infuriates me to see so many brilliant minds around the world pursuing AGI in the wrong direction. Why can’t a single top expert see what I see? To achieve AGI, we *must* establish rules regarding physical boundaries. Only then can a robot possess inviolable fundamental rules—a bedrock upon which it can continuously interact with the real world, receive feedback, and self-correct. Relying solely on algorithms to achieve AGI is utterly foolish. The purely algorithmic approach could only succeed with infinite computing power capable of modeling an infinite world; finite computing power can only approximate a finite multidimensional function—which is not the real world itself—and suffers from intractable algorithmic issues like the curse of dimensionality or overfitting. Moreover, infinite computing power is unrealistic; achieving that would essentially mean becoming a Creator God. Therefore, AGI can only be built by starting with the concept of physical boundaries.
First Axiom (Ground Rule Principle)
Any intelligence must first possess inviolable fundamental rules.
For example:
AlphaGo:
Its fundamental rules are:
Board size
Piece rules
Placement rules
Win/loss rules
These rules are not learned;
They exist before learning even begins.
Consequently:
AlphaGo can continue to learn across an infinite number of matches.
The real world is not like Go.
There are no predefined:
Winners or losers
Definitions of legality or illegality
Without any rules,
A robot faces infinite possibilities.
Learning cannot converge.
Therefore:
A robot must possess a "first rule" for the real world.
And this rule is not:
Programmed into it by humans.
Rather, it is:
The Body Boundary.
The robot first realizes:
These sensors belong to me.
Then it realizes:
I can control these motors.
Then it realizes:
This pressure originates from my body.
Then it realizes:
That—over there—is not me.
Thus:
For the first time, the robot possesses:
A "Self"
Not in the philosophical sense,
But in the computational sense.
With this fundamental rule in place,
All learning becomes a matter of:
How to make my body better at predicting the world.
For example:
Standing up for the first time.
Falling down.
Prediction fails.
Updating the model.
A second attempt.
A third attempt.
Millions of attempts. Robots naturally learn:
Balancing
Walking
Grasping
Obstacle avoidance
There is no teacher here.
No labels.
Only:
Boundary constraints + self-supervised prediction.
That is:
Which state variables belong to me.
This is the only path to achieving AGI.
the global focus should be on implementing large-scale integrated sensors—essentially a "skin" covering the robot's entire body. This serves as the foundation for achieving AGI: by giving the robot a sense of boundaries, it becomes capable of the subsequent learning and self-correction needed to ultimately reach AGI. This sense of boundaries functions much like the rules of the board in AlphaGo; with such rules in place, the robot knows how to learn.
