Challenging the Average With Open-Source AI: Hugging Face’s Thomas Wolf
Thomas Wolf is the cofounder and chief science officer of open-source AI platform Hugging Face, which provides access to thousands of pretrained AI models that can be downloaded and run locally. With over 10 million users, getting started on the site can be a daunting task. Thomas explains how the company aims to improve its accessibility through documentation on the company blog as well as community feedback, similar to social media likes and upvoting.
Thomas and Sam discuss the benefits and trade-offs of both open-source and closed-source AI models, as well as the evolution of microchips and the future of hardware and software development — as well as the hopes Thomas has for the future of coding with AI, starting with his children’s generation. Read the episode transcript here.
Guest bio:
Thomas Wolf is cofounder and chief science officer of Hugging Face, a collaborative AI platform. Wolf likes creating open-source software (OSS) that makes complex research, models, and data sets widely accessible. He can also be found pushing for open science in research in AI and machine learning, to try lowering the gap between academia and industrial labs through projects like the BigScience Workshop. He also writes and produces education content on AI, machine language, and natural language processing, including the reference book Natural Language Processing with Transformers, The Ultra-Scale Playbook, his blog, and videos.
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Building the "App Store" for Robots: Hugging Face's Thomas Wolf on Physical AI
Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, explains how his company is applying the same community-driven approach that made transformers accessible to everyone to the emerging field of robotics. Thomas discusses LeRobot, Hugging Face's ambitious project to democratize robotics through open-source tools, datasets, and affordable hardware. He shares his vision for turning millions of software developers into roboticists, the challenges of data scarcity in robotics versus language models, and why he believes we're at the same inflection point for physical AI that we were for LLMs just a few years ago.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Hugging Face’s co-founder on bringing open-source AI to life with cute robots
Hugging Face’s new AI robot, the Reachy Mini, has already racked up $1 million in sales just five days after launch. But the company isn’t trying to build a chore-doing humanoid just yet. Instead, Hugging Face sees the Reachy Mini as a hackable, desk-friendly device that's part entertainment, part entry point for developers and consumers to experiment with AI in physical form.
On this episode of Equity, co-founder Thomas Wolf joins to explain why open-source AI needs hardware, how Hugging Face is thinking about robotics long term, and what might happen if people actually start coding apps for their robots.
We'll also get into:
How Hugging Face plans to leap from software to hardware.
Hugging Face's ambitions to one day sell a full-sized humanoid robot.
The role of privacy in consumer robotics, and how open-source can address it.
Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.
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Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We’d also like to thank TechCrunch’s audience development team. Thank you so much for listening, and we'll talk to you next time.
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Big Science and Embodied Learning at Hugging Face 🤗 with Thomas Wolf - #564
Today we’re joined by Thomas Wolf, co-founder and chief science officer at Hugging Face 🤗. We cover a ton of ground In our conversation, starting with Thomas’ interesting backstory as a quantum physicist and patent lawyer, and how that lead him to a career in machine learning. We explore how Hugging Face began, what the current direction is for the company, and how much of their focus is NLP and language models versus other disciplines. We also discuss the BigScience project, a year-long research workshop where 1000+ researchers of all backgrounds and disciplines have come together to create an 800GB multilingual dataset and model. We talk through their approach to curating the dataset, model evaluation at this scale, and how they differentiate their work from projects like Eluther AI. Finally, we dig into Thomas’ work on multimodality, his thoughts on the metaverse, his new book NLP with Transformers, and much more!
The complete show notes for this episode can be found at twimlai.com/go/564