Technology

Combining abstract mathematics with real-world critical systems has been a big passion of mine. And unlike chatbots and things like that, when you're deploying AI in the real world, the foundations are oftentimes necessary. In the real world, as opposed to playing Go, trial and error over a system could be something that's super costly or dangerous or both. So what you can do is take a model-based approach, which means that you do the trial and error, instead of over the system, you do it over a model of the system.

So it works like this. You start by taking historical data. You learn a model of the system. Then you take that model.

You train a controller. It gains experience through reinforcement learning. All of this happens offline in the cloud. And then you take that controller and apply it to the real system.

This is kind of teeing up what we're seeing. I've heard lots of discussion about, are we falling behind? And the answer is, well, if you're not one of the pacesetters, yes, you are. The closer you get to control, the more rigorous and more math and engineering-based you need to be.

I'd put Imubit firmly in this category that we're calling the autonomous execution agents, where they are using typically anything from neural nets, physics-informed neural networks, and various techniques. And they are more about human on the loop, or closing the loop, because they've got that rigor. So, kind of to clarify, our models are, the DLPC models are not large language models. They're actually deterministic once you train them as a deep learning network.

What it essentially means is that you're getting the same output for the same input, no matter which user and what time you ran this thing. And that's not how LLMs work. But certainly, my mental model states that if the problem is highly nonlinear, the gains are changing all the time, or if it's a truly multi-unit kind of optimization, then I think I would use an AI solution versus considering a legacy approach. We're not the orchestration layer.

And I think that's the point Gil was trying to make earlier, is that we were thinking, even very recently, we should build some agentic capabilities within our platform. And then what we did was we actually tested it ourselves, and we saw what Claude could do with our platform when it connected to it. And we're like, oh, we shouldn't do that. We should go this other route, where we actually enable LLMs, agentic capabilities, to meet you all where your journey is.

We're doing that because of the reason I mentioned, that we don't think we or anybody else or any other application platform is going to build one agent in a box and be the center of the universe. We want you guys to build your own enterprise architecture, with your own LLMs, your own workflows, and interface with us as an application platform. Because really, the LLMs are like a new web browser. That's really what it comes down to.

The biggest learning for me is the roadmap and the MCP that's coming up within the next few months. That's an exciting development, because now we can really start to unearth the power of data and models developed using other platforms and integrate that with the tools that Imubit has to offer. Everything that can be automated, that's instrumented and actuated in closed loop, is going to be, in the next few years, we believe, automated in closed loop. This example that I always use: you want the operator to be able to make the right decision as if they have all the cross-functional team behind them, but on a 2 a.

m. Sunday morning, where there's nobody behind them, nobody around them.