not trust Big Tech to act in the public interest. “Tech companies [have] demonstrated again and again that they do not see respecting people as a part of running a company,” she said. Even if licensing is introduced, it may not have an immediate effect. At the hearing, industry representatives often drew attention to hypothetical future harms and, in the process, gave scant attention to known problems AI already enables. For example, researchers like Joy Buolamwini have repeatedly identified problems with bias in facial recognition, which remains inaccurate at identifying Black faces and has produced many cases of wrongful arrest in the US. Despite this, AI-driven surveillance was not mentioned at all during the hearing, while facial recognition and its flaws were only alluded to once in passing. Industry figures often stress future harms of AI to avoid talking about current problems AI Now’s West says this focus on future harms has become a common rhetorical sleight of hand among AI industry figures. These individuals “position accountability right out into the future,” she said, generally by talking about artificial general intelligence, or AGI: a hypothetical AI system smarter than humans across a range of tasks. Some experts suggest we’re getting closer to creating such systems , but this conclusion is strongly contested. This rhetorical feint was obvious at the hearing. Discussing government licensing, OpenAI’s Altman quietly suggested that any licenses need only apply to future systems. “Where I think the licensing scheme comes in is not for what these models are capable of today,” he said . “But as we head towards artificial general intelligence … that’s where I personally think we need such a scheme.” Experts compared Congress’ (and Altman’s) proposals unfavorably to the EU’s forthcoming AI Act. The current draft of this legislation does not include mechanisms comparable to licensing, but it does classify AI systems based on their level of risk and imposes varying requirements for safeguards and data protection. More notable, though, is its clear prohibitions of known and current harmful AI uses cases , like predictive policing algorithms and mass surveillance, which have attracted praise from digital rights experts. As West says, “That’s where the conversation needs to be headed if we’re going for any type of meaningful accountability in this industry.”
Hugging Face Frequently Asked Questions (FAQ)
When was Hugging Face founded?
Hugging Face was founded in 2016.
Where is Hugging Face's headquarters?
Hugging Face's headquarters is located at 20 Jay Street, Brooklyn.
What is Hugging Face's latest funding round?
Hugging Face's latest funding round is Series C.
How much did Hugging Face raise?
Hugging Face raised a total of $163.92M.
Who are the investors of Hugging Face?
Investors of Hugging Face include Betaworks Ventures, Lux Capital, Addition, Kevin Durant, Olivier Pomel and 31 more.
Who are Hugging Face's competitors?
Competitors of Hugging Face include Stability AI, Cohere, OpenAI, betterdata, Anthropic and 12 more.
Compare Hugging Face to Competitors
AI21 Labs builds AI systems with the capacity to understand and generate natural language. Its product, Wordtune, provides solutions for grammar and spelling fixes. It helps to write accurately. The company designs advanced artificial intelligence tools and language models that understand the context and semantics of written text. The company was founded in 2017 and is based in Tel Aviv-Yafo, Israel.
OpenAI is an AI research and deployment company that discovers and enacts the path to safe artificial general intelligence by conducting research and implementing machine learning. Focused on generative AI, the company develops GPT, Dall-E, and chatGPT. Its Generative Pre-trained Transformer (GPT) products offer a large language model (LLM) that uses deep learning to produce human-like text. The company was founded in 2015 and is based in San Francisco, California.
deepset aims to bridge the gap between NLP research and the industry. With its open-source software, it enables developers to use the latest language models and transfer learning techniques for their individual tasks. Its additional features and services help enterprises to build, run, and maintain production-ready NLP applications. deepset was founded in 2018 and is based in Berlin, Germany.
Dashbot operates as an artificial intelligence (AI) based conversational data platform. It ingests, cleans, stores, and processes conversation data such as phone calls, customer service interactions, and social media chats. It provides insights for enterprise stakeholders, data science and analytics experts, and more. The company was founded in 2016 and is based in San Francisco, California.
Rasa Technologies provides open-source machine-learning tools for developers and product teams. It helps to expand chatbots beyond answering simple questions offering machine learning-based dialogue tools to allow developers to automate contextual conversations. It was founded in 2016 and is based in San Francisco, California.
One AI is an API-first Natural Language Processing (NLP) service that is built for developers. Users can embed its API to analyze, process, and transform text in their project. The company's Language Skills enable language comprehension in context, transforming texts from any source into structured data to use in code. No training data or NLP/ML knowledge are required. One AI was founded in 2021 and is based in Ramat Gan, Israel.
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