Beyond Liquid Neural Networks

Welcome to the Heliox Podcast! Today, we're in for a mind-bending journey into the future of AI with Ramin Hasani, CEO of Liquid AI. Prepare to be amazed as we explore how a tiny worm's nervous system could revolutionize artificial intelligence. Hasani shares his vision for smaller, more intelligent AI that might change everything - from healthcare to scientific discovery. We'll dive into this groundbreaking technology’s exciting possibilities and essential ethical considerations. Get ready for a fascinating glimpse into a future where AI and human potential intertwine in ways we're only beginning to imagine!
 
 Recognition ( spoken word beyond LNN )
https://youtu.be/_sGm-apdxk0 

Liquid Al's Ramin Hasani on liquid neural networks
https://youtu.be/iXQRhDeOnV4?si=g1Wy32i4XxqYHLxp

Liquid Neural Networks, A New Idea
 That Allows AI To Learn Even After Training
https://www.youtube.com/watch?v=0FNkrjVIcuk 

Ramin Hasani, MIT - intro by Daniela Rus, MIT
https://www.youtube.com/watch?v=IlliqYiRhMU

Liquid Neural Nets (LNNs)
https://medium.com/@hession520/liquid-neural-nets-lnns-32ce1bfb045a 

Scientists map fruit fly brain to reveal neural circuit insights
https://phys.org/news/2024-10-scientists-fruit-fly-brain-reveal.html

Whole-brain annotation and multi-connectome cell typing of Drosophila
https://www.nature.com/articles/s41586-024-07686-5

Transcript

0:00

Ready to dive into some really cool AI stuff? Today, we're talking Liquid Neural Networks. It's all about making AI more efficient, and you won't believe this, it's inspired by worms. Yeah, you heard that right, worms. Specifically, a little guy called C. Elegance. Turns out this worm can teach us a lot about building better AI. Who knew, right? Definitely not something I'd expect. We've got research papers, expert opinions, even a company called Liquid AI, and they're all saying these LNNs, Liquid Neural Networks, are a game changer. So for those of us who don't speak worm, what makes them so special? It's all about adaptability. See, once you train a typical neural network, it's pretty much stuck. But LNNs, they're more like our brains, always learning and changing on the fly. So instead of a snapshot, it's like a constantly updating system. Bingo. And this dynamic thing, it all comes from our worm buddy, C. Elegance. I mean, it's wild. This tiny creature has a ridiculously small number of neurons, only three or two, but it can still navigate, find food, even learn new things. Pretty impressive for something so small. That is mind-blowing. Seriously makes me wonder, if a worm can do that with so little, what are we doing wrong with these massive AI models? So how does this worm-inspired AI actually work in the real world? One of the coolest examples I've seen is in self-driving cars. Researchers built an LNN, and get this, it only had 19 neurons. And it could keep a car in its lane, even with all the chaos of real world driving. Hold up, 19 neurons to drive a car, that's gotta be some kind of record. I mean, I use more brain power than that, just deciding what to have for breakfast. Right, it shows you just how efficient these things are. And get this, they could actually see what the LNN was focusing on while it drove. Like, literally visualizes decision-making process. So it wasn't just randomly turning the wheel, it was paying attention to the road, like a human driver. Exactly, they call it an attention map. And it showed that the LNN was picking up on the right things. Lane markings, edges of the road, you know, the important stuff for not crashing. That's amazing. But this whole efficiency thing, it's not just about saving energy or computer power, right? It also has huge implications for AI transparency, doesn't it? You're spot on. With these smaller, more streamlined networks, we can actually trace the information. We can understand why the AI makes certain decisions. And that, my friend, is huge for trust and accountability, especially as AI starts playing a bigger role in our lives. No doubt. It's like, would you rather blindly trust a magic box or have a clear window to see how the decisions are being made? Exactly. And that transparency is crucial, especially as AI gets used in big important things like healthcare, finance, even driving our cars. Totally agree. Okay, so this is all super interesting. But before we get too far ahead, let's talk about how these LNNs actually work. I mean, they're inspired by biology, but what's the science behind it all? We're going to get a little technical here, but I'll keep it simple. Instead of using those typical activation functions like other neural networks, LNNs use something called differential equations. Okay, now you're just using big words. All right. All right. Fair enough. Think of them like mathematical recipes that describe how things change over time. And it's these recipes that let LNNs do really cool stuff, way more complex than your average neural network. So like upgrading from a basic calculator to a supercomputer? Nailed it. LNNs aren't just crunching numbers. They're capturing the whole dynamic, the flow of information, which makes them super powerful. Okay, I'm following so far. But what does that actually allow them to do? Make better predictions. Learn faster. What's the real world impact of all this math? So these differential equations, they're like the secret sauce. What can LNNs do with them that other networks can't? They're great at this thing called extrapolation. Basically, they can make predictions about stuff they've never even seen before. Like, typical networks, they're good at spotting patterns and data. But LNNs can actually take it a step further and kind of anticipate what's going to happen next. Whoa, okay. So that's next level stuff. So like, if you were training an LNN on, say, the stock market, it could potentially see patterns that even the experts miss. Make predictions about where the market's going. Exactly. And because they're designed to constantly learn and adapt, LNNs are perfect for anything that's always changing. Financial markets, weather forecasting, you name it. Anything we're staying ahead of the curve is key. That's a game changer for sure. But let's be real, right now, it's all about these massive language models, the LLMs. How do LNNs even compare to something like, say, ChatGPT? It's everywhere these days. Yeah, it's a bit of a David and Goliath situation, right? Those LLMs, they're impressive, but they come with a cost. Huge data sets, massive computing power. Not exactly environmentally friendly. LNNs, on the other hand, they're leaner, more efficient. They could be a way to get the same results without hurting the planet. So it's about working smarter, not harder. Exactly. Remember that self-driving car demo with the 19 Mirons? They achieved that with a fraction of the resources you'd need for even a small LLM. Makes you think, right? For sure. So are we on the verge of a whole AI revolution? Could LNNs be the next big thing? It's still early days, but the potential is huge. There are already companies out there exploring LNNs for all sorts of applications. One company in particular, Liquid AI, they're doing some really interesting stuff. Okay, let's talk about them. What's Liquid AI doing differently? They're building a whole infrastructure specifically for LNNs. Think of it like a toolkit that lets developers build, train and use LNNs for pretty much any task. They're making this technology accessible to everyone, not just keeping it locked away in a lab. So it's not just a research project anymore, we're talking real world application. Oh yeah, they're teaming up with big companies, system integrators to bring LNNs to the global market. It's a huge collaborative effort and it shows you how much people believe in this technology. It's kind of wild, right? This whole revolution potentially sparked by a tiny worm. It's a good reminder that the best solutions are often the ones we least expect. Nature is full of them, we just need to know where to look. Totally. Speaking of looking in unexpected places, I read somewhere that you can actually run LNNs on these tiny computers like a Raspberry Pi. Is that for real? Remember how we talked about LNNs being super compact? Well, that means they can run on devices with very little processing power. A Raspberry Pi, it's basically a tiny computer, but with an LNN, it can do some pretty amazing stuff. Instead of relying on these huge data centers, we could have AI running locally on devices all around us. Exactly. Think about it. AI-powered sensors, smart home devices that can make decisions on their own without needing the cloud. That opens up a ton of possibilities. But as exciting as all this is, we got to be realistic, right? Powerful technology, it always comes with its own set of challenges. 100%. We can't just get caught up in the hype. Exactly. We've talked about how LNNs are more transparent, easier to understand, but does that automatically make them bias-free, or guarantee that they won't be used for bad stuff? That's the million dollar question, right? Just because we can peek into the hood, doesn't mean everything is going to be perfect. We still need to be careful, think about the ethical implications, and design these systems responsibly. Sounds like there's a lot of responsibility on the people creating this technology. Absolutely. We need to be having these conversations about the ethics of AI. It's important. This deep dive has definitely given me a lot to think about, and it seems like this is just the beginning for LNNs. There's one question that keeps popping into my head though. So we've got this potentially revolutionary technology inspired by, of all things, a worm. Makes you wonder, what other secrets are hiding in plain sight? What else can we learn from nature? It's a question that keeps me up at night, honestly. Think about it. All the amazing creatures out there, they've spent millions of years evolving these incredible solutions, right? Finding food, navigating, communicating. It's like a gold mine of ideas for AI. It's like nature's already done the hard work. We just got to figure out how it all works. I mean, imagine AI that can echolocate like a bat, or navigate like a migrating bird, or even like AI systems that work together like a colony of ants, each with its own special job. And those are just the ones we could think of, right? There's got to be countless other things out there just waiting to be discovered. And who knows, maybe by studying these natural systems, we can build AI that's not just more powerful, but also like more in tune with the world around us. It's almost poetic, isn't it? We set out to create artificial intelligence, and it leads us right back to, well, natural intelligence. It just goes to show you the power of, you know, being curious, looking for answers in unexpected places. Never know what you might find. So true. Well, we've covered a lot of ground today, from the nitty gritty of LNNs to some pretty big picture questions. One thing's for sure, this field is just getting started. For sure. And yeah, there are challenges. But overall, I'm really optimistic about the future of LNNs. I think they have the potential to, you know, not only change AI as we know it, but also to help us understand what intelligence really is. I think that's a great place to leave it. The future of AI might be closer than we think, and it might just involve a lab coat and a microscope. Thanks for joining us on this deep dive into the world of Liquid Neural Networks. Anytime. Just remember, keep asking questions, keep exploring. You never know what you'll discover. And on that note, we'll rep things up. Until next time, stay curious, everyone. Recognition, acknowledgement, payment on use, non-dilution. In the realm of trade, a liquid model awakes, economics speaks. In the realm of economics, let's embark on a journey where a liquid language model brings forth a new story. A system of harmony, dynamic and alive, where explicit and implicit knowledge truly thrives. Imagine a world where each voice holds weight, where contributions are valued no matter how great. It's a dance of ideas, a symphony of minds, where individuals shape the future, unconfined. A liquid LLM, self-reflexive and aware, captures the essence of thoughts in the air. It learns, adapts and evolves with each exchange, embracing complexity and innovative range. Contributions like seeds are planted with care, rooted in the present, but with a future to bear. For this economic model, it understands the art of acknowledging and rewarding every vital part. As ideas compound, they weave a tapestry, building upon foundations, expanding exponentially. No dilution of brilliance, no fading away. Instead, a compounding force, lighting the way. The system thrives on the wisdom we share, the discoveries we make, the burdens we bear. It's a dance of reciprocity, a flourishing dance, where innovation blooms, taking a chance. So let us embrace this Liquid LLM's embrace, creating an economy that's just and in pace, where individuals find purpose and worth, and the beauty of their contributions is given its birth. In this positive, innovation-generating realm, we harness the power to rejuvenate and overwhelm. With a Liquid LLM as our guiding light, we shape an economic future, bold and bright. Thanks for listening. Thoughts about ML architecture and social implications are most welcome.