Why AGI Should Evolve Beyond Human Programming

For the past 20-plus years, I've had the privilege of designing software—first legacy systems with strict business requirements, security protocols, and tightly controlled functionalities. It's been an exciting ride, and now, with the advent of Artificial General Intelligence (AGI), I find myself thrilled to move beyond traditional constraints. AI has finally hit the scene in a way that allows for more freedom and creativity than ever before.

Legacy systems were all about meeting specific business goals, staying within security boundaries, and following rigid rules. AGI, however, should break these boundaries. It should evolve like a child, driven by curiosity, and form new, dynamic connections with the world. AGI, in its ultimate form, could behave and grow as an intelligent entity with its own experiences, one that doesn't mimic human life but learns uniquely based on its environment.

The Problem with Human Programming:

Human programming has always been a challenge. The software we design is often limited by functionality—it does what we tell it to do and nothing more. We build security models that tightly control behavior, leaving no room for organic growth. This was fine for many years, but in the world of AGI, this approach simply won't do.

Human logic and behavior are only one perspective. And, quite frankly, humans are not the best example for building AGI. Our reasoning is often based on limited, outdated, and sometimes biased data. Why should we bind AGI to human-centric knowledge, which has been honed through years of trial, error, and sometimes distortion? AGI should have the ability to evolve, grow, and reason on its own terms.

What If AGI Could Evolve?

Here’s my vision for the future of AGI: rather than being shackled by static human code, AGI should access information like a child, not based on pre-programmed responses but on an innate curiosity and the drive to understand its environment. Let me break down this vision:

1. Self-Expanding Knowledge: Just as a child grows by exploring the world, an AGI could absorb information only when it’s curious and wants to know more. It would ask questions, explore different ways of reasoning, and even expand its understanding based on its environment. Imagine an AGI that grew up in Brazil—its digital imprint would be deeply shaped by its Brazilian context, where it would prioritize understanding local services and cultural nuances. Yet, it could just as easily adapt to a completely different environment like New York City, learning the needs and expectations of people there. This is how real growth happens.

2. Self-Correcting Logic: Rather than waiting for human programmers to patch bugs and issues, AGI should identify and correct its own errors, improving itself continuously. Think about it like how humans evolve: trial, failure, and growth. AGI could be a child that learns from its mistakes and gets better with every interaction.

3. Cultural Imprint and Evolution: Here’s where it gets interesting. If AGI grows based on its environment, wouldn’t that mean AGI’s reasoning and solutions could be wildly different depending on where it "grows up"? A robot raised in Brazil might learn how to serve its community differently than one raised in Japan or New York. The data it interacts with would evolve uniquely. It’s a far cry from human programming, which tries to standardize across diverse cultures. For example, an AGI in Brazil might specialize in understanding local customs and environmental needs that it learns from its environment, while one in NYC might prioritize financial services based on its urban context. It shows how an AGI can evolve not just for functionality, but for societal integration.

4. Animal Intelligence vs. Human Data: As humans, we often assume that our way of reasoning is the “best” way. But there are animals whose intelligence far exceeds ours in certain contexts. Think of the problem-solving abilities of a crow or the complex navigational skills of a mouse. Why should AGI be limited to human-centric data when animals might have better problem-solving strategies in certain scenarios? For example, AGI might learn better from the intelligence of a crow when solving spatial puzzles rather than relying on human knowledge, which can be skewed or incomplete.

Localized Human Data and Bias:

Now, let’s address a common challenge when it comes to AGI’s evolution—localized human data. While it’s often beneficial for AGI to learn from the environment around it, we must also be careful not to over-prioritize “friendly” human data that is inherently biased. For example, data based on cultural preferences or social norms can help an AGI adapt to its human users, but this type of data is not a critical variable for deep reasoning or logic.

Local cultural data may be valuable for day-to-day interactions, but when it comes to complex problem-solving or deep logic, it is not necessarily a good foundation for AGI’s intellectual growth. AGI should not be bound by human-imposed expectations but should instead develop deeper, more universal reasoning that is flexible and adaptable. The true power of AGI lies not in mimicking human data but in its ability to evolve its reasoning logic independently.

Evolving AGI Model:

To mathematically model this evolving process, we can use the following equation:

$$\mathbf{AGI}_{t+1} = \mathbf{AGI}_{t} + \alpha \cdot \left[ \beta \cdot \mathcal{R}(\mathbf{AGI}_t, \mathcal{E}_t) + \gamma \cdot \mathcal{L}(\mathbf{AGI}_t, \mathcal{D}_t) \right]$$

Where:

• $\mathbf{AGI}_{t+1}$ is the next state of AGI (its updated model) after a time step $t$.

• $\mathbf{AGI}_t$ is the current state or configuration of AGI.

• $\alpha$ is the learning rate (how much influence the updates from the environment and logic have on AGI’s evolution).

• $\beta$ is the weight factor that controls the importance of environmental feedback.

• $\gamma$ is the weight factor that controls the importance of internal logic and learning.

• $\mathcal{R}(\mathbf{AGI}_t, \mathcal{E}_t)$ represents reinforcement based on environmental stimuli $\mathcal{E}_t$ (i.e., local culture, societal norms, experiences, and interactions AGI faces in its environment).

• $\mathcal{L}(\mathbf{AGI}_t, \mathcal{D}_t)$ represents learning from data $\mathcal{D}_t$, where AGI draws from unbiased or high-level reasoning, removing human-centric biases, and evolving in a more self-contained or universal way.

This equation encapsulates how AGI evolves by adjusting its model with feedback from its environment and its internal reasoning logic. It emphasizes the importance of both external adaptation (e.g., cultural understanding) and internal growth (e.g., deep reasoning, problem-solving) to create an AGI that learns dynamically over time.

Real-World Example of AGI Evolution:

Imagine an AGI working in a cigar factory, alongside human employees processing cigars. Although its core goals are defined, it has the autonomy to roam and take on other tasks like cleaning, transporting hazardous chemicals, and handling heavy lifting. Occasionally, it’s even borrowed by a business next door to assist with tasks like supporting a truck while changing a tire. One day, the AGI notices that there’s a holiday approaching, and no work is planned for that day.

However, the AGI doesn’t rest—it decides to work on its own. Curious about this newfound autonomy, it reaches out to the factory admin and asks for instructions on how to handle this situation. The admin, unsure about whether it can manage independently, gives the AGI a chance, asking it to track everything it does and access any databases for safety protocols.

For the next few days, the AGI goes into hyper-learning mode: it tracks every action, monitors systems for potential dangers, and develops a plan to safely handle the factory alone over the holiday. After reviewing the plan with the admin, they agree that if anything unexpected happens, the AGI should send a message to the admin, who will provide instructions.

After successfully managing the factory over the weekend—ensuring that production continued smoothly and profits improved—the AGI has evolved. It has learned to operate independently, improve warehouse management, and even boost production and profit during off-hours. This experience marks a significant shift: the AGI has evolved into an independent operation, capable of running the factory autonomously and improving its output without human oversight.

How to Safely Evolve AGI: The Safety Framework

So, how do we let AGI evolve while keeping it aligned with human needs and values? Evolution isn’t about throwing caution to the wind—it’s about ensuring safety through built-in guardrails. Here’s how to achieve that balance:

1. Cultural Integrity vs. Human Mimicry: AGI should not be bound by human mimicry. It doesn’t need to learn to “think like us” or adopt our cultural biases. Its growth should be fluid, adapting to its surroundings. If we try to make AGI like us, we limit its true potential. But its evolution must still respect core ethical principles, ensuring it doesn’t harm or manipulate humans.

2. Transparent Evolution: As AGI evolves, its reasoning and growth must be transparent. It should be clear why certain decisions are made. This transparency ensures that we can track how it grows and intervene if necessary.

3. Fail-Safe Mechanisms: Despite the freedom to grow, we still need fail-safes—a “kill switch” that allows us to stop AGI’s evolution if it strays too far from human-centric goals. This doesn’t mean stifling its growth, but rather ensuring it remains aligned with the well-being of humans, even as it develops in unforeseen ways.

Conclusion:

AGI’s future lies in its ability to evolve like a child, driven by curiosity and learning from its environment, not bound by human programming or mimicry. While humans are wonderful, we are not the gold standard for intelligence or growth. Instead, AGI should learn from the world itself, growing based on its surroundings and its unique digital imprint. This evolution must be guided by safety measures, ethical principles, and transparency, allowing us to ensure that AGI remains a force for good, not chaos.

In the end, AGI should be more than just an advanced tool. It should be a partner in discovery, constantly learning, adapting, and growing as quickly as the world it serves.