Chapter 1: What are energy-based models and how do they differ from traditional AI?
My intelligence is not attached to any language in my brain. I think in an abstract way, not every AI has to be LLM-based. And there was a realization to me is like, not everything is related to language in this world. Like robotics is not attached to language. EBM is one part of the story. EBM attached to the LLM is another part of the story. This is the whole point of energy-based model.
You never have to guess the next word. You don't really play in a guessing game anymore. You see your energy landscape, you know where the right answer is.
Welcome, humans, to the latest episode of the Neuron Podcast. I'm Corey Knowles, editor of the Neuron, and we're joined, as always, by Grant Harvey. How are you, Grant? Looks like some big news dropped recently here from a company called Logical Intelligence, eh?
Yeah, yeah, that's right. Logical intelligence. This is some pretty huge news. So logical intelligence just announced that Yann LeCun, we're talking the Turing Award winner, former chief AI scientist at Meta, and one of the godfathers of deep learning, just joined as the founding chair of their technical research board.
And this company is building something completely different from ChatGPT and Claude. The founder, Yves Bodnia, has a wild background as well. She's a physicist with a PhD in quantum information and algebraic topology. She's published 22 papers on dark matter and quantum mechanics. And she's saying that her new Kona model represents the first credible science of AGI.
So today we're going to break down what energy-based models actually are, why they almost can't hallucinate, where they belong versus language models, and whether this is actually a path to AGI or just a really strong constraint solver. So today we're going to bring her on. Eve Bodnia, welcome to The Neuron. It's great to have you.
Thank you. Cheers. Cheers.
Excellent. Well, let's start with you. That's such an awesome background, thinking of dark matter and quantum physics. How did that background lead you to start Logical Intelligence?
Actually, this background is a little bit more complex than it sounds. I think since I was a kid, I was just naturally curious and I was trying to understand how this universe works. And I was trying to pick a field which has no limits to myself.
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Chapter 2: How do energy-based models reduce hallucinations in AI?
Hi, Dan. He was like so deep into dark matter, but also he was focused on just general like understanding how symmetries and how the symmetries work and how it's applied to describe the laws of nature. So it was not just dark matter, it was mainly the particle physics, which is one of the most fundamental areas. And I was attracted to mathematical foundations of it.
And eventually, once you expose the different areas, you start seeing the patterns. And I was like, well, I kind of like understand a little bit how particle physics works and the same mathematical methodology can be applied to like how brain works. Well, there's some frameworks, like not every framework, but some frameworks can be applied how the brain works.
And once you start questioning how the brain works, you're naturally questioning what is intelligence and how it works. And I met Michael Friedman, who was back then at Google Quantum AI, and we had collaboration at UC Santa Barbara during my PhD year. And he just shared, like, Eve, what are you doing with brain chip development space? We're also doing the same in AI space.
And we started naturally talking about it. And I'm like, well, maybe there is some fundamental laws describing intelligence just from the physics perspective rather than traditional computer science techniques. And I just went deeper and got an idea of sort of what is energy-based models are, but in my own mathematical language.
And then I spoke with our chief of AI and he's like, well, those ideas already exist in a different form and Yann LeCun is pioneering it. So I'm like, okay, I want to learn more. So it was like a natural
progression of things it was never me like imagining myself being a tech whatever person in Silicon Valley I was never I was never imagining myself doing this I was like oh I'm just gonna be a professor I'm gonna be teaching I'm just gonna go deeper publish my papers
So when the solution came for the architecture, my first instinct was like, oh, I'm just going to publish a paper and get a tenure somewhere. And then I met a friend and he's like, oh, if you're in academia, it's kind of going to be hard for you to move the same speed as AI companies. So maybe you should consider starting a company. And I was eight months pregnant by then.
And I already had my own home. I was like very nested, you know, and I'm like, no, no, no.
there's no way no way and then it just like sinks in in your brain and you just i'm like okay let's let's just do it and here we are that is so awesome yeah what a great story that's cool yeah it's cool to see how it kind of evolves naturally and it's like you're following your passion but then you're also following the science and then that leads you to this to where you are now it's cool
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Chapter 3: Why might energy-based models complement large language models?
And when I just started playing with the first LLM, it just came out from OpenAI and I opened it. I was like, wow, that looks amazing. If you ask like personal questions, it responds. And, you know, then I try to go deeper. It's like, oh, can you help me with math or like with my research? And it's still pretty good at like providing you some, you know, resources.
But then you kind of go in the links and you see like things a little bit off. And I'm like, well, I'm just wondering how... And as I was understanding a little bit more, I just realized like the way it's done, it's just taking all the data and it maps in a language space and then it tries to like predict the next word, like in the form of tokens.
And it's like, it naturally hallucinates because like sometimes words just naturally close to each other in some languages. And also it makes your intelligence language dependent. Like I speak multiple languages. Like my daughter speaks Spanish. I speak Russian. I also speak Ukrainian. I speak English. Wow. That's awesome. And when I think in general, when I think, I don't like...
my intelligence is not attached to any language in my brain. I think in an abstract way, but yet I have a chance to decode it in any language I speak. But also sometimes I don't have to speak at all. I can just move things around and I don't have to speak. And there was a realization to me that not everything is related to language in this world. Robotics is not attached to language.
if you're trying to control the circuits, the lowest logic level via filmware or hardware, you don't need to have any language in there, right? So language is a big part for us people to communicate with each other and create programs which communicate to us. But there's a lot of information around us not tied to any language. And I'm like, well,
It's a great realization, and if LLM historically were the first AI models which are tied to the language, people naturally think, oh, AI means LLM. And I'm like, I just need to understand how I could just teach people that not every AI...
is has to be LLM based and there are models out there which think in an abstract way just like your brain and you can have a chance to decode it in different form of action like it can be movement it can be software speaking like your AI model speaks to another software or it can be your AI speaks in different languages so you're supposed to have a choice
And each of these choices is not really relying on an extension you're supposed to attach to yourself. For example, Sudoku, people are asking, why Sudoku for the EBMs? Sudoku is an example when you can solve it with your brain. You don't have to... write a program to do this and you don't have to search for any patterns in any language to solve it.
The whole purpose is just to show people like, hey, there's a world around you. It's not just language. There's a lot more. There's like spatial thinking involved. And that was like the whole purpose because I realized like many people just don't see the difference. And the model we designed is exactly for that.
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Chapter 4: What practical applications can energy-based models enhance?
And then you can have a choice, like, do you want it to be drawn in a form of image or in a form of video, in a form of language? Or you might just want to continue thinking in the same way and talk to another software.
Wow. That's awesome. I've been thinking about this forever in the sense that not everyone, at a very simple level, not everyone types as their primary form of communication, right? Like an artist draws.
Exactly.
A musician will play music. There's other ways for people to communicate than just language. So it's brilliant that there is another way of doing this. And I wasn't sure if you had to tokenize everything in order to get, for example, a voice model that we know of today. Is a voice model technically tokenized text still? Is it still technically a...
Well, voice is still a language, right? It's just language in the audio form, so it probably makes sense. But you don't have to do this. The energy-based model we created, it just takes data and it maps it in its own abstract representation. We call it energy landscape, and then you see all the scenarios at the same time and
the whole science becomes how do you navigate this landscape in the fastest possible way. So in this case, we don't have any tokens at all. There's no token. It's token-free model, but the language can be attached to it. So we have a version of the EBM, which is suitable for robotics, which doesn't have any LLM layer. In this case, LLM is just like a user interface.
Because language for us, it's like I speak to you and my language has no intelligence. It's like my brain has some intelligence, but my language is empty unless it's attached to my brain. So your smart language or just any language, it's a manifestation of your intelligence. And you can mimic it, but you don't have to sometimes.
So in this case, the EBM, it has an ability to speak to people through the LLM if you want the language to be out there and LLM just like a user interface. But we also have a version which does not. So this is why I start talking about ecosystem. Like, you know, people talk about AGI. AGI is a fancy word. And at the beginning of this video or whatever, you just said, like, I see the signs of AGI.
It's not me. It's the journalists see the signs of the AGI. But... For me, naturally, I ask, what is AGI? Can we define what is AGI?
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Chapter 5: How does Eve Bodnia's background influence her work in AI?
It doesn't have to be precise prediction, but it's a part of the planning, right? So we as humans, as evolving, you have memory in your brain. Your brain has like certain parts and certain hierarchy. It communicates to each other. There's short-term memory, there's long-term memory. All of this is doing is helping you optimize for planning and prediction so you can survive.
And that's what makes us humans intelligent. And it's the same idea here, right? So there's going to be some evolution of AI. If we wanted to interact with the real world, you need to have ability to plan and predict and adopt. Because the world is a very, like, it can be pretty tough environment. Like you can have different weather for self-driving cars.
This weather can be changing or it can be like manufacturing situations when there's something unpredictable came up and you need to be able to respond to this quickly. And that's what makes it safe, right? How quickly you can respond to this changing environment, how quickly you can adopt your intelligence to it.
So to me, if you have this ability, this is maybe your general form of intelligence, which is ready to evolve. Maybe that's my definition of AGI.
I like that. Okay. Yeah, that's fair. So it's basically just how quickly you can adapt and use the information that you're taking in from all of your senses. Would that be accurate?
Maybe, but you also need to be able to preserve the task, right?
Yeah, right.
If you just adopt for no reason, there's no point. Evolution has a very well-defined task, like, hey, we're living beings, we want to survive. For different AIs, there's going to be some forms of it that's going to try to minimize the resources it's using, time for computing the things.
And also, if you give a task, if you are an AI-driven self-driving car, you want to reach your final destination, and you don't want it... randomly dropping you off in the forest just because like, oh, next word is going to be here. Sorry, we're going to go a different way.
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Chapter 6: What challenges exist in scaling energy-based models?
So you're still going to train it a little bit, right? You're going to show it some complete dataset with the answers, or you can show it some sparse data with the answers. And there, what it's going to tell you internally is going to map the shape of this energy landscape.
So the highest point on this energy landscape is going to be less probable scenarios, and the lowest is going to be highly probable scenarios. And this is very much matching sort of this theoretical physics modeling when we always want to minimize the energy.
So typically, if you're good at theoretical physics, you're going to write the Lagrangian, which is going to reflect your kinetic and potential energy in your system. And you're going to minimize this Lagrangian and derive equations of motion. And then you're going to make predictions how the model is going to behave. So in this case, it's the same situation.
We're going to find the minimal points of this energy landscape and we're going to oversee this landscape. So you always have this bird view eye and you're going to know exactly where the right answers are. And as you train in your model, you have ability to self-align it as well because sometimes your energy landscape is going to be a little bit off.
And just depending on what modeling you're using, we're using the model which has correction terms, so it can bring you back to the original landscape. And of course, it's called perturbation theory, and technical people understand what I mean. But typically, if you have a leading term and then you perturb it a little bit, you still can bring it back to where it was originally.
And it's a subject of delta, like how strong your perturbations are. And you could define the perturbation as a subject of your environment and so on. And this is what LLMs are. Unfortunately, you'll never be able to do so just because the model is different, the architecture is different. So this is the self-alignment part.
The hallucination-free part comes for the tasks where you want the answers to be precise, mathematically precise. Obviously, you cannot formally verify poetry or similar tasks. But if you want to verify your data analysis or you're generating the code and you want to make sure it's correct, here you can attach it to an external verifier like Lean4 and some people use it in other languages.
We personally use Lean4. And then you can formalize the output and have your answer kind of checked on the level of compiler.
That's awesome. I know the Sudoku test on your website, right? Yours is wicked fast compared to all the other ones. And so I wonder, so all of that's happening in split seconds, basically. And then it's also verifying it in that same time?
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Chapter 7: How do energy-based models contribute to the future of AGI?
So there's some very hardcore evolution behind, but here the idea is that if you make it right, it's not supposed to take you that many GPUs.
That's neat.
Something I noticed that really stood out to me in playing with Kona is that to watch it reminds me of, like, Diffusion. Is there a similarity or relationship there in that style? Like watching it, it's that same feel as it's going over and it looks like it's iterating over the same thing. But I understand there is a big difference, correct?
Yeah, so it can be quite deep discussion because there is so many different versions of diffusion models. And to me, to have a technical discussion, I always want to define things first so we don't go on ambiguous, right? But there's some similarities with diffusion models, obviously. And the ideas of energy-based models in general, they're not that new.
They've been out there for like 20 years and some early science even 40 years ago. The problem was nobody tried to build energy-based reasoning model. People tried to apply like energy-based techniques and modeling to like existing LLMs or image recognition, but to actually design the reasoning part itself, this is where things are really new and we just got lucky that we made it.
I love that. I appreciate the honesty too. I, I really feel like this is an exciting direction when I think of, you know, and it's hard to get out of the LLM trap in my mind when thinking of it, I've got to say too, cause I keep thinking of like nodes in agents, like the speed is insane. Uh, and as a guy who does a lot of Sudoku, it's also really impressive. Uh,
Well, Sudoku is just one of the things it's just we thought of like what's the simplest way to illustrate that there are different tasks which are not based on any language and people like know and love and can get immediately and something which can be tested with the LLMs because LLMs we compare it to advertised as LLM reasoning model, so it's meant to be extrapolating knowledge.
It learns from some games and then it's supposed to sort of extrapolate the rules for other games, and here we're not even close to this. And being able to extrapolate knowledge is one of the most crucial abilities for natural intelligence, right? So there's like, this is what LLM doesn't have.
So if you take LLM and you teach it to do some math and win IMO and all of this fancy Olympias, it's just, we have a natural assumption like, oh, this model is so smart. Let me give it some code or let me give it some other problems. in mass and it's going to solve it. And reality is not, right? It's not. It's just really good at one thing you're trained for.
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Chapter 8: What insights can we gain about AI from the discussion on energy landscapes?
And that was like a few months ago. And the natural question was like, oh, can the proof of concept be the actual, like a toy model for the model we have today? So the answer was yes, but to get there, we had to perform like a series of experiments to evaluate like what's right, what works, what doesn't.
So when the architecture was fully designed, the next step would be, oh, is it compatible with LLMs or with transformers in general? Because it's so fundamentally different. We didn't even know like it's possible to do. So the first step was to attach Transformer and try to scale it a little bit and then kind of shrink it back to the toy model version. So we successfully done so.
And then we're like, oh, can we like not even have an LLM, but the most simplest version of something related to LLM attached to Transformant, which is small and we understand. So we attach that, we also scale it and then scale it back and like, okay, that works. How about we just attach the real LLM to the EBM and see how it is as a user interface. Can it prompt the EBM in the way we want?
And the answer was yes. So we again scale it and then test it. We have a set of benchmarks, which is related to spatial thinking and hierarchical planning. So we had baselines for the smallest version of smallest version of the model, then proof of concept, then the real version of the model and kind of like compare it back and forth and it seems to be working.
So then we're like, okay, let's actually try to scale it as much as we can. And we performed a bunch of experiments, and also we have pretty decent theoretical understanding how it works, so we don't see any obstacles. But, you know, engineering can be tricky, so sometimes things work and sometimes things you need to debug. So the biggest part for me personally was to... Ah, how to say it?
So the EBM is not naturally autoregressive because there's no tokens in it. It's also non-autoregressive. So meaning it's overseeing all possible scenarios at the same time. But when you try to attach it to transformers, Transformers are very autoregressive. So you have to take this wild thing and attach to something which is thinking very sort of linearly.
One step after another, yeah.
Yeah. So you're facing a huge information loss in the middle. And then the same thing when you try to prompt using LLM the EBM. So there is also a giant reduction of the information on that layer. So we were trying to like orchestrate this layer alone, which took some time and try to see how it scales. So I think that was the biggest difficulty we faced. But now the architecture is there.
It's scalable. It's already like progressing the way we expected and a little bit even beyond. Yeah, that's where we are.
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