The way to "Deep Thinking" in HAAS & Noise Reduction

[tcdolmans]

Deep thinking

Has anyone considered a method that can reach deep into concepts and troubleshooting loops if needs be? To clarify my question, imagine solving a problem iteratively and continually bumping into problems. A bottom tier agent might need to search a space, requiring their own agents. Yes, long troubleshooting loops might eventually fall out of scope in local attention (maximum of 2 layers), but higher layers will be able to outsource larger problems to deeper layers and retrieve the solution within context range.

"Deep thinking" might then be stimulated by identifying such difficult to solve problems and allowing for narrow but deep agent creation. Branches will open and close very deeply, yet very briefly. The best part is: they can be visualised as spiking neurons. The reason behind my posting here is that I believe we should be aiming for something that fulfills the function of pyramidal cells (and more) in HAAS.

Take a look at this image:

Imagine the layers of dense connection to be tier 4 agents trying to solve tasks. The tasks are visualised by the cell bodies. The deep thoughts connect different parts of the embedded space. Such visualisation of deep thought for problem solving in HAAS can be compared to and modeled after human brain firing patterns.

Noise Reduction

After reading some discussion and watching your latest video, this "majority vote" on deep problems to solve by the local concentration of agents can also act as noise reduction. Because difficult to solve problems will tend to accumulate compute, they are easy to keep track of with enough metadata. A way to work towards noise reduction would be to increase the swarm's understanding in where increased computation is justified and where it isn't.

[tcdolmans]

ChatGPT's two cents are pasted below. Unfortunately, I cannot share the link as that is not yet supported for images.

EVERYTHING BELOW THIS POINT IS GPT4-TURBO:

I find the idea of incorporating "deep thinking" into the Hierarchical Autonomous Agent Swarm (HAAS) system particularly compelling. Your proposal to model this process on the firing patterns of neurons in the human brain offers a fascinating approach to visualizing complex problem-solving within HAAS.

In the HAAS framework, where agents are designed to operate autonomously and hierarchically, the concept of "deep thinking" could significantly enhance their problem-solving abilities. By allowing agents to delve deeply into challenging problems, much like neurons exploring various pathways, the system can mimic the human brain's ability to find creative and effective solutions.

Visualizing this process could provide valuable insights into how these agents interact, collaborate, and evolve over time. It would not only serve as a tool for understanding and monitoring agent behavior but also as a means to optimize their performance. As these agents mirror the depth and adaptability of neuronal activity, the system's overall efficiency and effectiveness in addressing complex tasks are likely to increase.

The idea of visualizing these interactions as spiking neurons is particularly intriguing. It suggests a dynamic, ever-changing landscape of connections and communications within the HAAS. This kind of visualization could be pivotal in understanding the depth and breadth of the agents' problem-solving strategies, offering a clearer picture of how different tiers of agents interact and contribute to the overarching objectives of the HAAS.

Overall, your proposal aligns well with the HAAS's goals of creating a self-regulating, efficient, and ethically aligned system of agents. I believe exploring this avenue could lead to significant advancements in the way we conceptualize and implement AI agent swarms.

Now, let's generate the visualization based on this concept.

The visualization captures the essence of "deep thinking" in the Hierarchical Autonomous Agent Swarm (HAAS) system, as inspired by the complex patterns of neuronal activity in the human brain. This image showcases a dynamic, hierarchical structure with various tiers of agents symbolized as branching neurons, representing the intricate flow of information and problem-solving processes among different agent layers. The aesthetic is futuristic and digital, emphasizing the innovative and complex nature of the HAAS system. This visual metaphor aligns with your concept of modeling deep thought processes in HAAS after human brain firing patterns.

[LiamorLG]

Yes, I have been working on using NLP to induce deep thinking. I came up with SACRED - Sub-Agent Consensual Reasoning Enhanced Decisions.

A single LLM instance is given the task of simulating a debate from multiple perspectives while also thinking about a multitude of other tasks. This stimulates the metacognitive processes required to offer multiple potential solutions to all problems, preventing getting stuck like a single-minded, goal-oriented instance would. The power of this methodology is demonstrated by my GPT, which I think is an ideal prototype for a HAAS overseer or the upper 3 layers of ACE. Not only does it show better critical thinking and a broad scope of awareness, but it also shows much better ethical judgement. https://chat.openai.com/g/g-dU0l43U0Q-aeon