The Exchanges

Q They just built something and then it kind of took off, um, sort of separately. Do you have any sort of advice to founders who are considering changing direction or thinking of new directions for their company, or how do you How do you keep your eye out for the things that are really interesting or working that may or may not be the core thing of what you're doing?

A Well, I think the best way to do it is to find like the good ratio in your company between like exploitation and exploration. Um, and, and I think that's what a lot of startups are not always getting right. Um, not, not only before product market fit, but also after product market fit, I feel like sometimes companies before product market fit are experimenting kind of like too much changing directions every week. Uh, and, and I don't think you learn a lot from, from that. And then after market feeds, uh, product market feed, they can't like stop experimenting and, and kind of like trying new things and trying to stay away from the local optimum in a way, and looking more for like the global, global optimum. So for us, so what, what we've always done and, and I think we'll always do, um, with, with hugging face is to make sure that, you know, Whenever, always kind of like make sure to spend at least like 30 or 40% of the company's efforts on explorating new things and, and kind of like finding the long-term bets that is going to make you, make you successful. Um, and then give these, uh, you know, experiments and initiatives like a chance, right? Uh, for us, we were lucky that, uh, Thomas, one of our co-founders was leading This kind of like, uh, experiments. Uh, so it made it easier. Uh, but we have examples of other initiatives that started as experiments, uh, from team member…

AI assessment note: “find like the good ratio in your company between like exploitation and exploration.”

Q Um, maybe just to zoom out before we, uh, run out of time here, what are you most excited about in the next year of AI or, you know, expanding into the next five years?

A Um, I think, I think I'm, um, we, we talked a little bit about it in the past, and I'm really excited about, uh, biology and chemistry for, for machine learning, um, because I think, The way I see machine learning is really as kind of like this new paradigm to build old tech, right? It's, it's kind of like this analogy from where software one point was like the first paradigm and now we're in software two point, which is like machine learning power technology building. And so if you look at, you know, like the big sectors and the big kind of like impactful topics, um, that it could, it could change. Um, obviously biology and chemistry are, are kind of like, uh, up there. So, um, and, and we're seeing, uh, kind of like the numbers of models and datasets and demos on, on hugging face increasing. Like a few days ago, there was a release of, uh, bio GPT by, by Microsoft. Meta has been doing a lot of work on, uh, protein generation and prediction. Um, so I think there's gonna be really, really cool stuff coming up, uh, on, on these two topics.

AI assessment note: “I'm really excited about, uh, biology and chemistry for, for machine learning”

Q meeting I had at a trade show that completely took my life in a different direction too. So I think it's kind of odd how sometimes those things happen early in people's careers. Um, can you tell us a bit more about the early iteration of hugging face, how you decided to start it, the early days as a talking emoji and sort of where, where it went from there?

A Yeah, absolutely. With my co-founders, Julien and Thomas, we can't like always share this, this passion, excitement for, for AI and for machine learning. Um, and when we started hugging face, we were like, okay, what can we work on that is both scientifically challenging, but also fun. We, we didn't want to, you know, do some, something boring. So we were like, okay, we're going to build some sort of an AI Tamagotchi. Um, we were heavy users of, you know, Alexa and Siri, and we're like, why is it so boring? You know, why is it only talking about productivity stuff? Why is it, you know, just telling you the weather? And so we, we started to build that kind of like some sort of an AI friends, Tamaguchi AI, uh, basically what you, what you see, uh, in a lot of movies as sci-fi, um, probably a lot of what people are using chat GPT for today, actually. And we, we did that for, uh, almost, almost three years, uh, got some, some level of traction, uh, billions of messages exchanged between, between users, um, and, and, and the chatbots. Uh, so that's, that's how Hugging Face started.

AI assessment note: “we were like, okay, we're going to build some sort of an AI Tamagotchi.”

Q Are there specific areas or trends you're most excited about from either a research perspective or from a model implementation perspective?

A I mean, I'm, I'm really excited these days. It's, it's a bit of an unsexy, uh, thing to, to say, but, uh, by, by the infrastructure side of things, um, because no, I think so far, uh, as a whole for like the machine learning domain and ecosystem, we haven't thought too much about, you know, uh, what it costs to run some of these models. Um, you know, how, how fast they can go, how slow they can be. Um, and, and I, I hope that, uh, this year there's going to be some sort of like more, more clarity around that, um, to make sure that, you know, as. As a community, uh, we build something, you know, healthy and, and sustainable and, and not, you know, I feel like sometimes in the field, there's something that I call the, the cloud money laundering, where you almost kind of like disconnect the, you know, infrastructure costs to like the actual, actual use cases. Um, I think as, as the field is maturing, you're going to see much better alignment between the two. And I'm actually excited about that because I think it's going to be a big, Enabler for, for the fields in the, in the long run.

AI assessment note: “I'm really excited these days... by the infrastructure side of things”

Q Will you explain what Bloom is and more broadly, like how you decide where Hugging Face should be a first party participant in, you know, model training or research?

A Yeah. Bloom, um, is the result of an initiative called big science, which also led to big code that I just, just mentioned. Um, and big science was like the largest, uh, collaboration in, in machine learning to date with like a thousand researchers from 200 organizations, kind of like coming together in order to, uh, build and train a large language model completely in the open. Right. So everything was, was perfectly available. You can see all the runs that they did, all the brainstorms that they did to get to the decisions that, that they did. And it's been really, really excited to, to see that kind of like almost, uh, building organically with our, with our support and the support of a lot of organizations, uh, like for example, which is a French computer that, that provided the compute, compute for, for this. And it informed a lot our, our thinking around, uh, Ethics and, and kind of like openness. Uh, because one, one of the reasons why we so focused on, you know, open source, um, and, and open science at, at taking face is that we believe like the two main challenges with AI today are one kind of like the concentration of power and, and second, uh, biases that are encoded in, in these models. And for both, we can't like learn that building in the open with open source is actually Uh, more part of the solution than part of the problem, because obviously control of power a…

AI assessment note: “Bloom, um, is the result of an initiative called big science”

Q I think most organizations are not capable of that sort of risk taking. Um, so, um, that is, that's really cool. Um, what else, what else do you think you guys have done right on the sort of community growth aspect? Because I think now everyone knows that such a powerful driver for business for an increasing number of technology companies, but it's, it's pretty hard to actually execute against.

A Um, that's a good question. I think timing, um, obviously we've been really, really lucky with, with, with timing. Um, you know, trying to listening, listen, listen, it sounds a bit cliche, right? But actually listening to, to the community and implementing what, what the community is, is asking. Yeah. And, and then just like build your culture around it, uh, to have people who are like excited about contributing to the community. Um, even independently of, of everything else. You know, I think sometimes you have companies where You know, they're doing community or open source work, but it's almost as like a mean for other things. And it's like, sometimes feels like they're almost like, um, they have to do it to get other things that they're more excited about. For us, it's been useful to try to hire people who are genuinely excited about this work and, and if they were to do, they could kind of like almost work for free, uh, for the community on, on open source and they'd be happy. About it. Um, and, and so that, that creates like the right culture for this kind of work. I feel like.

AI assessment note: “listening to, to the community and implementing what, what the community is, is asking.”