In a planet significantly shaped by artificial intelligence, some organizations have left a mark in 2024 like the open-source job Hugging Face.
What began as a robot app has since evolved into a hotspot for open-source AI, becoming an essential resource for academics, designers, and businesses alike. By 2023, following several investment rounds, Hugging Face was valued at$ 4.5 billion.
Hugging Face is Emerge’s Project of the Year 2024 because of its pioneering contributions to AI and commitment to democratizing system teaching. With creative leadership, open-source equipment, and a solid focus on morality, it empowers researchers and startups international. Thanks also to a growing online group of open-source AI fans, Hugging Face has become a standard-bearer for dependable and creative AI technology.
What is Hugging Face?
Hugging Face, founded in 2016 by European companies Clément Delangue, Julien Chaumond, and Thomas Wolf and based in New York City, is an open-source system for machine learning and natural language control.
Consisting of a large library of over one million AI models, 190, 000 datasets, and 55, 000 video apps, Hugging Encounter lets developers, researchers, and data scientists build, coach, share, and install AI models.
We initially saw our study code as being distributed as a gaming company, and we realized we could make a lot more of it open-source. That led to the development of our transformers collection and the community’s appreciation for its effects, according to co-founder and chief technology officer Wolf. ” We think open-source is the key method to simplify system learning”.
At its base is the converters collection, which offers state-of-the-art pre-trained versions for a wide range of jobs. People can explore versions through browser-based conclusion widgets, access them via API, and build them across computing surroundings. Through its Hub, a central store where customers can experiment with and help to cutting-edge AI types, users can communicate and refine models and collaborate.
A pre-trained AI model that includes weights and characteristics learned from primary datasets to train the model can be fine tuned to accomplish a task or improve performance on a particular dataset.
” Open science and open-source AI prevent blackbox systems, make companies more accountable, and help ]solve ] today’s challenges—like mitigating biases, reducing misinformation, promoting copyright, and rewarding all stakeholders including artists and content creators in the value creation process”, co-founder and CEO Delangue said on X ( formerly Twitter ).
democratizing AI
” Democratizing AI,” or encouraging people to apply AI for social good, development, and solving intricate problems without the consent of corporations or governments, is a common refrain in the fragmented and open-source group.
Hugging Face stands out for making cutting-edge tools readily available to the world Artificial community in an economy dominated by custom solutions and closed communities. Delangue reiterated Hugging Face’s commitment to the cause of democratizing AI during a June 2023 congressional hearing of the Committee on Science, Space, and Technology.
” Hugging Face is a community-oriented company based in the U. S. with the mission to democratize good machine learning”, Delangue said during the hearing. Our platform for hosting machine learning models and datasets, as well as an infrastructure that supports research and resources, aim to lower the barrier for all backgrounds to contribute to AI, are the primary means of our mission, which is” We conduct our mission primarily through open source and open science.”
democratizing AI is particularly impactful in underrepresented regions and industries, where researchers and small startups often lack the resources to compete with tech giants.
” The long-standing and widening resource divides, especially between industry and academia, limit who is able to contribute to innovative research and applications”, Delangue told Congress. We firmly support the U.S. National AI Research Resource and the funding of startups and small businesses conducting public interest research.
Collaboration over competition
Emphasizing Hugging Face’s collaborative spirit, the company has worked with other big names in AI, including Google, AWS, Meta, Nvidia, and Microsoft.
In January, Hugging Face teamed up with Google Cloud by combining its own open models with Google’s infrastructure, all with the goal of making AI more accessible. That same month, Hugging Face introduced its Hallucinations Leaderboard, which the company launched to address the ongoing problem of AI hallucinations.
The challenge is now having enough startups and teams ready to deploy models across a variety of verticals, Wolf said. ” No need to wait for GPT-5, it’s time to build AI applications now by learning how to use, evaluate, and adapt these models in today’s world”.
Hugging Face expanded its partnership with Microsoft in May, which started in 2022, giving developers greater infrastructure and tools to build more potent versions of their Copilot AI models. Later that month, Amazon and Hugging Face made a new partnership to make it simpler for developers to create AI models using Amazon’s computer chips.
In July, the computer chip manufacturer Nvidia made a joint venture with Hugging Face that would enable developers to use Llama 3 and other AI models with up to five times faster token processing.
In October, Hugging Face launched HuggingChat, the platform’s answer to OpenAI’s ChatGPT. Users of HuggingChat can select from a large selection of open-source AI models for its text generation capabilities. Hugging Face Generative AI Services, or HUGS, a tool for developers to deploy and train AI models offline in a personalized setting, followed by that.
Hugging Face and NVIDIA announced a partnership to advance open-source robotics at the Conference for Robot Learning in Germany in November by combining Hugging Face’s robotics platform LeRobot with NVIDIA’s AI tools to blend simulation and real-world training, all with the aim of making robots smarter and more effective.
It hasn’t always been smooth sailing for Hugging Face, however. After it was revealed that more than a million posts were created using scraped content from the rapidly expanding Bluesky social media platform, the company faced backlash in November.
” I’ve taken the Bluesky data out of the repo. Although I urged the platform’s tool development, I know this approach violated the principles of transparency and consent in collecting data,” wrote Bluesky’s Hugging Face Machine Learning Librarian Daniel van Strein. ” I apologize for this mistake”.
The outlook for Hugging Face
The CEO of Hugging Face made his predictions for the upcoming year in AI, including the first significant public protest against AI, a significant company’s market capitalization being cut in half as a result, and more than 100, 000 personal AI robots going on pre-order.
With 15 million AI builders on Hugging Face, Delangue predicted that” we will start to see the potential for economic and employment growth.”
Wolf shared a similarly optimistic view of the future of open-source AI and robotics moving into 2025, pointing to more energy-efficient models, open-
Wolf remarked,” Many things about the future pique my interest, but just a few,” adding. ” Smaller models that can be much more energy efficient, the rise of open-source robotics and the extension of all the tools we’ve discovered in AI to the field of science, for example, weather prediction, and material discovery”.
Hugging Face played a pivotal role in AI’s evolution in 2024 by driving innovation, global accessibility, and transparency while lowering barriers for startups and developers to create a multitude of AI solutions.
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