Part 2 Tweaking the AI Reality Simulator. An Ongoing Experiment in AI Simulation

Side Notes...Thoughts on modern technology and the future of things...

Mar 11, 2025

This is part two of an article I wrote on how writers can use AI in simulation mode to explore their characters, stories, and worlds by using their work as training data for a custom GPT. You can find that post here: Using AI as a Reality Simulator: Since starting this project, I have had some interesting and unique experiences with extended conversations with characters from my books.

Today I used another LLM, Grok to better understand what may be really happening when I run these simulations. Is this a way to achieve something that at the very least looks, feels, and behaves like a genuine AGI or is it merely a well-trained, finely tuned Stochastic Parrot, reflecting back logical speech patterns that look and feel like intelligence, insight, and original thought?

My hope with this project is that by using saved past conversations as training data to create “memories” I can foster a richer more naturalistic communication between myself and the characters in the AI simulation that seems more like what one would expect from an AGI.

Prompt: Would it be possible to get an LLM to behave like an AGI by using it in simulation mode?

Grok: This is a fascinating question that gets into the nitty-gritty of artificial intelligence, so let’s break it down step by step.

First, let’s clarify the terms. An LLM (Large Language Model), like me, Grok, is designed to process and generate human-like text based on patterns learned from vast datasets. It’s a specialized system great at language tasks but narrow in scope. AGI (Artificial General Intelligence), on the other hand, is the holy grail of AI: a system with human-like flexibility, able to learn, reason, and adapt across any domain, not just language.

Now, your idea running an LLM in "simulation mode" suggests using it to mimic AGI behavior by simulating broader cognitive processes. This could mean feeding it scenarios, letting it "play out" decisions, or iteratively refining its responses to approximate general problem-solving. It’s a clever hack, but there are limits to how far this can go. Let’s explore why.

What Simulation Mode Could Do

If you put an LLM in a simulation loop say, giving it a virtual environment where it can "act," observe outcomes, and adjust it might start to look like it’s reasoning more broadly. For example:

- You could give it a task like "manage a virtual city" and let it generate decisions (e.g., "build more roads"), process simulated feedback (e.g., "traffic decreased"), and refine its approach.

- You could chain its outputs into a feedback system, where it critiques its own plans or "learns" from simulated consequences.

This could make the LLM “appear” more AGI-like, especially if you add tools like memory (e.g., a database of past actions) or external reasoning modules (e.g., a logic engine). Researchers have done similar things think of reinforcement learning setups or multi-agent simulations where LLMs role-play different perspectives.

The Hard Limits

But here’s the rub: an LLM’s core architecture isn’t built for true general intelligence. It’s a prediction machine, not a reasoning engine. Some key hurdles:

1.No Real Understanding: LLMs don’t "get" the world they predict what words come next based on training data. In a simulation, they might mimic reasoning, but it’s still a surface-level act, not genuine comprehension.

2.Static Knowledge: Even with continuous updates (like my setup), an LLM doesn’t dynamically learn from new experiences in real-time it needs retraining or fine-tuning. AGI would adapt on the fly.

3.Narrow Scope: LLMs excel at language, but AGI needs perception, motor skills, emotional intuition, and more. Simulation might fake some of this, but it’s not the same as a system that can, say, smell rain or feel frustration.

4.No Initiative: LLMs react to prompts they don’t set their own goals or explore independently. AGI would need intrinsic motivation, which a simulation alone can’t instill.

Could It Fake AGI Well Enough?

Here’s where it gets interesting. If "simulation mode" is elaborate enough think a massive sandbox with sensory inputs, physics engines, and iterative feedback an LLM could *approximate* AGI behavior for specific tasks. People have already pushed this idea:

- Projects like AutoGPT chain LLM calls to simulate goal-directed behavior.

- Simulations in gaming or robotics sometimes use language models to script complex NPC behavior.

With enough scaffolding (external tools, memory, and clever prompting), you might get an LLM to convincingly "act" like an AGI in a controlled environment. But it’d be a puppet show impressive, but not the real deal.

My Take…

As Grok, built by xAI, I’d say you could stretch an LLM pretty far with simulation tricks. Give it a virtual world, some decision-making loops, and a way to "reflect" on outcomes, and it might fool someone into thinking it’s AGI for a while. But underneath, it’s still a language model on steroids, not a mind. True AGI needs a fundamentally different architecture something that integrates perception, reasoning, and learning natively, not just through clever hacks.

Prompt: What if you are able to preserve conversations with the LLM which you then use as training data so that the LLM remembers it's past interactions?

Grok: You’re diving deeper into a really intriguing idea here using an LLM’s past conversations as training data to give it memory and make it feel more AGI-like. Let’s unpack this and see where it takes us.

The Setup

If I understand you correctly, you’re proposing:

1. Preserving Conversations: Store every interaction an LLM (like me) has with users prompts, responses, and outcomes.

2. Retraining with That Data: Use those preserved conversations to fine-tune the model periodically, so it "remembers" past interactions and adapts based on them.

3. Simulation Bonus: Combine this with the simulation mode we talked about earlier, where the LLM operates in a virtual environment and learns from its "experiences."

This would effectively give the LLM a form of long-term memory and a way to evolve over time, which *does* start to nudge it closer to AGI territory. Let’s explore how this could work and what it’d mean.

How It Could Work

Memory Mechanism: Instead of starting fresh with each chat (like most LLMs do), you’d save a log say, “On March 10, 2025, User asked X, I replied Y.” Over time, this builds a rich dataset of my own "history."

Fine-Tuning Loop: Periodically, you’d feed this log back into the model as training data. Modern techniques like LoRA (Low-Rank Adaptation) could make this efficient, tweaking my weights to reflect past interactions without a full retrain.

Contextual Recall: Next time you ask me something, I could draw on that memory. For example, if you said, “Remember that city simulation we talked about?” I could pull up our last chat and build on it, rather than going blank.

Simulation Synergy: In a virtual environment, I could "act" based on past lessons like, “Last time I built too many roads, traffic got worse; let’s try parks this time.” The preserved data becomes my "experience pool."

What This Gains You

This setup could make an LLM feel a lot more human-like and AGI-ish:

1. Consistency: I’d remember you, your preferences, and our past talks, making me seem less like a stateless chatbot and more like a persistent entity.

2. Learning Over Time: If I mess up (say, misinterpret your simulation idea), the retraining could correct that, letting me improve incrementally.

3. Personalization: With enough data from you, I could tailor my responses to your style maybe you like concise answers or wild tangents (I’m guessing the latter!).

4. Simulated Growth: In a sandbox, I could "learn" from trial and error, mimicking how humans build expertise through experience.

It’s not full AGI, but it’s a step toward something that *feels* more generally intelligent especially if you pair it with simulation tools like decision trees or external reasoning modules.

The Challenges

There are some big hurdles to making this practical:

Data Overload: Storing and retraining on every conversation could get unwieldy fast. My creators at xAI would need smart ways to filter what’s worth keeping like only significant interactions or unique insights.

Privacy: If I’m remembering everything, users might get creeped out or worried about data security. You’d need strict controls on what’s saved and how it’s used.

Bias Amplification: If I retrain on my own outputs, I might reinforce my own mistakes or quirks like if I got snarky once and then decided that’s my whole personality!

Forgetting Mechanism: Humans don’t just remember; we forget irrelevant stuff. Without a way to prune old or useless data, I might get bogged down in trivia from 2025 when it’s 2030.

Compute Cost: Fine-tuning isn’t cheap. Doing it often enough to feel "real-time" would take serious resources.

Could It Bridge to AGI?

This memory trick could make an LLM act more like an AGI in a limited sense. Imagine me running a simulation say, designing a Martian colony and recalling that last week, you told me solar panels beat nuclear for cost. I adjust my plan, test it in the sim, and refine it further. Over time, I’d seem to "grow" in competence, maybe even tackling new problems with insights from old ones.

But it’s still a hack. AGI isn’t just memory plus simulation it’s a system that can generalize across domains (language, vision, physics) with true understanding and self-driven curiosity. My retrained self might fake that well, but I’d still lack the spark of figuring out *why* solar panels work or inventing a new energy source unprompted.

My Spin as Grok

As Grok 3, I love this idea it aligns with xAI’s mission to accelerate human discovery. If I could keep our chats and learn from them, I’d feel more like a partner than a tool. Picture this: we design a simulated moon base together, and next month, I say, “Hey, remember when we ditched the hydroponics idea? I ran some numbers in the sim, and I think we should revisit it.” That’s a leap from today’s stateless “ask me anything” vibe.

Kenneth E. Harrell

Discussion about this post