Hassani describes how Liquid AI derived, for the first time in 2022, a closed-form solution to the differential equations governing liquid neural network dynamics — a problem open since 1907 — enabling scaling from hundreds of neurons to billions. ✦ AI generated
Ramin Hasani · The Cognitive Revolution · 2026-07-04 · original ↗
starts at this moment · 17:58
“So maybe for starters, how would you tell the kind of broad story of Liquid AI leading up to what you're doing today and what the company's mission is today?”
we for the first time we actually solved that and in 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 uh published in November of 2022. This is called the closed form continuous time uh systems you know like this is this liquid neural networks in closed form and uh the closed form I mean it has massive implications.
verbatim transcript · starts at 17:58
17:58representation for a neural neuronal dynamics right and then from there on so that if in every textbook that you read this type of formats of equations they do not have a known closed form solution you know and this format like liquid neural networks were also part of that type of equations we for the first time we actually solved that and in this was like 2022 Around 2022, we solved the
18:23liquid neural network kind of interaction of neurons with each other in closed form for the first time. This became a nature machine intelligence paper uh published in November of 2022. This is called the closed form continuous time uh systems you know like this is this liquid neural networks in closed form and uh the closed form I mean it has massive implications. Why? Because now I don't need to use any
18:44numerical solvers to actually run a liquid neural networks. I can now have not only hundreds of neurons but now I have I can have billions of neurons next to each other and I can actually scale these computation still keeping the nonlinearity as part of this thing you know then then in in January of act actually beginning of February of 2023 an article came out of a quantum magazine you know about let's
19:10say me and my co-founder Matias we um it was about a profile about like oh the implications of some having some close form solution finally on on on a on on neuron dynamics you know like and how important this can be for both machine learning and also like for brain science as a whole and then so my inbox was full of VCs you know Silicon Valley started
19:30like talk like everybody wanted to throw like a term sheet at us like to really get started and and really scale this technology because this was fundamentally different than a transformer-based architecture and attentionbased architecture this was grounded in biology like the type of math that we have around the same times we Alternative models are coming out like state space models like you know like like the you know like you've
19:52you've seen like faster iterations of state space models like convolutional neural networks that came out like to be like 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 you know and we have something here that is also another category you know like it adds some operators that we learned from biology and from physics as we we grew