The Next AI Revolution Isn't About Words - It's About Understanding

Welcome to another exciting episode of the Heliox Podcast, where we explore cutting-edge developments in artificial intelligence! Join us as we dive into the fascinating world of Large Concept Models (LCMs), a revolutionary approach to AI that aims to understand language at a deeper, more conceptual level. Unlike traditional language models that process text word by word, LCMs work with entire sentences and ideas, potentially transforming how AI interacts with languages across the globe. Whether you're an AI enthusiast or just curious about the future of technology, this episode offers an engaging look at how these models could change everything from translation to creative writing. Get ready for an illuminating discussion that breaks down complex ideas into digestible insights while exploring the incredible possibilities that lie ahead in AI development.

Large Concept Models:
Language Modeling in a Sentence Representation Space
https://arxiv.org/pdf/2412.08821

by LCM The · 2024 — The Large Concept Model is trained to perform autoregressive sentence prediction in an embedding space.
We explore multiple approaches
https://github.com/facebookresearch/large_concept_model 

Experts are STUNNED! Meta's NEW LLM Architecture is a GAME-CHANGER!
https://youtu.be/1Z1vKdrmpj4?si=lWh_ZfvKyQ0AdrdR

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Transcript

0:24

All right, so get this, we're diving into AI that thinks in concepts. Oh, wow. Just words. Okay. It's called large concept models or LCMs. Okay. And it could seriously change how we interact with language. Yeah. Like forever. You ready for this? I am fascinated by this idea of AI moving beyond just predicting the next word, like a supercharged auto-complete, you know what I mean? Yeah. We're talking about AI that actually might grasp the meaning behind the words. Whoa. Planning and reasoning at like a whole new level. Okay, hold on. Yeah. Before my brain melts, let's start with the basics. Sure. What exactly are these large concept models and what makes them different from say, chat GPT? Okay, so think of it like this. Okay. Most AI we see today, like those large language models, they process language bit by bit. Okay. One word or even smaller chunks at a time. It's like trying to understand a story by only looking at individual letters. Yeah, good luck with that, you'd miss the whole plot. Exactly. You wouldn't get you. LCMs try to do something radically different. Okay. They process language in whole sentences. Capturing the complete thought, like reading a sentence, not just deciphering letters. Okay, that's starting to make sense, but how do they actually do that? Right. How do you teach an AI to think in complete sentences? So that's where things get really interesting. LCMs use these things called embeddings. Okay. Imagine each sentence having a unique fingerprint that captures its meaning. That's basically what an embedding is. But instead of shuffling words around, they're working with these abstract fingerprints. Exactly. That represent a whole idea. Precisely, LCMs manipulate these embeddings to generate new ones, and then those are decoded back into actual text. Wow. And get this, they're using an existing system called Sonar. Sonar. That works with a mind boggling 200 languages. 200 languages, seriously, that's more than I can count on my fingers and toes. It really is a game changer. And that multilingual aspect is just one reason why everyone's buzzing about LCMs. Okay, color me intrigued, but I need to know, why are LCMs such a big deal in the AI world? Okay. What's all the hype about? So while impressive current language AI, those LLMs have some major limitations. Like what? Well, they're mainly trained on English text. Okay. So truly understanding and creating in other languages is a struggle. So even with all the talk about AI breaking down language barriers, we're still stuck in an English centric world. For now, yes. And there's the issue of long texts. Current LLMs struggle to grasp really long documents effectively. Okay. Their attention spans are pretty limited. Makes sense if they're focusing on tiny bits of language at a time. Right. It's like trying to read a novel one letter at a time. Yeah. You'd never get through it. Exactly. But perhaps the most glaring limitation is the lack of true reasoning abilities. They can string words together in a way that sounds impressive. Right. But they don't really get the concepts and relationships. So they're more like parrots mimicking human speech, not actual thinkers engaging with ideas. That's a fantastic analogy. And that's where LCMs come in by thinking in concepts. Okay. They have the potential to overcome these limitations. They can handle many languages, tackle those long documents. Right. And possibly even unlock genuine AI reasoning. We're talking about a whole new ballgame. Okay. Now we're getting to the good stuff. All right. If we zoom out a bit. Yeah. What kind of impact could this have on the real world? The potential is vast. Okay. Imagine more accurate and nuanced translation, even for languages spoken by relatively few people. Okay. Think about AI that can analyze dense legal documents or scientific papers and pull out the key insights or even AI that can understand and create different forms of art. Like what? Like music or poetry. Whoa, hold up. Yeah. I need a minute to process all of that. Sure. That's a lot to take in. But before we get carried away with the possibilities. Yeah. Let's get into the nitty gritty. How are these LCMs actually built and trained? It's definitely complex. Researchers are exploring several approaches. Okay. All pretty technical. But one standout is their use of diffusion models. Diffusion models. You might recognize that from those amazing image generation AIs like DALI. Wait, so they're taking what works for creating images and applying it to language? That's wild. But how does that even work? So instead of starting with the jumble of pixels and refining them into a coherent image. Right. They start with random noise in that embedding space we talked about. Then they gradually refine that noise until it becomes a meaningful representation of a sentence. It's like sculpting a thought out of clay. Yeah. You start with a rough shape and gradually refine it until it expresses the idea perfectly. That's the idea. But instead of clay, they're working with these abstract concept embeddings. This is getting pretty deep even for a deep dive. I know, right? Do they run into any snags along the way? Absolutely. One challenge they highlight is sentence segmentation. Sentence segmentation. How do you accurately split a text into sentences? Yeah. Especially when you have different languages and messy real world data. Yeah. Punctuation isn't always reliable. Right. And some sentences can be ridiculously long and complex. It's like trying to decipher a long-winded email. Sometimes it feels like solving a cryptic puzzle. Precisely. And the quality of that segmentation directly impacts how well Sonar can encode the sentences into those fingerprint embeddings. If you feed it garbage, you'll get garbage out. Makes sense. Yeah. So how do they know if the LCMs were actually doing a good job? Right. How do you grade an AI? I was trying to think. They used a range of metrics. Some familiar and some pretty specialized. Okay. They want to make sure the AI isn't just spitting out grammatically correct sentences. Right. But actually capturing the meaning and context of the conversation, like a real person would. Right. You don't want an AI that's just a walking, talking grammar textbook. So did they scale these LCMs up to any significant size? They did. They eventually built a model with a whopping 7 billion parameters. Wow. That's getting into serious, large language model territory. Yeah. And they tested it on tasks like summarization. Okay. And even invented a new task called summary expansion. Summary expansion. Where the model takes a summary- Okay. And tries to flesh it out into a longer, more detailed text. Wow. So they're not just replicating existing tasks. They're pushing the boundaries of what AI can do with language. That's impressive. It is. What were the results like? Did the LCMs outperform the current state of the art? Well, that's where things get a little bit more complicated. So while the LCMs showed some real promise- Okay. Particularly in creating coherent text. Mm-hmm. You know, the current champs, those top performing LLMs like LLAMA- Right. Still edged them out on those specific tasks. Ah, so the revolution isn't quite here yet. Right. But it sounds like they're on the right track. Absolutely. And even though they haven't taken the crown- Okay. The researchers highlighted some truly exciting aspects of LCMs- Oh. That hint at a very bright future. Well, now you've really piqued my curiosity. Okay. What are some of the limitations they need to tackle? Yeah. And where do they see this technology heading? So one of the biggest hurdles right now is their reliance on that fixed embedding space, Sonar. Sure. I know. It's fantastic for supporting so many languages. Right. But it wasn't designed for this kind of deep language understanding. So it's like trying to run a marathon in shoes that are two sizes too small. Exactly. They need something specifically designed for LCMs. Yes. And figuring out how to define concepts consistently across all those different languages. Right. That's another massive challenge. Yep. What might be a clear concept in English could be expressed very differently in Mandarin or Swahili. That makes sense. Language is full of nuances and cultural context. Right. Capturing that in an AI- Yeah. Is a tall order. It is a tall order. But what about the future? What are the researchers most excited about? Well, one particularly fascinating avenue they're exploring- Yeah. Is teaching AI to plan the structure of a text before it starts writing. Oh, that's interesting. Yeah. So instead of just spitting out sentences one after another- Yeah. It would have a higher level understanding of the message it wants to convey. Precisely. They believe this could lead to much more coherent and logical text. Okay. Especially for longer pieces. It's like giving the AI a compass to navigate the vast sea of language. I love that analogy. Without it, it's just drifting aimlessly. Right. So to test this idea- Right. They actually built a large planning concept model or LPCM for short. LPCM, okay. And compared it to a regular LCM. I'm on the edge of my seat. What happened? Did the planning actually improve things? Well, they found that the LTCM- Yeah. Created significantly more coherent text than the regular LCM. Wow. It seems that having that high level plan really helped guide the model towards generating text that was more logical and well-structured. That's incredible. It's like teaching the AI to think before it speaks. Right. Or in this case, writes. Exactly. And it just goes to show the power of incorporating these higher level cognitive processes into AI models. It's not just about mimicking human language. It's about replicating how our minds actually work. This makes me think about the future of creative writing. Yeah. Could AI one day write novels or screenplays that aren't just grammatically correct- Right. But deeply engaging and thought provoking? It's definitely a possibility and the implications go far beyond storytelling. Like what? Imagine AI that can generate detailed scientific reports, compelling legal arguments- Mm-hmm. Or even philosophical treatises. Oh, slow down. I need to take my breath. This is all so mind boggling. I know, right? But before we get too swept up in the excitement- Yeah. It's important to remember that this research is still in its early stages. Yeah. There are many hurdles to overcome before we see this kind of AI in our everyday lives. You're absolutely right. The researchers themselves acknowledge that there are limitations. One of the biggest, as we've already touched on, is the reliance on a fixed embedding space like sonar. Right. It seems like they're constantly running up against the walls of that space. They need an embedding space that's tailor made for LCMs, something more robust and less fragile. Right. And they still need to crack the code on defining those concepts consistently across different languages and forms of communication. And let's not forget the sheer computing power needed to train and run these massive models. We're talking billions of parameters. Mm-hmm. And potentially trillions of data points. That's right. This kind of cutting edge AI research requires serious computing power. Yeah. Which isn't something everyone has access to. It makes you wonder about the future of AI development. Yeah. Will it become increasingly concentrated in the hands of a few big tech companies that have the resources to build these giant models? It's a valid concern and it highlights the importance of open source projects and collaborative research efforts. Right. We need to make sure that access to these powerful technologies isn't restricted to just a select few. I couldn't agree more, but let's get back to the exciting stuff. Okay. What are some of the other directions that LCM research could take in the future? Well, one area they're looking at is exploring different sizes of concepts. Okay. Right now, the focus is mainly on sentences. Yeah. But what about paragraphs, chapters, or even whole books? Mm-hmm. Could we build models that can reason at those levels? That's a wild thought. It's like building a hierarchy of ideas. Yeah. Where each level builds on the one before it. Exactly. And they're also talking about incorporating more diverse data sources. Okay. Including not just text, but also images, audio, and even video. So we're talking about AI that can truly understand and generate meaning. Yeah. Across all different forms of media. That's amazing. Exactly. Imagine an AI that can watch a movie. Oh, yeah. And then write a detailed review, compose a soundtrack, or even create a 3D model of the characters. My mind is officially blown. I know. It's pretty amazing stuff. Yeah. But let's come back down to Earth for a moment. Okay. While all of these possibilities are super exciting, it's important to remember that AI is a tool. It is. And like any tool, it can be used for good or for bad. I completely agree. As we create these increasingly powerful AI systems, we need to be aware of their potential impact on society. Yeah. We need to think about responsible development. Right. And make sure that AI is used to benefit humanity, not the other way around. Well said. So where does that leave us in our deep dive into large concept models? I think it's safe to say that we've only just scratched the surface. Yeah. This is a field that's overflowing with potential, but it's also full of challenges. And it's moving at lightning speed. What we've talked about today might be old news in a few months. It could be, yeah. Which is both exciting and a little daunting. It is. It's a reminder that we're living in a time of incredible technological change, and it's up to all of us to make sure that change benefits humanity as a whole. But let's get back to the nuts and bolts of LCMs for a moment. We've talked a lot about the theory, but how are these models actually performing in the real world? Yeah. That's a good question. Well, as I mentioned before, they're not quite at the same level as those top performing LLMs like LLAMA just yet, but they are showing some really promising results, especially when it comes to creating coherent text and handling those long pieces of writing. And one of the most exciting things, in my opinion, is their potential for zero-shot generalization to other languages. Absolutely. The fact that they can leverage Sonar's multilingual capabilities to process text in languages they haven't specifically been trained on is a huge step forward. It's like giving the AI a universal translator. Suddenly, it can communicate with people from all over the world, regardless of their native tongue. It's a powerful vision, and the researchers actually demonstrated this by testing their LCM on a massive multilingual new summarization dataset called ExcelSum, which covers 45 languages. OK, you've got my attention. Yeah. What happened? Did the LCM live up to the hype? It actually exceeded expectations. Wow. The LCM significantly outperformed LLAMA, which officially supports eight languages on English summarization tasks. OK. And it even generalized well to many languages with less available data. Like what? Like Southern Pashto, Burmese, and Welsh. That's amazing. So even though it's still early days, we're already seeing evidence of LCM's potential to revolutionize how we communicate across languages. It's really exciting. It offers a glimpse into a future where language is no longer a barrier to understanding and collaboration. That's a beautiful thought. But let's keep our feet on the ground. We need to be realistic about the challenges ahead. Of course, building AI that can truly understand and reason like humans is a monumental task. Yeah. But the research we've discussed today shows that we are making significant progress. And the fact that they're open sourcing their code... Yes....shows their commitment to advancing the field as a whole. It's a collaborative effort. Right. And that gives me a lot of hope. Absolutely. By sharing their knowledge and tools. Yeah. They're empowering others to build on their work and push the boundaries even further. I'm already buzzing with ideas for future deep dives. Oh, really? We could explore the ethical considerations of LCM's. Okay. Delve into the technical details of how they're built. Or even interview some of the researchers at the forefront of this revolution. I'm game for all of that. The possibilities are endless, which is what makes this field so captivating. It's incredible to think that we might be on the cusp of creating like a new kind of intelligence. It really is. One that can understand and generate meaning just like we do. Yeah. It kind of makes you question what intelligence even means, right? It really does. It blurs the lines if a machine can plan and reason and create coherent text that conveys complex ideas. How is that fundamentally different from what we humans do? It's a question that has puzzled philosophers and AI researchers for decades. It's both exhilarating and a bit unnerving, isn't it? Yeah. It is. To think that we might be creating something that rivals our own cognitive abilities. It certainly challenges our understanding of our place in the world. But instead of viewing AI as a threat, I think we can see it as an incredible opportunity. An opportunity. What do you mean? An opportunity to learn more about ourselves by studying how these AI models like LCM's process language and create meaning. Right. We can gain a deeper understanding of how our own minds work. It's like holding up a mirror to our own cognitive processes. That's a fascinating perspective. So instead of fearing AI, we can use it as a tool for self-discovery. Exactly. And who knows what other breakthroughs might come from this research. Maybe we'll discover new ways to enhance human learning, develop more effective treatments for language disorders, or even invent entirely new forms of art and communication. Now you're making me really excited about the future. But let's bring it back down to Earth for a moment. OK. For our listener who's been on this journey with us, what's the one key takeaway they should remember from this deep dive into large concept models? OK. If I had to boil it down to a single point, it would be this. OK. The future of language AI isn't just about creating grammatically perfect text. It's about understanding and generating meaning. It's about building AI that can think reason and create in ways we're only just beginning to grasp. And that's a future I'm definitely looking forward to. Thanks for joining us on this deep dive into the fascinating world of large concept models. We've only just begun to explore the vast potential, but it's clear that we're on the verge of something truly revolutionary.