The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (12 page)

Like Innocentive, the online startup Kaggle also assembles a diverse, non-credentialist group of people from around the world to work on tough problems submitted by organizations. Instead of scientific challenges, Kaggle specializes in data-intensive ones where the goal is to arrive at a better prediction than the submitting organization’s starting baseline prediction. Here again, the results are striking in a couple of ways. For one thing, improvements over the baseline are usually substantial. In one case, Allstate submitted a dataset of vehicle characteristics and asked the Kaggle community to predict which of them would have later personal liability claims filed against them.
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The contest lasted approximately three months and drew in more than one hundred contestants. The winning prediction was more than 270 percent better than the insurance company’s baseline.

Another interesting fact is that the majority of Kaggle contests are won by people who are marginal to the domain of the challenge—who, for example, made the best prediction about hospital readmission rates despite having no experience in health care—and so would not have been consulted as part of any traditional search for solutions. In many cases, these demonstrably capable and successful data scientists acquired their expertise in new and decidedly digital ways.

Between February and September of 2012 Kaggle hosted two competitions about computer grading of student essays, which were sponsored by the Hewlett Foundation.
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Kaggle and Hewlett worked with multiple education experts to set up the competitions, and as they were preparing to launch many of these people were worried. The first contest was to consist of two rounds. Eleven established educational testing companies would compete against one another in the first round, with members of Kaggle’s community of data scientists invited to join in, individually or in teams, in the second. The experts were worried that the Kaggle crowd would simply not be competitive in the second round. After all, each of the testing companies had been working on automatic grading for some time and had devoted substantial resources to the problem. Their hundreds of person-years of accumulated experience and expertise seemed like an insurmountable advantage over a bunch of novices.

They needn’t have worried. Many of the ‘novices’ drawn to the challenge outperformed all of the testing companies in the essay competition. The surprises continued when Kaggle investigated who the top performers were. In both competitions, none of the top three finishers had any previous significant experience with either essay grading or natural language processing. And in the second competition, none of the top three finishers had any formal training in artificial intelligence beyond a free online course offered by Stanford AI faculty and open to anyone in the world who wanted to take it. People all over the world did, and evidently they learned a lot. The top three individual finishers were from, respectively, the United States, Slovenia, and Singapore.

Quirky, another Web-based startup, enlists people to participate in both phases of Weitzman’s recombinant innovation—first generating new ideas, then filtering them. It does this by harnessing the power of many eyeballs not only to come up with innovations but also to filter them and get them ready for market. Quirky seeks ideas for new consumer products from its crowd, and also relies on the crowd to vote on submissions, conduct research, suggest improvements, figure out how to name and brand the products, and drive sales. Quirky itself makes the final decisions about which products to launch and handles engineering, manufacturing, and distribution. It keeps 70 percent of all revenue made through its website and distributes the remaining 30 percent to all crowd members involved in the development effort; of this 30 percent, the person submitting the original idea gets 42 percent, those who help with pricing share 10 percent, those who contribute to naming share 5 percent, and so on. By the fall of 2012, Quirky had raised over $90 million in venture capital financing and had agreements to sell its products at several major retailers, including Target and Bed Bath & Beyond. One of its most successful products, a flexible electrical power strip called Pivot Power, sold more than 373 thousand units in less than two years and earned the crowd responsible for its development over $400,000.

Affinnova, yet another young company supporting recombinant innovation, helps its customers with the second of Weitzman’s two phases: sorting through the possible combinations of building blocks to find the most valuable ones. It does this by combining crowdsourcing with Nobel Prize–worthy algorithms. When Carlsberg breweries wanted to update the bottle and label for Belgium’s Grimbergen, the world’s oldest continually produced abbey beer, it knew it had to proceed carefully. The company wanted to update the brand without sacrificing its strong reputation or downplaying its nine hundred years of history. It knew that the redesign would mean generating many candidates for each of several attributes—bottle shape, embossments, label color, label placement, cap design, and so on—then settling on the right combination of all of these. The ‘right’ combination from among the thousands of possibilities, however, was not obvious at the outset.

The standard approach to this kind of problem is for the design team to generate a few combinations that they think are good, then use focus groups or other small-scale methods to finalize which is best. Affinnova offers a very different approach. It makes use of the mathematics of choice modeling, an advance significant enough to have earned a Nobel Prize for its intellectual godfather, economist Daniel McFadden. Choice modeling quickly identifies people’s preferences—do they prefer a brown embossed bottle with a small label, or a green non-embossed one with a large label?—by repeatedly presenting them with a small set of options and asking them to select which they like best. Affinnova presents these options via the Web and can find the mathematically optimal set of options (or at least come close to it) after involving only a few hundred people in the evaluation process. For Grimbergen, the design that resulted from this explicitly recombinant process had an approval rating 3.5 times greater than that of the previous bottle.
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When we adopt the perspective of the new growth theorists and match it against what we see with Waze, Innocentive, Kaggle, Quirky, Affinnova, and many others, we become optimistic about the current and future of innovation. And these digital developments are not confined to the high-tech sector—they’re not just making computers and networks better and faster. They’re helping us drive our cars better (and may soon make it unnecessary for us to drive at all), allowing us to arrive at better predictions of solar flares, solving problems in food science and toxicology, and giving us better power strips and beer bottles. These and countless other innovations will add up over time, and they’ll keep coming and keep adding up. Unlike some of our colleagues, we are confident that innovation and productivity will continue to grow at healthy rates in the future. Plenty of building blocks are in place, and they’re being recombined in better and better ways all the time.

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In reality, many of the countries that
do
have large amounts of mineral and commodity wealth are often crippled by the twin terrors of the “resource curse”: low growth rates and lots of poverty.

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Some have tied the invention of the cotton gin to increased demand for slave labor in the American South and therefore to the Civil War, but its direct economic effect outside the textile industry was minimal.

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Keep in mind that if there are only fifty-two seed ideas in such an economy, they have many more potential combinations than there are atoms in our solar system.

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Improvements in this area are important because essays are better at capturing student leaning than multiple-choice questions, but much more expensive to grade when human raters are used. Automatic grading of essays would both improve the quality of education and lower its cost.

“And here I am thinking of those astonishing electronic machines . . . by which our mental capacity to calculate and combine is reinforced and multiplied by the process and to a degree that herald . . . astonishing advances.”

—Pierre Teilhard de Chardin

T
HE
PREVIOUS
FIVE
CHAPTERS
laid out the outstanding features of the second machine age: sustained exponential improvement in most aspects of computing, extraordinarily large amounts of digitized information, and recombinant innovation. These three forces are yielding breakthroughs that convert science fiction into everyday reality, outstripping even our recent expectations and theories. What’s more, there’s no end in sight.

The advances we’ve seen in the past few years, and in the early sections of this book—cars that drive themselves, useful humanoid robots, speech recognition and synthesis systems, 3D printers,
Jeopardy!
-champion computers—are not the crowning achievements of the computer era. They’re the warm-up acts. As we move deeper into the second machine age we’ll see more and more such wonders, and they’ll become more and more impressive.

How can we be so sure? Because the exponential, digital, and recombinant powers of the second machine age have made it possible for humanity to create two of the most important one-time events in our history: the emergence of real, useful artificial intelligence (AI) and the connection of most of the people on the planet via a common digital network.

Either of these advances alone would fundamentally change our growth prospects. When combined, they’re more important than anything since the Industrial Revolution, which forever transformed how physical work was done.

Thinking Machines, Available Now

Machines that can complete cognitive tasks are even more important than machines that can accomplish physical ones. And thanks to modern AI we now have them. Our digital machines have escaped their narrow confines and started to demonstrate broad abilities in pattern recognition, complex communication, and other domains that used to be exclusively human.

We’ve also recently seen great progress in natural language processing, machine learning (the ability of a computer to automatically refine its methods and improve its results as it gets more data), computer vision, simultaneous localization and mapping, and many of the other fundamental challenges of the discipline.

We’re going to see artificial intelligence do more and more, and as this happens costs will go down, outcomes will improve, and our lives will get better. Soon countless pieces of AI will be working on our behalf, often in the background. They’ll help us in areas ranging from trivial to substantive to life changing. Trivial uses of AI include recognizing our friends’ faces in photos and recommending products. More substantive ones include automatically driving cars on the road, guiding robots in warehouses, and better matching jobs and job seekers. But these remarkable advances pale against the life-changing potential of artificial intelligence.

To take just one recent example, innovators at the Israeli company OrCam have combined a small but powerful computer, digital sensors, and excellent algorithms to give key aspects of sight to the visually impaired (a population numbering more than twenty million in the United States alone). A user of the OrCam system, which was introduced in 2013, clips onto her glasses a combination of a tiny digital camera and speaker that works by conducting sound waves through the bones of the head.
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If she points her finger at a source of text such as a billboard, package of food, or newspaper article, the computer immediately analyzes the images the camera sends to it, then reads the text to her via the speaker.

Reading text ‘in the wild’—in a variety of fonts, sizes, surfaces, and lighting conditions—has historically been yet another area where humans outpaced even the most advanced hardware and software. OrCam and similar innovations show that this is no longer the case, and that here again technology is racing ahead. As it does, it will help millions of people lead fuller lives. The OrCam costs about $2,500—the price of a good hearing aid—and is certain to become cheaper over time.

Digital technologies are also restoring hearing to the deaf via cochlear implants and will probably bring sight back to the fully blind; the FDA recently approved a first-generation retinal implant.
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AI’s benefits extend even to quadriplegics, since wheelchairs can now be controlled by thoughts.
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Considered objectively, these advances are something close to miracles—and they’re still in their infancy.

Artificial intelligence will not just improve lives; it will also save them. After winning
Jeopardy!
, for example, Watson enrolled in medical school. To be a bit more precise, IBM is applying the same innovations that allowed Watson to answer tough questions correctly to the task of helping doctors better diagnose what’s wrong with their patients. Instead of volumes and volumes of general knowledge, the supercomputer is being trained to sit on top of all of the world’s high-quality published medical information; match it against patients’ symptoms, medical histories, and test results; and formulate both a diagnosis and a treatment plan. The huge amounts of information involved in modern medicine make this type of advance critically important. IBM estimates that it would take a human doctor 160 hours of reading each and every week just to keep up with relevant new literature.
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