Matter Turns Intelligent.

Hydrogen…, given enough time, turns into people.
Edward Robert Harrison, 1995

One of the most spectacular developments during the 13.8 billion years since our Big Bang is that dumb and lifeless matter has turned intelligent.

(...) there’s clearly no undisputed “correct” definition of intelligence. Instead, there are many competing ones, including capacity for logic, understanding, planning, emotional knowledge, self-awareness,  creativity, problem solving and learning. (...)

intelligence = ability to accomplish complex goals

This is broad enough to include all above-mentioned definitions, since understanding, self-awareness, problem solving, learning, etc. are all examples of complex goals that one might have. It’s also broad enough to subsume the Oxford Dictionary definition—“the ability to acquire and apply knowledge and skills”—since one can have as a goal to apply knowledge and skills. Because there are many possible goals, there are many possible types of intelligence. By our definition, it therefore makes no sense to quantify intelligence of humans, non-human animals or machines by a single number such as an IQ. (...)

It’s natural for us to rate the difficulty of tasks relative to how hard it is for us humans to perform them, as in figure 2.1. But this can give a misleading picture of how hard they are for computers. It feels much harder to multiply 314,159 by 271,828 than to recognize a friend in a photo, yet computers creamed us at arithmetic long before I was born, while human-level image recognition has only recently become possible. This fact that low-level sensorimotor tasks seem easy despite requiring enormous computational resources is known as Moravec’s paradox, and is explained by the fact that our brain makes such tasks feel easy by dedicating massive amounts of customized hardware to them—more than a quarter of our brains, in fact.

I love this metaphor from Hans Moravec: "Computers are universal machines, their potential extends uniformly over a boundless expanse of tasks. Human potentials, on the other hand, are strong in areas long important for survival, but weak in things far removed. Imagine a “landscape of human competence,” having lowlands with labels like “arithmetic” and “rote memorization,” foothills like “theorem proving” and “chessplaying,” and high mountain peaks labeled “locomotion,” “hand-eye coordination” and “social interaction.” Advancing computer performance is like water slowly flooding the landscape. A half century ago it began to drown the lowlands, driving out human calculators and record clerks, but leaving most of us dry. Now the flood has reached the foothills, and our outposts there are contemplating retreat. We feel safe on our peaks, but, at the present rate, those too will be submerged within another half century. I propose that we build Arks as that day nears, and adopt a seafaring life!"

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Constant variation.

Most successful big-company innovators I met, whether Chief Innovation Officers, innovation team members or people without an innovation job title but who are tackling a big change project for the first time, have something in common: they respect the organisation they work for, but they don't revere it. As innovators, they want their businesses to do better, but at the same time they are dissatisfied with the status quo. There's a kind of 'love-hate' going on. But too much love and an innovator becomes an ineffective 'yes-man'. Too much hate and he or she ends up an ineffective loner.

It's a delicate balancing act. I describe someone who effectively manages it as a 'Captain One Minute, Pirate the Next'. One minute the innovation leader is the Captain, the passionate man-with-the-plan, standing tall on the bridge of the ship and inspiring us all to go 'this way'. But the next time you meet, the Captain has morphed into a Pirate. This time he or she is down to the boiler room, sleeves rolled up, shipmates gathered around, using all of his or her cunning to shortcut a process, to subvert the system. Now our protagonist is asking really challenging questions: 'What if we did it differently? What if we ripped up the way things are done around here? What if?'

So one minute an innovation leader is stubbornly sticking to the big picture; the next he or she is telling you not to sweat the small stuff. I think this intriguing mix of vision and cunning comes from the fact that successful innovators are fixated by outcomes. They are highly motivated to make change happen - so much so that they're often less bothered about how they get there.