Ramin Hasani: evaluation

Transcript context

…Yeah, absolutely. So Liquid AI, 3 1/2 years ago, we spun out of MIT, CSAIL, building on a technology that we have actually have been working on it like 10 years before, like basically like a decade before when we started the whole company. We've been, our objective function like at MIT has always been like maximizing the amount of intelligence that we can into smaller format of algorithms. So efficiency has been like the cornerstone of our research. And we have been working on like specifically thinking about robotics and systems that were like coming into the real world, like the idea of liquid neural networks that was kind of discovered on my PhD thesis and together with my co-founders, the four co-founders that I have, we've been researching ideas about how can we build machine learning solutions that can go on robots and doesn't have like millions of billions of parameters. so that we can actually host them directly on, let's say, CPUs, NPUs, or let's say like, smaller GPUs that are mounted on top of like physical systems while delivering the reliability of much larger kind of instances of artificial intelligence systems. Essentially, what we try to do, we try to, build alternative algorithms in order to get creative in the algorithmic space to see how can we build machine learning systems that can generalize beyond the data that they have seen. Because when you go in the real world, the distribution shifts becomes like a real thing. You know, like you imagine you deploy a robot in an open world, kind of like let's say an autonomous car, a flying drone, a fixed-me vehicle, you know. So All of these systems are going to go on very, very rapidly you're going to get out of distribution. So you have to actually build systems that are really comfortable around getting it, being able to get out of distribution. You know, humans are extremely good at it, and humans do that extremely at an extreme, I mean, natural learning systems, animals also like the same way, right? So they... they actually follow a really nice trajectory out of distribution as well. So that means like the learned concepts can generalize to data that you have not seen before, right? So our idea was that like can we build systems that can have that kind of properties, you know, better out of distribution generalization. This has been not just our goal, I think an entire artificial intelligence field has been like working towards this thing and especially like in the robotics and closed loop environment, you know, like closed loop begin environment where you have an agent acting in an environment. You want to have that kind of properties, so naturally the place that we started looking into was brains. We started looking into animal brains, and we started looking into how one thing that I really liked, I wanted to look at it from a first principle kind of approach, like how neurons exchange information with each other. We started looking into brain of worms, small animals. thing that I really liked, I wanted to look at it from a first principle kind of approach, like how neurons exchange information with each other. We started looking into brain of worms, small animals. and why worms, this specific worm C. elegans, you started working on the brain of the worm. The reason behind it is that it was the only animal that in 2015 when I started this type of research as part of my PhD with my co-founder Matthias Lechner, this was the only animal that we knew the entire nervous system. as a whole, and this animal exhibits massive amount of kind of sensory reactive kind of behavior, like amazing levels of control with 300 cells in its nervous system. And that's what's fascinating for us because this is much smaller than any neural network that performs control at that time, like on, let's say like autonomous systems, and this worm, it can do better, dexterous movements and stuff like better than, the best robotic systems that we actually had in the world. So we thought that, okay, so let's start understanding how neurons exchange information in the brain of the worm. From there on, let's start building nervous system more complex than complex kind of neural circuits so that we can get to the stage where we can, let's say, like build the next animal, build the next, you know, like basically follow the path of evolution of nervous systems, you know, and see how we can rise and evolve as part of this thing. So There are equations that describe the neuronal dynamics of, let's say, like 2 neurons. In the brain of C. elegans, because the worm is very small, the neurons do not spike. e as part of this thing. So There are equations that describe the neuronal dynamics of, let's say, like 2 neurons. In the brain of C. elegans, because the worm is very small, the neurons do not spike. They're graded kind of neurons. Like they behave like in an electrotonic way, you know? So they're very similar to artificial neural networks that we have because they're also very differentiable, right? Because you don't have spikes in the activity of neurons, they're very differentiable. That's why it was even nicer for us, more attractive for us, because we could apply learning theory to these type of neural networks. Once constructed, let's say two neurons, four neurons, eight neurons, let's say 100 neurons next to each other, and then you start training them, you can apply back propagation as a differential kind of programming on top of a system that is built by these type of inspirations that we got from nature. The type of differential questions that were there were also very well-behaved. You can make them as complicated as you can, and then when they get more complicated, they mimic their biology much more. And of course, from a computational footprint, they become more complex to scale, but it's still like you can sacrifice, you can actually tune how much complexity you want to encode inside a neural architecture. In artificial neural networks, we try to abstract away those complex differential equation that describes the neural dynamics between two cells, and we just show them with a sigmoidal function, gated sigmoidal functions, and now we have matrix multiplications that are coming, capturing the impact of the inputs that are coming to a system with a transformer-based architecture and attention mechanism and all of those things. But these are all simplified computations for us to be able to scale these machine learning solutions. For us, from a neuroscience perspective, it was very interesting for us to explore to see if we actually start making, like bringing back those differential equation-based computation and a little bit more elaborate form of computation into the behavior of every single neuron and mimic the behavior of like how 2 neurons exchange information with each other, maybe we can unlock something like greater than what we have seen from an artificial neural networks perspective. So, I mean, early on, the results were fascinating. We saw that with 12 neurons, with actually 12 neurons, you could parallel park autonomously like a car, like a small car. With 19 neurons, you could drive a car. With 30 neurons, you can fly autonomously, like navigating kind of a drone. And you can get sensory information and process information with these. a little bit more complex and elaborate version of these neural dynamics, which we called liquid neural networks and liquid for adaptability. I called it liquid time constant neural networks, like that was the LTC kind of paper that we got out. And we coined the name liquid for the fact that the... dynamics of these systems are staying flexible even after training.…

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