Awesome. We are back here at Bits and Pretzels with Thomas from Hugging Face. Thomas, for those of us out there who don't know you, can you give us a quick intro who you are, what you do?
Of course, I'm a co-founder and chief science officer at Hugging Face. So, Hugging Face is a platform for open source models, data sets and spaces which are kind of AI apps. So, it's a place where you found a community who is building AI.
Awesome. And maybe again for those who are not so deep in these topics, what exactly does open source mean versus closed source and how does that apply to AI models?
Of course. Yeah. So you have two ways to use AI models nowadays. The first way is to go through CHP and so you use what is called a closed model which means you cannot really download the model on your computer for instance, you have to run it through the API. And the other way is called open source model or open weights more specifically. And the main difference is like software, you can download it, you can modify it, you can tweak it, you can control it, you have much more control on the AI model itself, and then you can put it into your own apps or do more things with it.
Yeah. For instance, if you want to fine-tune it because you think it needs to know a lot about legal and if it doesn't know enough, you can take an open source model, a legal data set like your contract and you can fine-tune the model which means you will train your model a bit more so it knows more about your own domain. And what is, can you maybe also explain like how do open source and closed source models differ? I understand the flexibility but in terms of speed monetization like what are other things that are pros and cons?
It's quite different. So when you use open source you leave on the table a little bit of the easy use I would say. Closed source you just pay basically a subscription, the thing works out of the box. Open source or open weights you need to download them, you need to find GPU to run them, which can be more complex. But on the other hand you have much more control. So let's say you want to run them super fast because you have a type of application you would like instant answer and not seeing the you know like JBD the thing writing, you can run them on some of these novel chips that exist like drug cerebrass and then you get extremely fast 5,000 tokens per second output. So for many novel application or exploration you actually better with open source model. Another equivalent, there was this summer a very interesting release called Genie3 from Google which is kind of a picture where this model generates a picture and you can work in the picture and interact with the picture. For this you will use open source model because you will take a video model, you will fine-tune it and you will deploy it in your application. So all of the things that are a bit exploratory but you have the knowledge to implement it yourself, usually people choose to go with open source. If you want the out of the box simple to use solution, you go with closed source. And open source is just usually more specific, you can give more context to the LLM. And I would say the other thing is you can also save a lot on the price because you can control on which GPU you run it, so you can run it on much cheaper GPU. But yeah, as I said this comes with the fact that you need more knowledge, you need to learn. So that's also why we have a big part of our website about education on how to use that learning.
And you recently acquired a robotics company. You've got the reaching mini coming out. I think the orders is rolling out nowish. Can you touch a bit more on why that acquisition and how you're leaning into robotics a bit?
Yeah, of course. So, I mean, one of my jobs at first is to keep an eye on the new trends in robotics. We're 8 years old now. There have been already many waves in AI. I would say in this 8 years, the LLM, the agents, all of this. And I think the next wave that's going to come, the next place where AI will kind of solve something is robotics. It's happening maybe the end of this year, mostly next year I think. And so that's why we decided first 18 months ago to start a software library called L robot. And what we discovered when we open sourced this software was like there was a lot of interest for that. The community around this, the open source robotics community grew exponential. And so this year we decided to double down and not only do software but also do some hardware. And I would say the specificity of the hardware we do is two ways: it's extremely very accessible. It's the same idea around learning. We want people to be able to learn about robots. And we see actually a lot of nontechnical people buying our robots to get a first foot. They're curious about robots. They don't want to buy, you know, 30,000 humanoid, but it's $300. That's fine. So, this is accessibility. And I would say the second one is building an open source platform so that people can take this robot and just like the model, they can tweak it, they can control it, maybe make it as a, you know, like a receptionist, like an agent to welcome people to a conference. So you can do all of that with this hardware provider.
It sounds a bit like you care a lot about the education piece and sort of making things accessible to the wider public. Is that right? Or can you talk a bit more about that?
If you think super long term, I think AI is going to be everywhere. And my goal is that we feel that's a technology we own and we control and that it's not a technology that controls us. For me the way to make that is actually to not only let people use the technology like CHP which is already great but also let people build the technology themselves so that they know how it works internally, what type of data they use to train a model, how you train a model, how you build a robot. We want this thing to be like common knowledge.
Yes. And so it doesn't really matter whether it's hardware software just whatever it is people need to understand it. And this is great for startups, right? Because here, if you want to build a startup, you don't want to just depend on someone providing you the tech. You want to build the stuff yourself. I think that's one reason a lot of them build an opportunity.
Yeah. And I mean, the closed source LLMs are always like a shortcut because they know a lot already and it kind of works.
But it's sort of like the Olympic of AI because it just, you know, it's a quick fix to a solution, but you don't really understand what's going on.
Exactly. What is, I mean you also do a lot of angel investing and you just said obviously AI is the main thing in startups right now. What are signs of greatness that you look for in founders building with AI?
So I would say the founder themselves is usually the biggest thing. So they need to have this ambition. I think AI, the one cool thing about AI is it's a whole green field. You can build a lot of things in AI. And I would say the counterpart of this is you have to be ambitious. There's a lot of small things you can build like small tweaks, but there is also what is much more interesting to me is you can completely change the world in some part. So I'm looking for these founders who have huge ideas about things they really want to change in the world. And the second thing is, for technical people, you have to be very technical in AI. You have to understand a bit how the models work to understand also what is coming because it's nice to build with what is today. It's even better to build with the tech you think there's going to be in 6 months, one year. So you have to be very technical to understand that. And then I think you're just good mentoring and that's why I do a lot of angel investment which is basically I try to help these people to grow.
How many angel investments have you done?
