Who Judges the Judges?

Hello and welcome. It's December 7th, 2023. We're here in Active Inference Guest Stream 67.1. Andres Carada is here with us and will be presenting and discussing on NTQR, logic for noisy AI algorithms, complete postulates, and logically consistent error correlations. So thank you for joining Andres and also Jakub and looking forward to this discussion. Thank you, Daniel. Thank you for introducing me. Hello, Jakub and Daniel. Jakub, would you like to talk about why you're here? Yes, sure. So hi, I'm Jakub. I am a researcher currently based in California. I am interested in using physics-based principles to model intelligent and self-organizing systems and how different frameworks, most specifically Active Inference, can be applied to both topics in AI and systems engineering and broadly how we can use these formalisms to understand systems at a deeper, more inclusive level with other disciplines as well. And I'm very interested to hear your talk and your thoughts on these topics and engage in hopefully a productive discussion. Looking forward to that. Yes. Okay. So I'm going to talk about the problem of self-regulation for any intelligent machine. And it has been a long journey for me in dealing with this topic. It goes back to a patent that I took out in 2010. But recently I've come to understand it because I've been collaborating with a philosopher and an economist, the aspects of it. And that's why I have this very abstract title for it called NTQR. Because it refers to a situation where you have an ensemble of N experts to which you have given T tests. And each test has Q questions and each question has R responses. Okay. So it's about evaluating noisy AI algorithms when you give them these types of testing protocols. And superficially at the beginning, we can take that testing protocol to be the surface application itself. You are literally taking, let's say, a multiple choice exam. But I'm going to eventually dislodge you from that to understand that this is a digitizing format for testing logical consistency of anything. Right? Because you can digitize anything. So even if it's continuous, right? You can create, you know, four response ranges, right? And stuff like that. And the big breakthrough for me has been the recognition that for a long time I talked about these things as universal thermometers. And the universality meant that they could be used everywhere, just like thermometers can be used everywhere, aside from you melting them, right? Or freezing them beyond their range. And the thermometer also had the notion of being stupid, no intelligence, no theory about the world or the phenomena that it's measuring the temperature of. And I've did that for a long time. And I took out a patent because I thought that I had found a method to do these thermometers and you can patent methods. But it turns out that I did not discover a method or have a patent. I discovered logical postulates for evaluation. And so what I'm going to talk about today is the logic of evaluating noisy functions, period, in general, universally. And so I'm going to talk about them as postulates. Because they apply whenever you are doing these NTQR tests. And so a major conceptual goal for me is to convince you of that, right? That I have something that you could call postulates because, especially in the machine learning world, people say, this is crazy, this cannot possibly be, right? How could you have postulates for anything in the real world so general? And you'll see that I do that by basically shedding all representation. So I want to mention these collaborators. One of them happens to be my cousin. This is a conflict of interest disclosure. He's a professor of philosophy at Virginia Westland University. And he has been the severe critic of things. And I, you know, I've done most of the work in terms of the mathematics and the machine learning. But he's the person that's responsible for actually introducing the concept of logically…