Ramin Hasani: evaluation

Transcript context

…for the first time, we actually solved that. And this was like 2022. Around 2022, we solved the liquid neural network kind of interaction of neurons with each other in closed form for the first time. This became a Nature Machine Intelligence paper published in November of 2022. This is called the closed form continuous time systems. You know, like this is liquid neural networks in closed form. And the closed form, I mean, it has massive implications. Why? Because now I don't need to use any numerical solvers to actually run a liquid neural networks. I can now have not only hundreds of neurons, but now I can have billions of neurons next to each other, and I can actually scale these computations. Still keeping the non-linearity as part of this thing, you know? Then, in January of actually beginning of February of 2023, an article came out of a Quanta magazine, about let's say me and my co-founder Matthias, we it was about a profile about like all the implications of having some closed form solution finally on. on neural dynamics, and how important this can be for both machine learning and also for brain science as a whole. And then, so my inbox was full of VCs. Silicon Valley started like talk, like everybody wanted to throw it like a term sheet at us, like to really get started and really scale this technology because this was fundamentally different than a transformer-based architecture and attention-based architecture. This was grounded in biology, like the type of math that we have around the same times we have alternative models are coming out like state space models, like you've seen faster iterations of state space models, like convolutional neural networks that came out to be scalable, all of them in a linear form because the scalability of alternative models, you have to linearize them and then you can scale them. And we have something here that is also another category, like it adds some operators that we learn from biology and from physics as we grew the research. And we got to the point where we can now become very competitive and building models for solving more and more general purpose tasks. And when I say more and more general purpose tasks, I'm talking about modeling signals beyond predictive performance, modeling signals for language, for audio, and for vision in a way that humans understand, right? That's the complex format that humans understand. So the mission of Liquid AI became building efficient general purpose AI systems. systems at every scale. And we coined the word efficient in our mission, and we care about it so much, because I think we are, from a foundation model company's perspective, we are probably the most efficient foundation model company on the planet. I'll tell you why about this in a minute. it so much, because I think we are, from a foundation model company's perspective, we are probably the most efficient foundation model company on the planet. I'll tell you why about this in a minute. But the reason why we set the mission into efficient machine learning, because we start at how much of the considerations that we had to do in order to really build a closed-form solution, then taking this closed-form solution and scale it to, let's say, a system that can model language, a system that can model audio models, a system that can model video and vision in general, you know, how much considerations we had to go into these things so that we can, in a computationally tractable way, scale our neural architectures to the point that where we are today. the base of our technology. I'll talk about like the core technology of the company later on, but I will tell you today the technology became liquid foundation models. Liquid foundation models or elephants are pretty popular right now. Like we're ranked #5 actually in the US from the number of downloads from the popularity perspective. We have over 1,000,000 downloads per week on the Hugging Face. Like that's a lot of downloads of these small models that we're building. They're very popular. Like the top ones from the US side are Google, Meta, Microsoft, and Nvidia, and the fifth one is So from the US side. So I mean, we got ourselves there by consuming about 1,000 GPUs, having 1,000 GPUs in-house. So that's, like from a foundation model perspective, like getting to that level of popularity and releasing more than 50 models, instantiation of models that people are using in enterprises, that's kind of the place where we brought Liquid, building on top of inspirations that we got from nature and the past that I actually like portraying for you. Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr…

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