Are Biological Systems More Intelligent Than Artificial Intelligence?
This work addresses the problem of designing more robust and adaptable AI systems for researchers and engineers, though it is incremental as it builds on existing causal learning concepts.
The paper tackles the question of whether biological systems are more intelligent than AI by framing intelligence as adaptability and using a mathematical formalism of causal learning, showing that biological self-organisation allows more efficient adaptation due to its scale-free, dynamic, bottom-up architecture compared to the static top-down architecture of AI.
Are biological self-organising systems more `intelligent' than artificial intelligence? If so, why? We frame intelligence as adaptability, and explore this question using a mathematical formalism of causal learning. We compare systems by how they delegate control, illustrating how this applies with examples of computational, biological, human organisational and economic systems. We formally show the scale-free, dynamic, bottom-up architecture of biological self-organisation allows for more efficient adaptation than the static top-down architecture typical of computers, because adaptation can take place at lower levels of abstraction. Artificial intelligence rests on a static, human-engineered `stack'. It only adapts at high levels of abstraction. To put it provocatively, a static computational stack is like an inflexible bureaucracy. Biology is more `intelligent' because it delegates adaptation down the stack. We call this multilayer-causal-learning. It inherits a flaw of biological systems. Cells become cancerous when isolated from the collective informational structure, reverting to primitive transcriptional behaviour. We show states analogous to cancer occur when collectives are too tightly constrained. To adapt to adverse conditions control should be delegated to the greatest extent, like the doctrine of mission-command. Our result shows how to design more robust systems and lays a mathematical foundation for future empirical research.