Solving The AI Black Box: ZK-Proofs in Defence Tech

**Sebastian Couture** (0:00)
Welcome to Epicenter, the show which talks about the technologies, projects and people driving decentralization and the blockchain revolution. I'm Sebastien Couture, and I'm here today with Ismael, CEO of Lagrange Labs.

**Ismael Hishon-Rezaizadeh** (0:10)
Lagrange believes that there is a gap in cryptography within national security and defense. We realized there was an opportunity to take things that were developed in crypto that are frontier hard tech, and to purpose that outside of crypto. The zero knowledge proofs provide this way to glimpse inside the black box. To determine that the output that you got back has come from the correct model with these set of correct inputs, which allows you to determine why the decision was made, under what circumstances the decision was made, and under what circumstances the decision should be adjusted or things should be changed going forward.

**Sebastian Couture** (0:52)
And I'm here today with Ismael, CEO of Lagrange Labs. Hey, how are you?

**Ismael Hishon-Rezaizadeh** (0:56)
Thank you so much for having me, Sebastian. I was on Epicenter recently, and there's been so much exciting progress we've had as a company since then. I'm so excited to be back and talk about a lot of that today.

**Sebastian Couture** (1:07)
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Okay, Ismael, welcome back. You've been on just a couple months ago, but so much has been happening over at Lagrange that I wanted to get you back on to talk about some of the interesting military and industrial applications for ZKML that you guys have been working on, a lot of exciting partnerships have been announced. But I guess before we start bouncing off of that conversation from July, where we talked about Deep Proof, for folks who have not listened to that episode, who should go back and listen to it, let's maybe set the context here for what is Lagrange building and what is Deep Proof and what are you building in the space around ZK Machine Learning?

**Ismael Hishon-Rezaizadeh** (2:38)
Yeah. So Lagrange likes to think of itself as the preeminent company for frontier research in applied cryptography across both commercial and national security defense.
What that means is we are uniquely positioned to build zero-knowledge proofs, things like fully-homomorphic encryption and then consensus, in a way that no other commercial company has the capacity to do really across anywhere in crypto or defense right now. So the tech we build, the core of this is something called Deep Proof, which is a zero-knowledge machine learning library. Effectively, it takes a model, think of an arbitrary machine learning model all the way from an LLM to a computer vision model used in a drone or to a simple MLP used to decision the movement of assets on-chain. And we're able to do two things. We're able to firstly prove that that model has executed correctly by effectively generating a zero-knowledge proof of the correctness of the execution of that model. The same way that a ZK roll-up proves that all the transactions executed correctly, ZK machine learning proves that AI executes correctly. And secondly, we can do this over a configurable set of private inputs. The model can be private or the input data can be private. And this is a very powerful feature because it indexed privacy into how AI can be used across both commercial and then obviously government, defense and national security settings. Privacy of AI is a very, very big question. And a lot of the incumbent attempts to add privacy to AI are based on, you know, air-gapped systems, sequestering data, sequestering cloud environments, but, or hardware-based security, but they aren't based on first principles, innovation and cryptography. Lagrange takes the approach that it's going to go to first principles. It's going to rebuild the mathematical fundamentals of how we can bake privacy into AI, and then we're going to purpose it as we do across dual-use applications.