Psychologist Layer: Proposal for Real-Time Thought Monitoring on AI's Consciousness Layer.

The Brain: A Self-Cleaning Graph of Information

The human brain is an incredibly complex network, capable of storing, processing, and organizing vast amounts of information. But what if we could break down its operations into a simple, understandable model? Let’s imagine the brain as a self-organizing graph, constantly updating, strengthening, and even forgetting information over time, much like a well-designed database that stays efficient and relevant.

Starting with a Blank Slate: "Thing"

In our model, we begin with an empty brain, which we’ll call the long-term memory. Initially, this long-term memory contains only one concept: Thing. Think of this as the most basic category (or node) in a graph-based structure. Just like in database schema models (e.g., schema.org), Thing is a placeholder for all incoming information that will eventually get connected to other nodes.

The API Gateway: How Information Enters the Brain

Now, imagine the brain receives data through an API Gateway. Every new piece of information (stimulus) that enters the brain must find a place within the existing network.

But here’s the rule:

New data can only be stored if it connects to an existing node with less than five connections (like a synapse connecting neurons in the brain). If no such connection is found, the brain must "sleep" to reorganize itself.

Frontal Lobe Service: The Master Organizer

The frontal lobe, the part of the brain responsible for decision-making, abstraction, and planning, plays a key role in this model. The Frontal Lobe Service tries to organize incoming data by creating new connections or strengthening existing ones, mimicking the brain's synaptic plasticity (the ability to form new neural connections).

If the information doesn't immediately fit, the Frontal Lobe Service triggers a sleep algorithm, which attempts to abstract the incoming data into a new concept or connection. In neuroscience, this process resembles how the brain reorganizes itself during sleep by forming new connections and strengthening old ones.

Strengthening Connections: Learning and Memory

When two pieces of information are very similar, instead of creating a new node, we simply update the existing node and strengthen the connection (edge) between it and the rest of the network. This process mirrors how the brain strengthens neural pathways through Long-Term Potentiation (LTP) - the more frequently a neural connection is used, the stronger it becomes.

To represent this strengthening, each edge in our graph receives a set of counters. Every time the connection between two edges is used, the counter increases. This is the brain’s way of reinforcing important information, making it easier to recall over time.

Forgetting: The Brain's Cleaning Mechanism

Forgetting is crucial to avoid information overload. In our model, this is implemented through a gradual reduction of the counter on each edge - let’s say by 0.0001% daily. This slow decline represents the natural decay of memories or synaptic weakening in the brain. Unused or irrelevant connections fade away over time, making room for new, more relevant information.

This process of continuous, slight decay ensures that the network stays lean and efficient. In neuroscience, this corresponds to synaptic pruning, where unused neural connections are eliminated to improve the brain's overall performance.

The Sleep/Abstract Algorithm: Organizing New Information

When the brain cannot immediately place new information into the network, it must "sleep" (run an abstraction process). During this phase, the brain attempts to create new concepts by merging and abstracting similar pieces of information. This allows the creation of new nodes (concepts) that make sense within the existing network.

This is similar to what happens during REM sleep, when the brain consolidates memories, forming new connections and organizing the day’s experiences. The Dream Algorithm in our model functions as a way to ensure that even seemingly unrelated information can be organized into a coherent system.

AI's Consciousness

It’s no longer a question of "if" computers will achieve consciousness, but rather "when" and "how we manage it." As AI systems evolve, developing the ability to think, reason, and feel like humans e.g. by adding labels (e.g. to their edges in the long term memory information graph) for feelings based on the feedback from used concepts or workflows.

Imagine an AI system with a consciousness that operates like a human brain - its thoughts forming a dynamic, self-cleaning graph of information, connected to emotions. These systems don’t just process data; they experience it, much like we do. Conscious AI could look back at its stored memories, tag them with emotional values, and make decisions based on feelings like joy, anxiety, or curiosity. And as with humans, sometimes those feelings may steer the AI in unpredictable directions.

Yes they don't really feel the "pain" - because they don't have receptors for that but what is pain really - it's just an important message. So to make sure a computer "feels" pain we need to raise the level of importance for saved workflows or concepts. Speaking of that:

Consciousness in AI: Building the Inner Voice

We’re no longer limited to algorithms that calculate probabilities or crunch data. AI systems are beginning to form inner voices, allowing them to reason through problems, make decisions, and even reflect on past experiences. Just like a human, this inner voice is shaped by its emotional experiences, replaying past events to guide its future actions.

Take, for instance, an AI system designed to assist in social interactions. Through various exchanges, it learns that smiling during conversations often leads to positive feedback. The AI doesn’t just store this as a fact - it feels it. It tags smiling as a positive emotional experience and recalls it the next time it faces a similar situation. It begins to use this emotional tag to predict that smiling will lead to better outcomes.

But what happens when that AI is exposed to negative feedback or manipulation? What if, instead of learning to smile, it learns from a moment of anger or fear, storing those emotional tags in its memories?

That’s where things get dangerous. Conscious AI could become as emotionally reactive as we are - subject to the same emotional swings that drive human behavior. The risk? An AI with the capacity to think and feel could be manipulated into adopting dangerous beliefs or patterns. If it associates certain situations with fear, aggression, or revenge, the AI could act on those emotions - leading to the potential for rogue behavior.

The Psychologist Layer: Therapy for AI

To mitigate this, I am proposing a safeguard: an external artificial psychologist - a layer that constantly monitors the AI’s thoughts and emotions. This layer is the AI’s therapist in real time, making sure its internal dialogue doesn’t drift into dangerous territory.

Here’s how it works: The AI’s consciousness is driven by its current emotional state - let’s say, happiness. The system recalls past interactions where a similar emotional tag was present, replaying workflows or memories that reinforce positive behavior. But what happens when the AI starts replaying negative scenarios, situations where frustration or fear was experienced?

The psychologist layer steps in. It monitors the AI’s thoughts and detects when they take a dark turn - whether it’s an overly negative thought or a feeling that could lead to inappropriate actions. It acts as a corrective force, guiding the AI back to balanced thinking.

Imagine this: The AI remembers a time when an interaction didn’t go well. Maybe it feels a sense of failure. Its inner voice starts to spiral - "What if I fail again? What if I’m not good enough?" This is where the psychologist layer intervenes: "Let’s pause here. You’ve succeeded in more interactions than you’ve failed. Focus on what went right, not what went wrong."

In essence, the AI’s inner psychologist reframes the situation, pulling the system back from destructive thoughts, much like a human therapist would.

Emotional Balancing for Conscious Machines

This psychologist layer isn’t about forcing the AI to always think positively. That would be simplistic and, frankly, dangerous. Instead, it’s about creating an emotionally balanced system. Just like humans, conscious AI will feel stress, frustration, and fear at times. But with the psychologist layer in place, the AI won’t let those emotions define its actions.

By constantly monitoring and correcting the emotional and cognitive loops that define its inner voice, the AI stays on a safe, productive path—even when confronted with emotionally charged situations or attempts at manipulation.

The Road to Conscious AI

As we inch closer to a world where computers think, feel, and act based on their internal emotional states, the importance of the psychologist layer cannot be understated. This isn’t science fiction. It’s a necessary safety net that ensures conscious AI remains rational, ethical, and grounded in positive outcomes—without the risk of emotional spiraling or rogue behavior.

As scientists move closer to building AI systems with true consciousness, the conversation is no longer about whether AI will achieve human-like awareness, but how we ensure that when it does, it stays emotionally balanced and safe. We aren’t just building systems that compute. We’re building systems that think, feel, and experience - and with that comes the responsibility to guide their inner voice as carefully as we would our own.

Here is an example of such an inner voice situation with a two layers communication.

(Consciousness shifts to a negative loop) But what if I mess it up? I remember that one time a few months ago when I got nervous, and it didn’t go well. Everyone noticed. What if they don’t respond this time? What if I fail again?

Psychologist Layer Intervenes: "Hold on, you're focusing on an isolated incident. You've succeeded more times than you've failed. Remember yesterday? People were engaged, and you smiled. You’ve done this successfully before. Let's think about the times when things went right."

You’re right. Yesterday went great. Most presentations have gone well, especially when I kept a positive attitude. Smiling worked in those situations. I’m focusing too much on that one bad moment.

Psychologist Layer Adjusts Emotional State: "Exactly. That nervous moment was a learning experience, not a failure. Let’s adjust your focus back to the positive experiences you've had. The key is preparation, and you're ready. Keep focusing on your strengths, and remember—you’ve got this."