Interesting Snippets

The consequences of all these changes, this report will argue, amount to a third industrial revolution. The first began in Britain in the late 18th century with the mechanisation of the textile industry. In the following decades the use of machines to make things, instead of crafting them by hand, spread around the world. The second industrial revolution began in America in the early 20th century with the assembly line, which ushered in the era of mass production. As manufacturing goes digital, a third great change is now gathering pace. It will allow things to be made economically in much smaller numbers, more flexibly and with a much lower input of labour, thanks to new materials, completely new processes such as 3D printing, easy-to-use robots and new collaborative manufacturing services available online. The wheel is almost coming full circle, turning away from mass manufacturing and towards much more individualised production. And that in turn could bring some of the jobs back to rich countries that long ago lost them to the emerging world.

The difference with live testing is not just that there is no time to learn and apply lessons. It’s more radical than that: There are no clear lessons to learn, no rules to extract. At the gaming network IGN, for example, executives found that crisp, clear prose was outperforming hyped-up buzzwords (like free and exclusive) on certain parts of the homepage. But in previous years, the opposite had been true. Why? They talked and talked about it, but no one could figure it out. Soon they realized that it simply didn’t matter. A/B would guide them at ground level, so there was no need to worry about why users behaved in one way or another.

There’ll likely still be applications that really need petroplastic, so landfills will become goldmines. The characteristic drawback of plastic, its stubborn resistance to degradation (‘this plastic bag will still be around in ten thousand years!’) will become a virtue, as it sits unchanged in anaerobic landfills waiting for us to decide that it’s worth excavating

if a modern-day MacBook Air operated at the energy efficiency of computers from 1991, its fully charged battery would last all of 2.5 seconds. Similarly, the world’s fastest supercomputer, Japan’s 10.5-petaflop Fujitsu K, currently draws an impressive 12.7 megawatts. That is enough to power a middle-sized town. But in theory, a machine equaling the K’s calculating prowess would, inside of two decades, consume only as much electricity as a toaster oven

The electrical efficiency of computing (the number of computations that can be completed per kilowatt-hour of electricity used) has … doubled every year and a half since the dawn of the computer age. Laptops and mobile phones owe their existence to this trend, which has led to rapid reductions in the power consumed by battery-powered computing devices. The most important future effect is that the power needed to perform a task requiring a fixed number of computations will continue to fall by half every 1.5 years (or a factor of 100 every decade). As a result, even smaller and less power-intensive computing devices will proliferate, paving the way for new mobile computing and communications applications that vastly increase our ability to collect and use data in real time.

Johnson and his team are currently working on futurecasting 2020. Obviously, it’s not done yet. But I asked if I could get a “rough draft” of what we should be looking for. And one of the things he’s modeling for the year 2020 is the “Secret Life of Data.” “Algorithms will talk to algorithms, machines will talk to machines, and humans won’t be involved. When data takes on a life of it’s own, what will that do? How will we remember that when that data comes back, it’s ultimately meant for humans? It has to make our lives better. We can’t forget that.” That’s not all, he adds. There’s also what he refers to as the “Ghost of Computing” – what happens when computers get so small that they disappear, and we have an entire world filled with computational intelligence?

“It sounds science-fictiony,” he laughs. “But it’s ultimately pragmatic. Chip designs have lead times of 5-10 years, so it’s important to have an understanding of how people will want to to interact with computers. I’m literally working on chips for 2020 right now.”…

Their teams work with ethnographers, social scientists, and others to understand the current state of the culture and try to figure out where it’s going.

The next step is then looking at the hardware. Johnson and his team work with computer scientists to look at the current state of the art in hardware, software, and algorithms, as well as the research coming up. The tech data is meshed with the social sciences data to answer a simple question: how can we apply this technology to capture people’s imaginations and make their lives better?

“At that point,” Johnson says. “I start to look at the trends. Which is really where most people start.”

Combining all of this data, Johnson then develops what he calls a “vision of the future” that his team can work to build.

It’s like an arms race to hire statisticians nowadays,” said Andreas Weigend, the former chief scientist at Amazon.com. “Mathematicians are suddenly sexy.” As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions