Intuition Pumps And Other Tools for Thinking

It turns out that all the “magic” of cognition depends, just as life itself does, on cycles within cycles of recurrent, “re-entrant,” reflexive information-transformation processes ranging from the nano-scale biochemical cycles within each neuron, through the generate-and-test cycles of predictive coding in the perceptual systems (see Clark, 2013,

Attributed to William of Ockham (or Occam), the fourteenth-century logician and philosopher, this thinking tool is actually a much older rule of thumb. A Latin name for it is lex parsimoniae, the law of parsimony. It is usually put into English as the maxim “Do not multiply entities beyond necessity.” The idea is straightforward: don’t concoct a co

Every increment of design in the universe begins with a moment of serendipity, the undesigned intersection of two trajectories yielding something that turns out, retrospectively, to be more than a mere collision.

In a computer model of creativity there should be junk lying around that your creative processes can bump into, noises that your creative processes can’t help overhearing. The spontaneous intrusion of that little noise from the next room may tweak what those processes are doing in a way that is serendipitous, or in a way that is destructive, but ei

Sturgeon’s Law is usually put a little less decorously: Ninety percent of everything is crap. Ninety percent of experiments in molecular biology, 90 percent of poetry, 90 percent of philosophy books, 90 percent of peer-reviewed articles in mathematics—and so forth—is crap.

Imagine you’ve written a chess program, and you feed its source code to two different compilers. Then play the two compiled versions against each other on the same computer. Even though the two versions “think all the same thoughts in the same order” (they have to—they have exactly the same source code), one may always beat the other simply because

Sometimes you don’t just want to risk making mistakes; you actually want to make them—if only to give you something clear and detailed to fix. Making mistakes is the key to making progress.

Figuring out what to credit and what to blame is one of the knottiest problems in AI, and it is also a problem faced by natural selection.

Try to acquire the weird practice of savoring your mistakes, delighting in uncovering the strange quirks that led you astray. Then, once you have sucked out all the goodness to be gained from having made them, you can cheerfully set them behind you, and go on to the next big opportunity.

What is all this noise for? It’s not for anything; it’s just there so that every other process has that noise as a potential source of signal, as something that it might turn, by the alchemy of the creative algorithm, into function, into art, into meaning.