6 ideas
A system can pursue a target by continuously measuring the gap between its current state and the goal, then feeding that error back to drive corrective action. This makes purposeful behavior a mechanical property: the machine 'aims' not by foresight but by reacting to the difference between where it is and where it should be.
The same mathematics of feedback and signal regulation governs both biological nervous systems and engineered machines, so control and communication are a single subject regardless of substrate. A muscle correcting its reach and a servomotor holding a course are instances of the same underlying mechanism.
Information is a measurable quantity inversely related to entropy: the more organized and improbable a message, the more information it carries. Communicating and computing are therefore acts of maintaining order against the universe's statistical drift toward disorder.
Malfunctions in goal-directed systems can be understood as feedback gone wrong rather than as missing parts. Excessive correction produces oscillation and overshoot, mirroring conditions like purpose tremor, so disorder is reframed as a problem of loop tuning rather than component failure.
When a signal is mixed with random noise, optimal future behavior is computed by statistically separating the predictable structure from the random part and extrapolating it forward. This treats forecasting and noise removal as the same statistical estimation problem, solvable by minimizing expected error.
Machines that sense, decide, and self-correct can replace not just muscular work but routine judgment, collapsing the economic value of repetitive mental and physical tasks. This shift demands deliberate social choices, because the technology itself is indifferent to whether displaced humans are cared for or discarded.