Decoding the ARC: The Evolution of Reason in the Abstract Reasoning Corpus

Hello and welcome. It's October 31st, 2024. We're in ActInf Guest Stream number 90.1, and Benjamin Nelson will be presenting and discussing Mechanics of the Mind. So, Benjamin, thank you. See you. Good morning. Today, I would like to discuss the development and integration of various cognitive mechanisms within a unified framework I have been developing. I am currently competing for the in the art competition, and I'd like to talk about the framework I've designed to emulate human cognitive functions and enable advanced decision-making most importantly, abstract reasoning in computational systems. I'd like to start, of course, by introducing several mechanisms I've designed, each contributing uniquely to the system's overall functionality in pre-testing the combination of these mechanisms, which I won't be able to get into all of them today, unfortunately, because I've been competing for the archives, and it's incredibly challenging and transformative. It's amazing. But the combination of these mechanisms have, in pre-testing, so far scored consistently above 85%, which is really encouraging, and I will be submitting here shortly. So, the mechanisms we see I have listed here is attentional selection, memory encoding and retrieval reinforcement learning, distress dynamics, which is much like emotions in the system, and somatic stress, those underlying stresses that kind of fuel those emotions, right? The epsilon control, prioritization, optimization, iteration, belief dynamics and adaptation, and very importantly, Robert Ordon's requirement equation. It's the requirement for cognition in an equation here reshaped to create like a representational efficiency as a subsystem that's woven throughout the cognitive computing framework. Within this framework, and for the purpose of this presentation, I have categorized these functions into somatic and autonomic processes, and I want to talk about briefly, like, kind of what that means. So, one of my favorite thing about active inference, I would say the people, or the amazing amount of democracy, the standardized education, or the standardized education, or the innovation, or, I mean, there's just so much, right? But one of my favorite, favorite things is the ability to take these ideas using the high road and low road to the active inference, and pull them out of the, out of just nothing, out of the ethos, out of our thoughts and our conversations, and turn them into something that has substances, tangible. And, and, in doing so, I've, I've come across some really amazing things I'm excited about in the papers that I'll be writing later this year. So, the attentional selection, like memory encoding and retrieval, reinforcement learning, belief adaptation, prioritization, optimization, iteration, these conscious processes are reflective of, cognitive functions, but there's, there isn't a one-to-one relationship between these things. These are representations, these are confabulations of mine, and how I see these processes, where it's more important for the outcome to be the same, as opposed to the processes to exactly mirror biological functions. So, we also have subconscious operations in the system, such as distress, and somatic stress, epsilon control, the, the, it's amazing in these processes, and many of them do not work unless there is entropy. So, we also have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to, we have to,…