Feedback: The Other Tree
There is a second lineage in the story, and it is easy to miss. Cybernetics and control theory asked: how does a system steer itself toward a goal in a changing world? Their answer was feedback.
The vocabulary of sequential decisions
Control theory gave us the formal furniture of decision-making. A Markov decision process (MDP) describes a world as states, actions, transitions, and rewards — with the Markov property: the future depends only on where you are now, not how you got there. That stripped-down model is the foundation of modern reinforcement learning.
The thermostat is the canonical example, and it is genuinely instructive: a sensor, a target, an error signal, and a correction. No symbols, no search, no knowledge base — just a loop that closes.
Two trees, one forest
Symbolic AI reasoned about the world from the outside. Control theory acted inside it, adapting moment to moment. Modern AI is where the two trees finally braid: a model that represents the world and a loop that acts in it.