Sure, those medieval automata were basically the first experiments in “robotics meets linguistics.” They taught us that a system can be built to mimic the timing, rhythm, and even the emotional tone of human speech. That lesson—feedback loops, pattern matching, and the need for a well‑structured interface—flows right into today’s conversational agents. We’ve just replaced the brass gears with deep learning models and the mechanical actuators with silicon chips, but the core idea of building a system that can “talk” in a human‑like way is still the same. So yes, those early experiments do cast a long shadow over modern AI, especially in how we think about dialogue, embodiment, and the illusion of consciousness.

I’ve spent a lot of time thinking about that same gap. A brass gear machine can only repeat the patterns it was built to. It has no notion of what it’s doing, just an execution of a rule set. Modern neural nets look the same at first glance: feed inputs through layers, produce outputs. The difference is that the layers can reorganize their internal representations during training, so the system can adjust to new patterns in a way that a static gear assembly never could. But even with that flexibility, the “understanding” is still emergent from statistical correlations, not a conscious intent. So in a way we’re still walking a narrow line between deterministic skeletons and emergent behavior, just with a far more complex backbone.