This article was partially developed with the support of AI-assisted writing tools.
I have been thinking about a question recently:
If we do not begin with large models, training data, or predefined architectures, but instead start from “zero” and allow a population of simulated neurons to evolve spontaneously within a closed environment, could some form of primitive intelligence eventually emerge?
This is not code-based life, not self-modifying software, and not a dangerous digital organism.
It is a controlled, closed, and accelerable digital evolution experiment.
Below is a directional blueprint I have organized. Discussion, critique, and extensions are welcome.
1. Why Build Intelligence from Zero?
Despite their impressive capabilities, current AI systems exhibit several fundamental limitations:
- Lack of persistent internal state
- Lack of behavioral consistency
- Lack of homeostatic mechanisms
- Lack of intrinsic “style”
- Lack of evolutionary history
They behave more like tools than entities.
In contrast, even the simplest biological organisms—such as worms—possess:
- Internal state
- Homeostasis
- Behavioral tendencies
- Structural evolution
- Environmental adaptation
This leads to a natural question:
Can we simulate evolution in the digital domain and allow intelligent structures to emerge naturally rather than being manually designed?
2. Core Idea: A Digital Neuron Ecosystem Under Evolutionary Pressure
The goal is not to train a model, but to:
Construct a population of minimally functional simulated neurons that can spontaneously connect, organize, replicate, and be eliminated within a closed environment, eventually evolving into intelligent structures.
These “neurons” are neither biological neurons nor deep learning nodes. They are abstract computational units that:
- Maintain simple internal state
- Receive and emit signals
- Form and break connections
- Replicate or die under defined rules
Intelligence is not engineered; it is:
- Structurally emergent
- Behaviorally accumulated
- A product of long-term evolution
This is essentially a digital evolutionary experiment.
3. Experimental Environment: Closed, Controllable, Accelerable
The system is inherently closed:
- No interaction with the external world
- No access to external resources
- No code-level self-modification
- Fully pausable, resettable, and replayable
- Evolutionary time can be accelerated through compute
This enables something nature cannot provide:
Observing thousands or even millions of generations within real-world time.
4. Evolutionary Dynamics: From Chaos to Structure, from Loops to Intelligence
Early stages will likely be chaotic:
- Random neural connections
- Meaningless behavior
- Frequent structural collapse
- Or stagnation in simple loops
These are not failures—they are the starting point of evolution.
When the system stagnates, we can introduce:
- Additional stimuli
- Increased environmental complexity
- Resource competition
- Extended time horizons
- New feedback dimensions
to break cycles and push evolution forward.
Over time, we may observe:
- Subnetwork replication
- Stabilization of local structures
- Longer behavioral sequences
- Emergence of simple preferences
- Improved recovery after perturbations
When these phenomena persist, we can consider the system to have reached:
The early form of “worm-level intelligence.”
5. Failure Modes and Elimination Mechanisms: An Open Design Space
Evolution may fail in many ways:
- Structural degradation
- Overactivation
- Structural freezing
- Excessive complexity
- Environmental mismatch
Elimination mechanisms should not be fixed in advance; they form part of the experimenter’s design space. Examples include:
- Energy depletion
- Ineffective behavior
- Structural instability
- Lower fitness relative to competitors
Different elimination rules may lead to different forms of intelligence.
6. Levels of Intelligence: Starting with Worm-Level and Expanding Gradually
This blueprint is not about “building AGI in one step.”
It is a staged exploration.
Stage 1: Worm-Level Intelligence (Core Goal)
- Simple preferences
- Homeostasis
- Behavioral consistency
- Recovery from perturbations
- Basic strategies
Stage 2: Small-Animal Intelligence (Optional Extension)
- Long-term memory
- Multi-objective behavior
- Simple planning
- Context switching
Stage 3: Higher Intelligence (Long-Term Exploration)
- World modeling
- Causal reasoning
- Internal simulation
Whether the system can reach mammalian-level intelligence is unknown and unnecessary to promise.
7. Value Along the Way: Extracting “Intelligent Structures” at Every Stage
Even if the system never surpasses worm-level intelligence, we can extract:
- Homeostatic control structures
- Behavioral consistency modules
- Preference modeling structures
- Simple planning mechanisms
- Environmental adaptation structures
These can be applied to:
- Smart home systems
- Small robots
- Environmental management
- Long-term consistent AI
- Automation systems
This path is not a gamble on AGI. It is:
A route that continuously produces usable intelligent building blocks.
8. Not a Procedure, but an Open Blueprint
To avoid constraining creativity, this blueprint intentionally avoids specifying:
- Concrete algorithms
- Specific parameters
- Exact environments
- Training procedures
Instead, it provides:
- Direction
- Framework
- Key concepts
- Design dimensions
- Possible pathways
Researchers can design their own experiments based on this blueprint.
9. Conclusion: Discussion and Exploration Welcome
The purpose of this proposal is not to provide definitive answers, but to:
- Offer a new research direction
- Provide a controllable framework for evolving intelligence
- Establish a path that yields value at every stage
- Create an open starting point for exploration
If this blueprint inspires experiments, papers, open-source projects, or educational tools, all the better.
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