Emergent Models
Emergent Models is our proposal for an alternative machine learning framework.
Instead of relying on large, carefully engineered neural network architectures, we investigate whether task-solving behavior can emerge from simple, iterative, physics-inspired rules operating on minimal substrates such as cellular automata, trained through evolutionary processes.
More broadly, this line of research explores unconventional computing methods for machine learning.
Cellular Automata
Computation emerges from simple local update rules applied over time.
Evolutionary Training
Model behavior is shaped through evolutionary processes rather than standard gradient-based optimization.
Alternative Foundations
The goal is to explore a different foundation for machine learning, not just a variation of existing neural architectures.
Research
Supported through collaborations and early research funding.