Macheng Shen
I think about intelligence as closed-loop, multiscale competence in finite physical systems: how useful structure is acquired, how control is maintained, and how learning signals move through real substrates.
Featured Essay (Full Text)
This PDF is the current overview of the broader agenda: intelligence as a finite physical system that builds, updates, and deploys internal structure sufficient for prediction, intervention, and control.
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Research Map
Toward a theory of intelligence
The core question is what sort of object intelligence is. The working answer is not “static function approximation,” but multiscale closed-loop competence under physical, informational, and control constraints.
Line loss, viability, and distributed cognition
These essays develop the higher-level language: physical budgets, coordination costs, endogenous viability, and why multiscale organization keeps appearing in biological and artificial systems.
Credit transport and wave-inspired learning
This branch asks a narrower question: how does task-relevant update information move inside a learning system? Some physical media can compute with fields and even measure gradients in situ; cortex may use its own cousin of this idea.
Original Raw Prompt
I am still sharing the original prompt for reproducibility. It was useful as a seed, but several claims on the site have since been revised into a more careful layered framing.
About
I am trying to find a language that can describe intelligent behavior across biology and AI without collapsing everything into current engineering jargon. That means connecting learning, control, multiscale feedback, physical implementation, and safety.