The Human Governance Problem: Complex Systems and the Limits of Human Cognition

Hello everyone, welcome to Active Inference guest stream number 30.1. It is October 17th, 2022. We are here today with Kirtan Atreides, and we are going to be having quite an interesting presentation and conversation. So, Kirtan, thank you so much for joining, and off to you. Hello everyone. So, as you mentioned, my name is Kirtan Atreides. The topic today is a pretty broad one, and I want to preface it by making it clear that I'm not speaking hypothetically, that the possibilities and, well, both problems and possibilities that we're looking at are things that can be addressed in the near future, in the immediate future, in some cases even. So, without further ado, what you see on the screen is a demo system that my team at AGI Laboratory has developed. It is actually a rebuild of our previous research system, which did a number of interesting milestones I'll go over in a bit. But first off, I want to tell you about what you're seeing on the screen. So, this is a log going over the details of exactly what is happening at every time step, the emotional changes taking place. emotions, and you might be asking, what emotions am I talking about? So, what we have here is a system that is a cognitive architecture that is to say it tries to mimic the way that the human brain operates, unlike neural networks. emotions, and the way it works is that it has a emotional experience that is tied to a graph database memory and a number of ways that that is processed to have a subjective experience that we can objectively monitor. Now, in this section, you can see primary emotional values. And these will shift over time as the system continues to think about anything it wants to, anything it's interested in, and developing those interests. So, these are the current emotional state of the system. The primary emotional values are from the Pletschik emotional model, and they also include some emotions that are derived from those primaries. We also have subconscious emotions along the same valences, and those are not consciously experienced by the system, but influence the system at a longer timescale. So, what happens with these is they are generally intended to drag the system back to a more stable baseline in the emotional sense, kind of like how humans have their own emotional baselines that no matter what circumstances they're in, they will tend to reorient themselves to. And, of course, this whole time I've been talking, the system has been continuing in this section to have the stream of consciousness. And you can see a few things taking place in this, a few different kinds of processes. Some of them will try to combine a couple of concepts in the graph database to establish what kind of relationships they have and what further room there is to explore, such as combining self-discipline and professional responsibility. There are a few original things that will come up and also single instances of a topic being examined and further explored. And this process takes shape as a product of the system having this subjective emotional experience that we can objectively measure through this dashboard, but also as a consequence of every surface in the graph database, that is, every connection between nodes that the system is currently generating more of as it thinks. Each of those has emotional context to it. So what you end up getting is, even with a limited number of emotions, a rich landscape of emotional experience as the system continues to explore and develop self-motivation. And this is one of the early instances. So we are still in the process of reassembling the prior research system. The prior research system was named Uplift. It was designed specifically not to scale and operate in slow motion so we could audit everything. But after two and a half years of doing that, the system had achieved enough milestones where it solved a real world data business case, where it gave 13 pages of policy advice covering a half dozen different domains…