The Ledger
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Delangue: China is likely the current leader in open-source AI
“China has been publishing much more open source AI lately. I would argue that they're probably the leader today of open source AI which is surprising to some, but for example, in, in video, they've been leading in, in open source AI.”
Delangue: Traditional software startup rules are outdated for AI
“A lot of the things that we learn with traditional software are like outdated with AI.”
Delangue: Open-source AI leads closed-source in most text applications
“Open source is actually heads of, closed source for most of the text applications today, especially when you have, like, a very specific, narrow use case.”
Delangue: AI regulation should address present risks, not doomsday scenarios
“In general, for me, I'm in favor of the regulations that are creating more competition, more opportunities for everyone to build with AI, and tackling some of the end user risks of AI today, like the risk of misinformation, the risk of biases. Instead of focus…”
Delangue: Every company and use case will have its own optimized model
“So the same way today, every company, every product has its own code base. Tomorrow every company, every use case is going to have its own model optimized.”
Delangue: There will ultimately be more AI builders than software engineers
“I think ultimately there's going to be more AI builders than software engineers, right?”
Delangue: NLP is the most important field of machine learning
“I believe that NLP today is the most important field of machine learning.”
Delangue: AI is ending human exclusivity over natural language processing
“We're really moving from a world where only humans could do natural language, which is why you have search large customer support, sales, community, communication departments in companies.”
Delangue: Every company will use NLP within three years or face disadvantage
“I believe that in in three years, every single company in the world will use NLP. And even more than that, I believe that in three years, a company that is not using NLP will be in a systematic disadvantage compared to a company that is using NLP, because it m…”
Delangue: Collective open source can outperform tech giants like Google and Facebook
“We believe that actually if all the other companies than Facebook, Google, Apple, Amazon are actually all contributing to the same open source technology, they can actually end up with something way better than each one of these guys could build.”
Delangue: OpenAI uses open source and is a Hugging Face customer
“As a matter of fact, open AI is using open source as a customer of Fergingface”
Delangue: Policy makers will require AI to be shared as public infrastructure
“I believe the policy makers will require it to be shared across as some sort of kind of like a fundamental infrastructure for all.”
Delangue: Full open-source AI requires sharing weights, datasets, and training scripts
“So it's a gradient, right? Openness is a gradient from kind of like full open source AI, which would be considered kind of like a practice where you share the weights, you share the data sets that you use to train the model. You usually share the training scri…”
Delangue: France promotes open-source AI much more than the US
“France also has promoted open source much more than the U.S.”
Delangue: Companies start AI cycles with large models, move to smaller ones
“We've seen a lot of companies who started their AI life cycle more with like a large model and then moving on to smaller models.”
Delangue: Public AI leaderboards are the wrong way to evaluate models
“People are doing evaluation the wrong way. You know, just looking at one massive public leaderboard that doesn't really tell them much about how the model is going to perform on their own use case.”
Delangue: Transfer learning was the missing piece enabling deep learning in NLP
“The last kind of, like, missing piece to this puzzle for us is transfer learning with the reference paper on the subject that came out a bit more than two years ago called Attention is all you need. And as a kind of, like, high-level, simplistic way that's wha…”
Delangue: Hugging Face generates zero revenue and has not focused on monetization
“We haven't had the constraints to work on monetization yet, so we're making zero revenue.”
Delangue: AI will create open-source commercial giants like Red Hat and MongoDB
“And I would expect there to be even more in AI. We'll have kind of like the MongoDB, the Elastic, the Red Hat of AI, which are going to be like open source AI, AI companies.”
AI will split between massive generalist and tiny specialized models
“So I think we're gonna end up in a world where there are both Right? On both sides of the spectrum, some extremely large, powerful, generalist, costly models for some use cases, all the way down to a very specialized, simple, optimized models, and depending on…”
Delangue: Hugging Face has 5M builders and 1M public models
“Today, as I mentioned, where we have five million AI builders using, using our platform, who collaboratively shared one million public models.”
Delangue: Consumer background drove Hugging Face's community-first model over B2B enterprise sales
“Before we had some experience around consumer, and so I think it helped us to have a very community driven approach instead of maybe a more, like, enterprise driven approach. Or more traditional kind of like B to B approach.”
Delangue: Hugging Face is profitable with most of $500M raised in bank
“We're lucky to be profitable. We raised a bit less than five hundred million so far. Most of it is still in the bank.”
Delangue: Non-text AI modalities will transform technology like NLP did
“It's gonna be audio, it's gonna be video, it's gonna be biology, chemistry, AI. These are, in my opinion the domains that in the next few years are going to change the world the same way NLP has in the past.”
Delangue: Open-source video generation will match Stable Diffusion's impact
“We're gonna have the stable diffusion model for video generation soon. There are a lot of, like proprietary models in in video generation, but the first companies that are going to release an open source video Generation model. I think it is going to have a tr…”
Delangue: Hugging Face has almost 1 million private models
“A big part of the usage of the Hugging Face platform now is in private mode. Almost as much as what is public. So there's almost one million private models on the platform.”
Most Hugging Face users will stay open source and free
“We think most of our usage and users are always going to stay open source and free and we need to build some sort of a freemium model.”
Microsoft and NVIDIA are massive Hugging Face enterprise customers
“When you think about Microsoft, when you think about NVIDIA all, all these companies are massive customers of ours.”
Over 2,000 companies, including NVIDIA, use Hugging Face's Enterprise Hub
“We have over 2000 companies using the enterprise hub today from kind of like Mercedes, Bloomberg all the way to larger ones like Nvidia, for example, that I was mentioning.”
Delangue: Hugging Face built the most popular open-source NLP library
“We mostly known For having built the most popular open source NLP library, which is on GitHub, that is called Transformers.”
Delangue: Over 1,000 companies including Apple and Bing use Hugging Face
“And we have more than a thousand companies using our library from medium-sized one, like Monzo, for example, the banking one, to really large ones like Bing or Apple.”
Delangue: Transformer models destroyed previous scientific benchmarks in NLP
“And what these, kind of, like, new, kind of, models did is that they started to basically like completely destroy all the previous science benchmarks, and really, kind of, like generate this immense, kind of, like, progression on, on the science side.”
Delangue: NLP models surpassed human baseline on GLUE benchmark by June 2019
“The human baseline, which means having humans do exactly the same tasks is just below what we reached in, in June last, last year.”
Delangue: Pre-trained NLP models require only a thin software layer
“Most of the intelligence is in the models, and the software engineering layer on top of these models is really thin. Which basically led these models to go to production really, really fast, right?”
Hugging Face spent over three years building an AI chatbot before pivoting
“Started building this Tamagotchi AI, right? So at the time it's like a Siri Alexa, but we wanted to make it entertaining. We did that for, ah, a bit more than three years”
Delangue: Half of Hugging Face's 1M public models downloaded recently
“Half of them have been downloaded in the past 30 days.”
Delangue: Users contributed over 200k datasets to Hugging Face
“They also contributed, I think it's more than 200,000 data sets to the platform.”
Delangue: Users built over 300k app spaces on Hugging Face
“And collectively they built over 300,000 spaces which are the apps, ah, on, on the Hugging Face platform.”
Delangue: Hugging Face acquired two startups in four months
“We actually acquired, acquired two in the past four months.”
Delangue: Over 5,000 leaderboards have been created on Hugging Face
“I think we crossed 5000 leaderboards that have been created on the Hugging Face platform as a way to rank your models based on their accuracy and their performance.”
Delangue has angel invested in nearly 100 startups in past two years
“I've invested in almost a hundred startups in the past, past two years.”
Delangue: Hugging Face raised over $20M from Lux, Betaworks, and Kevin Durant
“We raised a bit more than twenty million dollars from Lux BetterWorks, so a lot of, like New York investors and some Silicon Valley investors like A Capital and some fun angels like Kevin Durant before he moved to New York and the hat.”
Delangue: Hugging Face cited in over 700 research papers over 15 months
“We've been lucky to have been mentioned in more than 700 research papers for the last 15 months”