The AI Model Built for What LLMs Can't Do

Can you define EBM for us? EBMs are naturally non autoregressive. There are no sequences of tokens and that's what makes it fundamentally different. Like imagine you're trying to navigate the map and you have a left brain. To navigate, you're sort of allowed to choose one direction of the time and sometimes you take The wrong turns just because you hallucinate, like there might be a hole in the road, and you're just gonna fall. And you might see this hole, but you cannot turn back.

Because you're auto-regressive LLM. EBM gonna have the bird view all the time. So if you see there's a hole, you're gonna choose a different route. Eve, welcome to the show. Hi, thanks for having me.

Great to have you on. For people who don't know, you are the founder and CEO of Logical Intelligence. Tell us what Logical Intelligence does. So logical intelligence does uh a few things. First of all, we see ourselves as a foundational AI company. So we work in both with EBMs and L LMs. So everything the build in house we prototyped on L M initially and we building EBM at the same time and that sort of gets plugged in in the long term.

Um we focused on correctness of software and hardware as a product. Um because I believe there is a lot of issues with being placed in mission critical systems today? Like, you know, can we do the code gen? Can we do the chip design? And the answer is yes, yeah, people use L LMs today. Um, but very few actually questioning of like

how this results uh actually like correct. Does it make sense what it produce? And it seems like there's a big gap on market today, um, having deterministic AI, verifiable AI, so we trying to fill that gap.

I I my the the place my my brain goes first is why does correctness or whether something makes sense, why does that matter if it works? Um, actually let me ask you a question back. So speaking of correctness I don't know. Well, imagine there is a guy driving a car and you are in that car and that car is an L L M. And someone tells you like, mm, you know, twenty percent of the time it's gonna hallucinate and you might end up like in in like wrong place. Uh how would you feel about it? Well...

I I think it in my in my case I'd be like, Wow, that's kinda interesting. I'm I'm curious where it takes me. Um Oh, okay. Let me give you another example. Yeah, sure. The plane. How about the plane? You take a plane from SF to New York and someone says, you know, like twenty percent of the time it might just like the next word not gonna match and it's gonna go down. So how would you feel about it?

Yeah, my my my my feeling about that is planes are currently run very well by deterministic systems, so I don't know why I would need an AI for that. Mm, I feel like we just cannot avoid AI anywhere. Like next ten years people are gonna try to place AI everywhere, automate systems with AI and you know Technically you might not need we we survived somewhat without AI up to this moment, but now it's just like a next step of evolution that people just want AI everywhere.

Like for the banking you don't need AI initially, but we learn it's really helpful to automate like certain processes and decision making. and it's gonna save us a lot of time and allow us space to be creative instead of like debugging and fixing things. Um so I just feel like it's unavoidable future. I think maybe what I'm getting at is um what am I getting at?

It seems like if you want a guarantee of certainty, using um the only way to sort of guarantee certainty is to use something that you can express in code or logic. That's a part of it. So the certainty comes from internal verifiers and external verifiers, at least for us. Um so for example if you take L L M

Um obviously it's a language-based model and architecture doesn't allow you to do internal verifiers. So you you like it's like a black box for you. You don't have access to what's inside until it's all processed. Um, but you have access to the output and many people and companies sort of take LLM, uh train it for certain tasks. And if it requires logic they attach texturing verifiers to it, such as languages like Lean Four, which is a proof uh I mean machine verifiable language, proof language.

um which allows you to check this output using mathematical uh framework. Um, however, you know, it doesn't solve the problem of things being um so expensive because what expensive is your architecture, which is still playing a guessing game out here. And even if you attach external verifier, even you fine tune this L L M specifically for the task you're trying to create, you're still not solving the problems of Tokens being expensive, it takes compute for you to play a guessing game.

So this problem is solved by the EBMs, but we're talking about L LLMs for now. Um so here we have the situation when there's right internal absence of verifier but there's external one. Um so now about the EBMs. EBMs don't have tokens. It's token free model. There's no guessing game of this kind. So essentially you could oversee all the possible scenarios. Can you define EBM for us?

Yeah, I'll define in a second. So for now just think of it as something which doesn't play a guessing game and something which has architecture which is essentially allow you to self align itself as it processing the information. and it's no longer a black box for you. So as it's performing you can open it any time during the training and you could see what's happening in there. So you cannot do this with LLMs. Just the nature of architecture is different.

So you have for verification tasks, you have this notion of self-alignment because of the EBM architecture and the absence of token makes it cheap, but also you have external verifier on top of it. So you have verification sort of on both sides, inside and outside. Hopefully that makes sense.

I think so. Let me let me play it back to you and you tell me if if if I'm getting you. So basically I think what you're saying is we're living in this in this world, which is really cool with L O Ms, which is we can generate lots of output with them. And the output is um uh really useful for for a lot of different things. But in order to tell if the output is right.

The the best we can do is sort of guess and check. We generate the output and then, for example, if it's code, then we go and check the t check the code with integration tests or manual tests or whatever just to see if it works. And that totally works, but it is expensive and time consuming. Um and uh one of the problems is it's very hard for us to know, okay, how did the LLM get to this answer? We can't like go look inside of it.

Exactly. And I think what you're saying is there are other types of models that are a little bit more inspectable. And that give us a sense before we even try the um before we even try the output to understand does this work, does the output work? We can get a sense from the model by looking at its internals. s sort of like how good is this solution? How good does this model think this solution is? Um

uh and it's sort of like being able to ask someone like, Are you sure about this? Like how how how good is this before you like go check their work? And a language model can answer that question. But a language model's answers are working at a at a different level when it answers that question than than this models, uh, than than these EMB models are working. And the the the answers from EMB models are more likely to be correct.

Mm-hmm. Yeah, so you always have an opportunity to see what's inside with the E BN. And you control the training. Yeah, I'm sorry. Yeah. In other words. So the the EBMs you control the training. It's no longer a black box for you. You control sort of how the training goes. Well, you do some extent with the L LMs, but you need to wait until the training is done. before like you actually go and like see what's inside. In here you could do like in real time.

Yeah, and also you can attach the same external verifiers which works for LLMs. So you have sort of double double verification things. Um yeah, so you ask me what is a EBM I just wanna give like a historical note um because I feel like there's so many terms today and just people throwing those terms without defining it.

Um so EBM just simply means energy based model. What is energy? Energy based um it comes from physics. It's a very popular term when they're trying to minimize the energy. If you're doing theoretical physics, like your full time job is just to write Lagrangians which sort of correspond to uh terms associated with the energy in your system, like hey, this is my kinetic energy, this is my potential energy, and then you're trying to derive equations of motion of it.

And the way you derive the equations of motions is you're doing the minimization. So that's pretty much how whole theoretical physics works. Hey, just start with the energy terms, then you minimize this energy and you derive equations of motions and equations of motions gonna give you a conservation laws. So you're gonna know exactly what are your laws about your system. And this principle is fundamental principle. Like everything wants to minimize energy around us.

Um yeah, so like even us we talking to each other, we sitting on the chairs, we're not like jumping and running around because it's a natural state when we minimize the energy. So We're just using this minimization energy principle as AI is processing information in high level terms.

So the term energy-based minimization doesn't really mean anything, specifically to AI. It's just the whole like idea of like, hey, let's take some energy and try to minimize it and discover what's the laws about it. Um so our model is called official name of that model, uh even though we call it Kona just because we're like big fans of coffee culture and Kona is one of our favorite kinds.

So we decided to start with that. Um the formal name of the model is called energy based reasoning model with latent variables. And I'm gonna like describe exactly what those words mean. So we already understand what the energy minimization is. Okay. Yeah. It it's just for now think of it as just something which minimizes the energy. It it means this AI architecture has a framework which allows you to construct the energy function of your system and minimize

I get it. I I just think that um so I just want to make sure for for people listening they understand what it means to minimize energy, what what energy is and what it means to minimize it. So I'm curious, um give me a give me a uh uh Tell me if this concrete example is is about what you're like sort of a qu like close to what you're talking about. So if I'm going to let's say uh I'm gonna go lie on on the couch behind me.

And um and I'm trying to f I'm trying to predict or understand how is my body going to be lying on that couch, um, given the laws of gravity. Uh the the couch is uneven, my body's uneven, and so I'm trying to like sort of understand the fit of like how my body is gonna end up settling onto that couch. Um Mm-hmm. I'm gonna end up settling onto the couch in a way that minimizes energy. So there's gonna be like a a good fit between my body and the couch.

versus like me being sort of like jerky like this and and having lots of different spaces. Is that the sort of the sort of energy minim minimization that you're talking about? Yeah, yeah, you just you it's all about your body finding The most comfortable configuration for you was the which gonna correspond to

the like the lowest potential of your body. I would even tell the like even more high level example of this, like You know, um, you then, you just like imagine you're tired, you like done thousands of podcasts and you just came home and someone is asking, like, okay Dan is a variable here. Uh let's try to figure out what's his equations of motion in the house and what where he's gonna most likely to end up. So you're probably gonna end up on a couch.

With like a nice show and uh probably some drink. Yeah. Um yeah, so that's gonna be a a law. Like okay, when then it's tired, he's gonna go and sit on the couch and just relax. Um, but to get there we're gonna look at all your possible states, like you washing the dishes, you know, walking around the house. So those are gonna be different states, but your most probable scenario is gonna be on the couch.

So essentially all of this picture can be mapped into something we call energy landscape when w the the high it's gonna have like

It's gonna look like a map. So you're gonna have highest points, you're gonna have lowest points, the highest points we can associate less probable scenarios, so probably if you're tired you're not gonna dance around, although I don't know. But, you know, typically people assume that if you're tired you're probably gonna want to relax. So that's gonna be the lowest point. And as we trying to figure out where you are during the training, we're gonna observe you multiple times.

Um during different days and um you know, how much of the workload you have it's gonna be a variable. Your internal state is gonna be a variable, how your body feels. And eventually we're gonna train this landscape to be, you know, based on what we see in real world, right? The lowest point is gonna be you on a couch. We've all been there. You're sitting in an important meeting.

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I I that makes total sense. Now I want to relate this to LLMs for a second, because you can imagine that there's an LLM that's trained to predict where I end up after a long day of podcasts. Um and you can imagine it probably would also end up predicting that I would end up on a couch. What are the differences in the ways that it makes those predictions that make um energy based models better for this scenario?

Okay. Uh that's a good thought exercise. Um so okay, now you are L L M. So they okay, let's talk about back to EBM because what we described is very natural about EBMs are all about constructing energy landscapes and how we navigate those energy landscapes. And energy landscapes is sort of the maps of your states based on the data we observe.

So in your case, we're just gonna look at you in all possible scenarios, all of this possible scenario is gonna map into energy landscape, highest point, uh less probable scenario, lowest point is more probable. So Yeah, yeah, yeah. Um there might be some other additional low points. Like sometimes you might go to gym which actually kinda Alright, I'm sick of it.

I feel tired and you won't go to gym. So it's gonna be, you know, lowest points compared to everything else, but some of them are gonna be lower. Yeah. Um yeah, so that's the situation. So this is how energy based model actually we think. It just takes the data and map it directly to this energy landscape and then we use certain algorithms to navigate this.

But there are different kinds of energy based models today, so I'm gonna talk about it a bit a little bit later. But the whole idea is just, hey, let's map it into the structure and navigate the structure. As you see, as we like map into this, there are no tokens, we don't predict any tokens and so on. So that's already a crucial difference.

Uh how would LLM think? Um LLM, yes it's gonna rely on the training data and it's gonna be a lot of training data. Like a lot and a lot of observations of how you behave and to figure out where you would end up it's gonna be attached to probabilities of your next talk.

if that makes sense. And those tokens gonna come from words. And what usually bothers me about LLMs, it's intelligence which is language dependent. like our brains we are intelligent. I'm relatively intelligent. So like I speak different languages and none of my thoughts processes really depend on any language. Like I could just think in an abstract way and then I speak different languages and decode the information and the channels.

And with the L L Ms is like if you're searching for the next token in certain words, like intelligent process I I would say the information processes in French are gonna be different from what's in English. Just because like words naturally gonna be end up next in to each other.

So see what I'm saying? It's like and then we have so many languages in the entire world and you have so many LLMs trained on different languages, so you're gonna end up reasoning you're gonna end up having reasoning processes different for each of the language, which feels really wrong. So in this case, observing you walking around the house has nothing to do with language. It's a pure visual spatial reasoning task, just looking at your body navigating the space-time and geometry of your house.

So we need to map that information in the language space, find the right words and embeddings. And then we start to Associating those tokens with the probabilities based on what the data we see from you. So we trying to map something absolutely has nothing to do with language into language space and think about it in that space. Which feels really wrong.

Okay. And I don't know, I I just realizing that for many people It's counterintuitive, just because LMS is the first form of AI we sort of know and it's the most popular form of AI today. For for many people it's by default like, Oh yeah, we're just gonna use language to navigate the world, to drive a car.

But and I'm like every time I'm speaking I'm like, Well, let's wake up, let's like actually see when you drive a car, when you walk around your house, how much language you actually use. Are you trying to predict next word as you navigate yourself around the house? Probably not. you just use your visual data, your state of the body and you know, you just move your body, right, without speaking.

Uh there's a lot here. I I'm really into this conversation. So I wanna start with A, it seems absolutely right to me that um there are many different ways in which we um process information or many different ways in which an intelligence can occur and and only a few of them are verbal. But there's

There's certain things that come up for me when I when I think of this. One is Language models happen to work with languages as their primary way of working, but really they just work with sequences of tokens that have um weak correlations, many thousands of weak correlations between each token that helps us to know which comes next.

So even though it might be unintuitive to model my behavior inside of my apartment with like specifically with language, although I'm gonna I think there is some interesting things there that it might be related to language. we we could model it as just like a sequence of movements, right? That were um uh one one movement is weakly correlated to the next one that we sort of have a trajectory of movements that tell us where where I'm going. Why is that not a good way to model things?

It's a good way to model things and you don't need L L M for it. You need a form of AI which is not attached to language. It can be compatible with language if you want it to. And that's what our model is about. Right, I guess what I'm saying is um uh f f forgetting about the the language part of it, just like modeling my movements as this string of uh correlated events, like an event stream where each token is like one next thing I do.

Yeah, you c you can do it and people do it today, right? People even do image recognition using language models. Uh you could be really creative but it's like that's what makes it expensive and super slow because you're trying to play a guessing game what my next token could be and this is what makes it extremely expensive. like, you could do it but you don't have to do it. You just can use different architecture which is more suitable for non language related tasks such as spatial reasoning.

Or applied engineering is another example of spatial reasoning, like when you build a bridge you don't go to literature department, you go to engineering school and learn formal methods, right? So here we are trying to use literature department everywhere and I'm like, Hey, we don't have to. There are EBMs, there are also other forms of AI which you can experiment with and you don't have to do everything through language.

It's a matter of like it's it's right, it's like energy based minimization principle when it comes to your resources. If you have infinite money and you don't care about the timescale, sure, you can do everything. You can attach it to language.

you can attach it to I don't know your cat movement around the house and connect it to the cat movement and you know where cat goes and your next talking goes and we decide where you gonna go. You could be really creative but if you wanna minimize your resources and you don't have opportunity to wait

Like for example if your AI controls the circuits, you probably cannot wait even even a second. It's all milliseconds, microseconds. So it just this form of AI is not suitable for those types of So basically, if I'm understanding this right, if I'm spending tons and tons of tokens and I'm looking for a more efficient, more direct way to

um predict some some of these solutions to these these problems, an energy based model is gonna get me there faster than uh, you know, modeling it with tokens. Is there is it also able to do it with less training data? Um, yes, actually the beauty of the m of the EBMs is it's really good at working with sparse data.

Because like, you know, there's evolutions of like traditional EBMs which were applied for the L L Ms, then there was diffusion models. And diffusion models came from the fact that sometimes you don't have enough data to train the models or your data is just data set is incomplete. So there are ways to reconstruct those energy landscapes by injecting certain noise and changing the navigation strategies.

Um, so that's what the diffusions models were about and the E BRM with latent variables is just like, hey, on top of the diffusion stuff, we also understand the data. We not just take in any data but we also understand why the data looks the way they are. So that understanding goes to the latent variable. just like latent space in your brain sort of understands the world around you and keeping you on top of your tasks and ha allows you to like predict and plan. So it's the same idea here.

So now we got to the latent variable part of our part of it. So I I would love when you when you use the word understanding, I f I think that must mean something very specific to you. Can you help me understand that and how it relates to latent variables and what those are?

Yeah, so that's also back to your question was that like how L L Ms are different with from the those kind of EBMs we're creating. Um, L L Ms don't understand the data. It's just you feed a lot of data into it and it's sort of like hey I I got it. Like okay, I know what's the most probable scenario here and here we are.

However here, EBM, you can feed a lot of data. It's not just gonna look at like, hey, I I see the biggest pattern here. It's gonna try to understand the pattern. And that understanding that knowledge is gonna go to latent variables. So what is understanding about data?

Like it's just basic knowledge about the world. Basic rules about the world. Like i if there is a couch behind Dan, it's probably because he likes to sit on it or be because he likes it on the background. So there are little rules you can guess. Yeah.

And then then there's you can s try to create those kind of rules like for everything, right? For you navigating your apartment. There are little rules like, you know, there's a kitchen for cooking, there's I don't know, bathroom, there's sofa, there's your bed. So that that understanding allows you to have your own mental world model for your brain.

um which helps you to understand your environment and if something changes in your environment you understand the rules. Like if somebody brings you a different couch, different shape, you're still gonna know what to do with it. So that's an example of how you can infer what to do with something new based on what you already know. So with with people it's kinda comes natural because of their evolution and so on, but with AI we need to teach it. So we need to like mimic that evolution.

And what latent variables allow you to have here is like, hey, let's look at the data, but let's also try to understand the data. Let's look at you know, if you do it with numerical analysis, we're gonna look at all possible correlation functions. And the model is gonna be creative. It's gonna try to figure out what's the total state of the energy and minimize and figure out the the the laws about your data. Um but uh there are so many creative ways how you can infer those rules.

What is a so is is a latent variable equivalent to a rule in this scenario? Like uh if there's a couch in my apartment, I sit in it. It's not equivalent to a rule, but it's equivalent to something which holds the knowledge about the rules of your data. It's like a knowledge storage. So it it it it has many rules in it? Yeah, you could have minerals. So one latent variable has many different rules. It's just like a knowledge data set, essentially. About your data. Is it an explicit data set?

As in like does it have key value pairs of rules or is it a uh It's it's in a form of energy landscape. It's just another energy landscape you're you're gonna navigate. So essentially we take the data We look at the data, we construct some sort of Structure.

for AI to deal with the data, so it can start learning the rules about the data. And once it understands the rules, it stores its knowledge in the latent variables, in the form of energy landscape, and then we navigate that energy landscape later. Interesting. And like could it for example Explicitly write out for me, theoretically, explicitly write out for me, here are all the rules that I know, or uh is it It it stores all it stores all of them in in this energy landscape, but

Mm. Yeah, we we can access that. We can access that. And that's what makes it what that's what like EBM potentially makes it um powerful for data analysis because data analysis is all about searching for patterns and rules about your data. So

And it's it's something where language is not gonna be helpful to you. If you try to attach the rules about your data and those data is like numbers and some relationships and functions to like American English and words in American English and then you try to search for the next word it kinda like you're losing a lot of information. So in this case you have an opportunity just directly work with the data and understand the data.

I think one of the one of the things I'm trying to understand is when I hear rules about the world and how things relate to each other, I think of symbolic AI. And I'm wonder and and and obviously th those approaches ended up being pretty brittle and requiring too much compute and stuff like that. Um And I'm wondering how an energy landscape that acts as a that's is a uh that stores a bunch of rules about the world doesn't fall into the same problem.

Mm, well,'cause I guess we avoid to tokenization in this case. We just map it directly into different data structure. So see EBMs are naturally non auto regressive. Like there are no sequences of tokens and That's what makes it fundamentally different. So essentially, I don't know if it helps. There could be another analogy. Like you're trying to navigate

uh the maze and you are L L M person, so you have L L M brain. Um, well maybe maze is not a good example. Like imagine you're trying to navigate, I don't know, the map of San Francisco. So and you have a left brain, so you're like, Okay, I'm on Mission Bay, let me turn to embarcadera. So you cannot you cannot choose So essentially you you're just forced to choose one direction at a time. So you like choose to walk embarcadera and you're just gonna keep walking and walking.

You can if you want to turn you just need to choose one direction at a time And imagine you like trying to get to the Bay Bridge from like I don't know, King Street. Like, you know, it's typically twenty minutes walk, but depending how you walk. So to navigate there, you're sort of allowed to choose one direction at the time, you don't see any other options, you like have tunnel vision and you just

Kinda keep walking, walking one decision at a time. And sometimes you take the wrong turns, just because you hallucinate, you know, some words just natur naturally next to each other and it doesn't allow you to turn right when you want it, turn you left. And then you just keep wondering and wondering until you try to reach the bay bridge.

And the roads you take in might never take you there. Like there might be a hole in the road and you're just gonna fall. But you and you might see this hole, but you cannot turn back. Because you're autoregressive LLF. you have to go into that home and that's like sometimes you run out. So this is the reason why sometimes we prompt it and it doesn't give you an answer. It's just because it's searching and searching and searching

It's spending more and more compute and it doesn't have a bird vision. It just doesn't have ability to turn as it performs a task. It doesn't know what's right and what's wrong anymore. It just like randomly chooses one direction at a time and keep walking until you try to read it. So you might never reach that destination.

And that's why you need a lot of training. So how is it different from the EBM? EBM gonna have the bird view all the time and you allow it to take different routes. So if you see there's a hole Choose a different route. It may not look like it, but Dan Schipper is currently hard at work testing the latest. Yeah. Call this hammock mode. Looks like Dan has to jump in. Hammock mode. Idea by Avery. Every, the only subscription

That's really interesting. I've done been doing a lot of coding with language models recently to sort of test the limits of vibe coding. And one of the things that I find with Or have found with big production apps is in particular if you have vibe coded something. you over the course of op coding it

You may have slightly changed exactly what is this project even supposed to be about? Um and what is the uh what are the problems that I'm trying to solve with it. And If you then go look at the code base it feels like all of the code is locally correct, but it forms this sort of like patchwork of like hotfixes and solutions where if you

zoomed out, you'd be like, actually there's a much simp we should just throw all this out and there's a much simpler way to think about how to do all this stuff. Um but it it has a trouble It has trouble when it's presented with a lot of context, then zooming down into Okay, I need to create a unified solution here that is not a patchwork of different things, but like carries one concept throughout the entire system.

and it gets sort of it ends up being distracted a lot by whatever it's looking at at the at the current moment. Is that the the sort of problem that you think this type of system can help with? Um there's actually a lot of problems in what you're describing. So um yeah so

Solving the problems with wipe coding is a one of our use case. We dreaming about generating formally verified code and automate the coding entirely. So moving you from vibe coding in one specific language to coding in natural language. So you can code the natural English, for example, and no more C plus plus or Python needs to be involved in there. So that's an idea.

And was the coding is like at the state it is today, um yes we prompt L LMs and it gives us something back, but it's still on you as an engineer to figure out what's right and what's wrong. So there's gonna be set of rules LLM can try to help you and even if it has external verifier. Um which just gonna check whether your old logic in your GitHub space is sort of compatible, compliant to what you're trying to create, and if the new logic is compatible with your old logic.

So this thinks external verifiers can check. They could just say, Hey, we know the old logic, we know the new logic, we're gonna see how it's merged together, we're gonna write mathematical proof, making sure that, you know, this logic is compatible with what you already have. and provide you a certificate, it's all like you don't have to review any of it. It's machine verifiable. It's all happening on compiling level.

So all it's gonna say is gonna send you a message, a natural language, like, hey look, this part of your code is not compatible by logic. This is potentially how you fix it and this is the things we cannot fix for you. So we're moving you from vibe coding to vibe code specifications. Those rules and information about your code is called code specification. So once you

Like this this is the first problem, right? We're trying to solve. It's just logic can be incompatible with what you already have. The second problem is Is this code actually doing what you want it to be doing? And this is what AI cannot solve for you. Because AI cannot look in your brain and know what you want. Example would be like imagine you coding web coding autopilot.

So you have specifications from the hardware perspective. You have specifications from your logic perspective. Like hey, make it and there's also instructions, right? How the car is supposed to behave. So there's behavior parameters for your code. So code being able to be compiled is one problem. The second problem is is this code doing what you want it to be doing? So, for example, how fast it is on the hardware and so on, and if the answer is yes, another set of questions is like, okay

Is it gonna hit a pedestrian by chance? Is it actually gonna navigate the map of I don't know, San Francisco? And the answer is I don't know, right? So in here you need to write a bunch of tests and and test your entire system you created. Like, oh, is it overall behaves the way it was meant to be? And so this is this is another form of specifications, right?

And essentially the behavior part sometimes can we can guess it. Like if we have a lot of data, we could have another L L M or EBM proposing you like okay, people who try to do the autopilot of the sky and this is what they're looking at. But it you might be doing something absolutely new and we just don't have data about it. So it's gonna be on you to tell the behavior. And this is where the big

the big thing starts for me personally. If you have L L M as a form of AI driving something important where people trust their lives, like a car or plane or you know, similar Um LLM can misbehave based because you cannot constrain it. It just hallucinates. And EBM can be constrained. You can come up with a set of constraints and EBM just forced to follow it.

So it's on you as a human to make sure that You know what you want for AI to be doing and then from our end, from the technical point, we make sure AI always obeys the rules by given by humans. So And it can go really far, right? We're talking about the cars and planes, but look back to the language. Sometimes model can say something super sensitive to mental person like struggling with depression and it can go really wrong. So even the language can be dangerous.

Here we like what what I also like feel like we're solving is this problem of AI just sometimes we don't know how it's gonna behave a different environment. Uh, but we do know how EBM will behave. Like at least architecture is designed to be constrained and there are ways formally to force those constraints. So it seems like you have a really promising architecture and a model you built, uh or several models you built And

It's very different from the predominant paradigm right now where companies are pouring like hundreds of hundreds of billions of dollars into building data centers and training new LLMs and all that kind of stuff. What do you think about the current state of the industry and Um investment in L M versus other model

Um it's an ecosystem, right? Silicon Valley especially it's an ecosystem and there are lots of micro versions of those ecosystems around the world. So LLM's historically the first form of AI which gave us a ha effect.

like 2021, 2023, when those just they just start appearing, people like, oh my god, this is the new future. It's amazing. So this is why like People start believing that okay, if it's really good at talking to me, eventually it's gonna be good at doing data analysis, my taxes and other stuff. So all the investment communities start pouring money into L L M. And there were a lot of money to be put in that back then.

And right now people see that okay, we grow the compute we trying to change the architecture a little bit and it's sort of reaching out plateau. And there's so much money already put in there. Like, what do you do with this? Um, it's like billions of dollars, literally. You can just like forget it and like, okay, you know, let's dismiss it. Let's pour money into something new. Nobody thinks this way.

And we don't have probably enough money in this entire economy to like just make decisions like that. Billions dollars there, billions dollars there. For the AI specifically. So this is why It's so hard just for investment community just like take that step understanding like, okay, this is not working. Maybe I invest into something radically new. And I'm not saying people don't do it, like people do it.

Percentage-wise, it's m a lot smaller. What people feel comfortable is to take something LLM-based. Which is changed a little bit. So it has a little bit of elements of novelty, but it's also LLM based. So they can still use the portfolio companies and so on. Um so they pour money into that. And I understand because like If I were an investor, I would just say I'm not going to be able to do that. Always looked at what variables would give me risk.

and how can I reuse what would I already have? So it's naturally for you to keep investing into LLMs like architecture just because you already invested a lot in the past. You already committed to this. And maybe start investing a little bit into something new. Um, and there's a lot of big tech companies who are a part of this ecosystem, right? So there's a lot of circular deals happening. Like those companies who create the LLMs.

they create ecosystem for companies who create in data centers. And those who create in data centers they have dependencies with the hardware industry. So it becomes like a one giant thing which is impossible to break. And when we came with alternative architecture, we're like, okay, let's not just try to put it as something out there radically different, which you have to abandon LLM for.

We we are very much compatible with LLMs. Like you could put LLM on top of us. ABM is compatible with Transformers. Transformers can, you know, work with any LLM. We can be that layer, said Where still all your LLM investments valued, you wanna make them cheaper, everyone wants. You can outsource the task to us.

related to spatial reasoning. Like if somebody comes to big tech LLM and say, Hey, can you try to do my taxes? LM not gonna solve this. But if it's attached to EBM, we could take care of that and you can take care of anything language related. So we could actually Try experiments to reduce the cost for your LLM portfolio companies and be a part of the ecosystem which is already out there while we create in a new ecosystem on the side for alternative forms of AI.

That's I think that's really smart. It's a great strategy. I'm really curious about something you said a little earlier that that progress is plateauing.

in L LMs. That's that's news to me. Like I feel like every month or two I'm testing a new model where I'm like, Holy shit, this is actually way better and it does feel like if you look at the top model companies, if you're, you know, talking look talking to OpenAI or Anthropic or Google, there they feel like there's a lot of more room in the L L M paradigm. What are what do you think the what what do you think I'm missing or or the big model companies are missing?

Um personally, you know when I'm saying plateauing it doesn't mean it's like reaching out flat. It's like you're incrementally better and better. But is there gonna be another phase transition, like another breakthrough? I don't anticipate that, just because we already reached so much complexity of those networks using billions of parameters.

so much compute, so much of frameworks like creatively paralleling this uh reasoning processes and it still doesn't phase transition you. Um So the reason why I figure out it's not gonna work in the long term for some tasks like applied to engineering. is when I just start speaking to different companies in that space. like we speak into um like digital assets companies like banks, trading firms, where a lot of data analysis is needed.

Also drug discoveries essentially, just people who look in at a bunch of data, not just patients talking like language set of data, but also like the blood markers, the genes and so on. Um, so a lot of this is data analysis, which is still done by people today. Um They're also like decision making pipelines like sometimes you just need to distribute the energy on your energy grid and you need to know how much energy to pump in your system.

So what it means is you need to analyze the data in the short term, in the long term, construct the prediction how much data. I mean how much power you actually need to put into your system next in the next millisecond or second or an hour. And all of this is still done by people or a combination of people and some programs which are controlled by people. So LLMs are relatively new and AI is like, you know, not relatively new, it's been like a few years for us.

And all of this mission critical industry is still not automated by AI. And even like I'm just asking like, oh, how much of your data analysis LLM is doing today? And the answer is zero. And I'm like, why, what's the issue? And the issue is the big tech LLMs, they're mo mainly like B2C. So it works for you, for your coding and for your personal needs sometimes.

But for businesses, they don't want to share the data with them. They don't want to share data in that big brain for all. They want to have privacy and they want to have their own custom AI, like custom version of AI specifically designed for the attack. And this is what like LLMs cannot do for you in the form we have it today. So there's no B2B model. There are B2B model for like co generational tools, right? Do you have an enterprise package for the co gen for I don't know businesses, but

It's still done by people. Even coding is still done by people. So it's like it's interesting to see that there's still a huge gap, especially in applied engineering, data analysis. Anything which requires a layer of verification, like LLMs are not there. I I I I totally agree with you that that there is definitely s there are definitely still a lot of gaps in L LMs. I'm curious given this and and given what you're seeing in the in the customers you work with, the companies you work with,

Do you think the big model companies are sensitive to this? Are they working on energy based models? Are you working with them? Are like if if they're not gonna get to the next paradigm, do you suspect that they'll start to adopt stuff like this? Um, I do know that some big tech LM models, I mean the companies have EBM models in house.

So which is a positive signal for us, right? So Um, you know, the leaders who were there before we came, um, they started with L LMs and now if they started building the EBMs, after we start building the EBM it's a positive signal, right? Fascinating. Um, Eve, this is an incredible conversation. I feel like I learned a lot. Thank you so much for coming on the show.

Appreciate you. Thank you, Dan. Of course. If people are interested in uh following you or uh following your company and maybe using some of your products, where can they find you? I'm mostly on egg. Um yeah, we have logical intelligence account and my personal account on X. Um, I'm still learning to be more active on social media. Um, we also have LinkedIn page, so we're trying to update it. Cool. Awesome. Well thanks for joining.

Thank you so much, Dan. You absolutely positively have to smash that like button and subscribe to AI and I. Why? This show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure unadulterated knowledge bombs. About chat GP.

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