Sublime

switches on, E. coli does not produce a smoothly increasing supply of the corresponding protein.

There is an additional element of importance: these systems usually generate implicit internal models of their environments, models progressively revised and improved as the system accumulates experience. The systems learn.

These are worlds that will be revealed not by better instruments but by new models and frameworks that allow us to see the familiar world in unfamiliar ways—to transform domains described into domains rigorously quantified and observations informally sensed into those formally understood.

This paradox of comprehension was articulated explicitly by a great physicist of an earlier age: “Sir Isaac Newton, when asked what he thought of the infatuations of the people, answered that he could calculate the motions of erratic bodies, but not the madness of a multitude” (quoted from The Church of England Quarterly Review, 1850).

Academic scientists, not politicians and bureaucrats, spurred the funding for and shaped the design of more-powerful computers, breakthroughs in artificial intelligence, and the Internet—a marvelous communication network of many nodes but no single command center.

He proved, and received the Nobel Prize for showing, that some systems can, in fact, develop in an upward spiral of ever-increasing complexity.