Ramin Hasani: evaluation

Transcript context

…And how about the differential equation inspired original liquid neural networks? It's clear that the worm is running on a very small amount of watts. Do we, in practice, do you encounter things that are so resource constrained that you have to go to these extreme, extremely specific or extremely biased architectures to actually deploy today? Yeah, 100%. Think about places where you have to bring in latency to, like computation has to happen like in microseconds. So you cannot really afford to have like larger computational complexity. You want to have the most simple type of system that actually handles that. And you know what, there you can have adaptive systems, like you can have like many different formats of systems. And there, this is like one place. The other thing is simulation in a whole, as I mentioned, like in physics, You have physical data that is coming, like you want to build a digital twin of a, let's say, like a chemical reaction that happens at a factory, right? So, in those kind of places, you would go towards like an extremely biased and even maybe just differential equation-based models like liquid neural networks today, like they're getting applied to many, many different applications. I see that because the repository, the original repository from years ago is still like open source, you know, like people are still like building predictive kind of machine learning models on sequential data or physical data or sensor data that it makes sense because the systems are continuous time and it makes sense to have like a continuous. time that I'm consistent to apply to modeling those kind of behavior. And then you can like today with these larger instances like clouds of the world, you could also like direct your agents to actually try out a bunch like in an auto research kind of format, you can just direct them to say, hey, you know what, go pick like the neural networks, like the best neural networks that would be the best fit for these type of data set. And then here is kind of the space of possibilities you want to explore, like based on the biases that you have, like machine learning people are now Mostly like orchestrating kind of these automated kind of agentic pipelines as we are building, so that's what's happening in terms of Liquid ourselves like we are trying to contribute to the open source and we are open sourcing some of the instances that are coming. out of our, let's say, search spaces, like let's say LFM2, LFM3 is going to be like, again, the objective is that the next generation should always beat the previous generation on the criteria that we care about, without sacrificing quality. That's extremely important for us. And then the range of possible, like we decide, for example, how large of a neural network we want to make based on these neural networks that we have, and then this becomes the open source version of our models. Then we work very closely also with silicon companies. we work with AMDs of the world, Qualcomms of the world. And what we do with them, we try to understand the silicon roadmap that they have, you know, the hardware roadmap that they have. con companies. we work with AMDs of the world, Qualcomms of the world. And what we do with them, we try to understand the silicon roadmap that they have, you know, the hardware roadmap that they have. And based on that roadmap, we try to kind of inform also like what they have to do even in the next generation of their ASICs, you know, because when we have like this understanding of the algorithmic aspects of intelligence and the variety of things that they could be supporting, in order to reduce, dramatically reduce the cost or satisfy the constraints of the use cases that they want to enable, this is, these are the considerations that you have to do. Or even building like to the point that we can build also like, you know, like a specific foundation model graphs, like graphs for them, you know, like that's, and that's kind of the projects that we do with semiconductor companies. In terms of like on the commercial kind of side of things, we also take models, we apply them in many different I call the category that Liquid AI is very enabled, we call the device foundation models. Anything, all the processors that are outside of data centers, we try to apply our technology to those kind of places. This is outside of data centers. Inside of data centers, we apply to constrained use cases. Use cases like low latency, like ultra low latency applications of AI. Extremely long sequences and wanting very, very small memory footprint of your AI system. You want to have one cost efficient implementation of these foundations.…

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