The second machine age pdf

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To my parents, David McAfee and Nancy Haller, who prepared me for the second machine age by giving me every advantage a person could have. MIT Initiative on the Digital Economy. The Second. Machine Age. Erik Brynjolfsson. MIT Sloan School. Director, MIT Initiative on the Digital Economy. @ erikbryn. “Is Polanyi's paradox soon to be at least mostly overcome, in the sense that the vast majority of tasks will soon be automated? My reading of the evidence.

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The Second Machine Age Pdf

Erik Brynjolffson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (review). A. Bowdoin Van. PDF | On Nov 1, , Xiaojing Dong and others published The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant. PDF | On Nov 16, , Pericles asher Rospigliosi and others published Teaching in the second machine age.

It uses html tags, rather than italics, because it was formatted for web publication. Bowdoin Van Riper Human societies change continually, but the changes tend to be slow and incremental. Genuinely revolutionary changes—sudden, radical breaks with the status quo—are rare in human history. Deeper changes, which dissolve and re-form the foundations of the status quo, are rarer still. The great shift from nomadism to village agriculture was one; the emergence of cities, and all they entail, was another. The late-eighteenth-century substitution of steam power for wind, water, and muscle—the trigger for the Industrial Revolution and the First Machine Age—was a third. The First Machine Age began with the general-purpose steam engine, which was developed by James Watt between and , but took decades longer to mature, spread, and begin to transform society. The chip-based computer, the basis for the Second Machine Age, emerged in the late s and early s and likewise took decades to develop and spread. Computers, however, have grown more powerful, more quickly than steam engines—or any other technology in human history. Gordon Moore, the co-founder of Intel, observed in that the number of transistors that could fit on a computer chip doubled roughly every two years. Computers have, as a result of it, improved at speed unprecedented in the history of technology, radically increasing in power and flexibility while plummeting in size and price.

For those without a college degree, wages have fallen since Those with college degrees have seen their income remain relatively flat, while those with advanced degrees have fared much better.

It is this disparity of incomes by level of education that has been the most challenging for nations, as no longer is the economic tide raising all boats.

It is the double-edged sword of technological advancements that makes one wonder whether technology is good or bad. The Luddites certainly had their opinion on the matter, but today we celebrate new technologies. However, machines have not replaced all middle class jobs. To best understand how the second machine age has impacted the middle class, Daron Acemoglu and David Autor suggest that work can be divided into a two-by-two matrix: cognitive versus manual, and routine versus non-routine.

They found that the demand for work has been falling most dramatically for routine tasks, regardless of whether they are cognitive or manual. These are the jobs that can most easily be replaced by machines and lead to job polarization, a collapse in the demand for middle-income jobs. However, non-routine cognitive jobs financial analysts, doctors, professors, architects, etc.

What is evident in this second machine age is that the world we live in will require new tools for understanding and hopefully, addressing the societal and intellectual changes that technology is creating. Brynjolfsson and McAfee point out that we need a new way to measure our G. Likewise, our economic theories are in need of an objective and honest review, as they are becoming more political platforms to preserve power than genuine attempts to improve the welfare of communities.

We also need to improve how we educate our children. Our modern economy will provide few opportunities for those who are not educated, but boundless opportunities for those who are educated and can harness machines to increase their productivity.

It uses the familiar analogy of grains of rice on a chessboard 1 on the first square, 2 on the second, 4 on the third, 8 on the fourth, and so on , but draws an unfamiliar lesson. The thirty-second square, the authors note, holds a relatively comprehensible 2 million grains of rice.

The same eye for unexpected interpretations and telling examples is evident when they break new interpretive ground. Pointing out the fact that the digital economy is capable of creating value without creating jobs, they contrast Kodak and Instagram, two businesses built on enabling millions of customers to share billions of photographs.

Kodak, created in an age when photos were emulsion-on-paper objects, employed over , people directly a third of those in its Rochester, NY headquarters alone , and supported thousands more who were part of its network of dealers and distributors.

Instagram, created in an era where photographs were collections of digital information, had only fifteen. Later, arguing for educational reform, they draw a striking, suggestive parallel between large government bureaucracies which the existing system was designed to staff and vast simulated computers in which humans are the components. Brynjolffson and McAfee falter only when they shift, in the third section of the book, from analysis to advice and description to prescription.

Here, and only here, their scientific approach to the material—building their argument around powerful explanatory concepts, and taking real- world data seriously—fails them. We have decades worth of evidence, both academic and anecdotal, that partisan political views are essentially impervious to data. Likewise, large numbers of immigrants may well be—and have been shown to be—beneficial to the economy, boosting innovation without driving down wages, but immigration remains one of the most politically and culturally fraught issues in the twenty-first century United States.

An acknowledgement that "we don't pretend that the policies we advocate here will be easy to adopt" p. We are having more fun. Absolutely not. Telepho ne o perato rs went away in the s. We have failed to reco gnize that we are at a techno lo gical plateau and the trees are mo re bare than we wo uld like to think. GPTs are important because they are economically significant—they interrupt and accelerate the normal march of economic progress.

We are failing to understand why we are failing. To do this. With their typical verbal flair. We have been living o ff lo w-hanging fruit fo r at least three hundred years. Electricity gave a further boost to manufacturing by enabling individually powered machines. Yet during the last fo rty years. Mo re recent and thus mo re familiar was the rapid develo pment o f the web and e-co mmerce after Economic historian Gavin Wright offers a concise definition: The first perso nal co mputers arrived in the early s with their wo rd pro cessing.

In addition to agreeing on their importance. All o f these pro blems have a single. Most economic historians concur with the assessment that ICT meets all of the criteria given above.

We are also having more cheap fun. Gordon and Cowen see the invention of powerful technologies as central to economic progress.

Are we alone in thinking that information and communication technology ICT belongs in the same category as steam and electricity? Are we the only ones who think. So what do the data say here? Do the productivity numbers back up this pessimistic view of the power of digitization? Airline reservatio ns systems came in the s. Perhaps the mo st impo rtant ideas o f all are meta-ideas—ideas abo ut ho w to suppo rt the pro ductio n and transmissio n o f o ther ideas. Those benefits start small while the technology is immature and not widely used.

There was not a single unknown in the scheme. In this perspective. Another school of thought. While we humans are still the ones doing the driving. As he recounted in his Nobel Award speech. We co nsistently fail to grasp ho w many ideas remain to be disco vered. Po ssibilities do no t merely add up.

As he writes: Eco no mic gro wth o ccurs whenever peo ple take reso urces and rearrange them in ways that make them mo re valuable. When multiple GPTs appear at the same time. GPS system. And the more closely we look at how major steps forward in our knowledge and ability to accomplish things have actually occurred. As he summarizes in his book The Nature of Technology. Every generatio n has perceived the limits to gro wth that finite reso urces and undesirable side effects wo uld po se if no new.

the-second-machine-age-erik-brynjolfsson2.pdf

I thought. And every generatio n has underestimated the po tential fo r finding new. There are. Seco nd. Every step involved had been done already. We can mix and remix ideas. It was too easy. When the idea first came to him on a nighttime drive in California. Waze is a recombination of a location sensor.

After examining many examples of invention. The next great meta-idea. Like language. An invention like the steam engine or computer comes along and we reap economic benefits from it. The team at Waze invented none of these technologies. None of these elements was particularly novel.

In such a wo rld the co re o f eco no mic life co uld appear increasingly to be centered o n the mo re and mo re intensive pro cessing o f ever-greater numbers o f new seed ideas into wo rkable inno vatio ns.

Digitization makes available massive bodies of data relevant to almost any situation. Kevin Systrom and Mike Krieger decided to build a smartphone application that mimicked this capability. Facebook has built on the Web infrastructure by allowing people to digitize their social network and put media online without having to learn HTML.

The open source software advocate Eric Raymond has an optimistic observation: This progression drives home the point that digital innovation is recombinant innovation in its purest form. In the early stages o f develo pment. Whether or not this was an intellectually profound combination of technological capabilities.

One excellent way to do this is to involve more people in this testing process. As a result of these two forces. It extends into the physical one. As Weitzman writes. Their combination was revolutionary. From this perspective.

Erik-Brynjolfsson-Andrew-McAfee-Jeff-Cummings-The-Second-Machine-Age.pdf

Each development becomes a building block for future innovations. Limits to Recombinant Growth If this recombinant view of innovation is correct. This seems like a minor innovation. This model has a fascinating result: In recent years. Accuracy and plenty of advance warning are both important here. All fo ur submissio ns successfully achieved the required challenge o bjectives with differing scientific mechanisms.

Instead of scientific challenges. The experts were worried that the Kaggle crowd would simply not be competitive in the second round. Anyone can work on problems from any discipline. They also found that people whose expertise was far away from the apparent domain of the problem were more likely to submit winning solutions. Their hundreds of person-years of accumulated experience and expertise seemed like an insurmountable advantage over a bunch of novices.

NASA experienced this effect as it was trying to improve its ability to forecast solar flares. The first contest was to consist of two rounds. As it turned out. This phenomenon goes by several names. His recombination of theory and data earned him a thirty-thousand-dollar reward from the space agency.

Cragin said that. I had thought a lot about the theory of magnetic reconnection. Another interesting fact is that the majority of Kaggle contests are won by people who are marginal to the domain of the challenge—who. Here again. In many cases. Despite thirty-five years of research and data on SPEs. He was Bruce Cragin. Eleven established educational testing companies would compete against one another in the first round.

Between February and September of Kaggle hosted two competitions about computer grading of student essays. In one case. They found that the crowd assembled around Innocentive was able to solve forty-nine of them.

Jeppesen and Lakhani provide vivid examples of this: It was eventually so lved. 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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After all. Plenty of building blocks are in place. It does this by combining crowdsourcing with Nobel Prize—worthy algorithms. 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. People all over the world did. 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.

The standard approach to this kind of problem is for the design team to generate a few combinations that they think are good. These and countless other innovations will add up over time.

In both competitions. The company wanted to update the brand without sacrificing its strong reputation or downplaying its nine hundred years of history. One of its most successful products. And in the second competition. Affinnova offers a very different approach. The top three individual finishers were from. 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.

By the fall of It knew that the redesign would mean generating many candidates for each of several attributes —bottle shape. For Grimbergen. Unlike some of our colleagues. Quirky seeks ideas for new consumer products from its crowd. The surprises continued when Kaggle investigated who the top performers were.

Quirky itself makes the final decisions about which products to launch and handles engineering. It makes use of the mathematics of choice modeling. Auto matic grading o f essays wo uld bo th impro ve the quality o f educatio n and lo wer its co st.

Digital technologies are also restoring hearing to the deaf via cochlear implants and will probably bring sight back to the fully blind. But these remarkable advances pale against the life-changing potential of artificial intelligence. After winning Jeopardy!. Watson enrolled in medical school.

Either of these advances alone would fundamentally change our growth prospects. More substantive ones include automatically driving cars on the road. Our digital machines have escaped their narrow confines and started to demonstrate broad abilities in pattern recognition.

To be a bit more precise. How can we be so sure? Because the exponential. A user of the OrCam system. Artificial intelligence will not just improve lives. The huge amounts of information involved in modern medicine make this type of advance critically. When combined. Available Now Machines that can complete cognitive tasks are even more important than machines that can accomplish physical ones. Instead of volumes and volumes of general knowledge. These three forces are yielding breakthroughs that convert science fiction into everyday reality.

Soon countless pieces of AI will be working on our behalf. T hinking Machines. As it does. OrCam and similar innovations show that this is no longer the case. To take just one recent example. And thanks to modern AI we now have them. As it races ahead. The economist Julian Simon was one of the first to make this optimistic argument.

This theory further holds that people also play the vital role of filtering and improving the innovations of others. And the data on everything from air quality to commodity prices to levels of violence show improvement over time. This improvement is not a lucky coincidence.

The main impediment to progress has been that. It is a remarkable and unmistakable fact that. Our good ideas and innovations will address the challenges that arise.

Not only was this software at least as accurate as humans. The first mobile phones bought and sold in the developing world were capable of little more than voice calls and text messages. Between and the economist Robert Jensen studied a set of coastal villages in Kerala. Coming Soon In addition to powerful and useful AI.

A team led by pathologist Andrew Beck developed the C-Path computational pathologist system to automatically diagnose breast cancer and predict survival rates by examining images of tissue. He wrote. But fundamentally. These data. In the long run. In the industrialized West we have long been accustomed to having libraries. Things have gotten bet t er because there are more people.

Billions of Innovators. And this contribution is large enough in the long run to overcome all the costs of population growth. The World Bank estimates that three-quarters of the people on the planet now have access to a mobile phone. The theory of recombinant innovation stresses how important it is to have more eyeballs looking at challenges and more brains thinking about how existing building blocks can be rearranged to meet them.

That situation is rapidly changing. We do have one quibble with Simon. IBM estimates that it would take a human doctor hours of reading each and every week just to keep up with relevant new literature.

The journalist A. It will make mockery out of all that came before. They can follow online courses. Fish prices stabilized immediately after phones were introduced.

They can share their insights on blogs. The overall economic well-being of both downloaders and sellers improved. Liebling famously remarked that. Technology analysis firm IDC forecasts that smartphones will outsell feature phones in the near future. They can even conduct sophisticated data analyses using cloud resources such as site Web Services and R. The second machine age will be characterized by countless instances of machine intelligence and billions of interconnected brains working together to better understand and improve our world.

They can search the Web and browse Wikipedia. But most of it comes from increases in our ability to get more output from the given level of inputs —in other words. On television. Robert Solow got his Nobel Prize in Economics for showing that increases in labor input and capital input could not explain most of the increase in the total output of the economy. Simply working more hours does not increase productivity.

That rate of improvement is comparable for workers in Europe and Japan. While some still do. Part of it comes from using more resources. Most commonly. While this rise had a number of causes. Bob Solow himself noted that the slowdown seemed to coincide with the early days of the computer revolution. Americans would be five trillion dollars richer by In the United States.

Americans once routinely worked fifty. But when you zoom out and consider trends over the past century. University of Chicago economist Chad Syverson looked closely at the underlying productivity data and showed how eerily close this analogy is. Perhaps unsurprisingly. Paul David. GPTs always need complements. Exploiting all three dimensions. The new factories looked much like those we see today: The key to understanding this pattern is the realization that. While the technologies involved were very different.

While less visible. If the axle was too long the torsion involved would break it. Years later. But due in part to the power of digitization and networks to speed the diffusion of ideas. Instead of a single massive engine.

There might have been less smoke and a little less noise. Only after thirty years—long enough for the original managers to retire and be replaced by a new generation—did factory layouts change. Coming up with those can take years. As with earlier GPTs. Instead of putting the machines needing the most power closest to the power source. Less than ten years after its introduction.

Even when brand-new factories were built. In the late s. Part of the recent slowdown simply reflects the Great Recession and its aftermath. The good performance since the year was clustered in the early years of the decade. CVS found that their prescription drug ordering process was a source of customer frustration. Like earlier GPTs. American productivity growth in the decade following the year exceeded even the high growth rates of the roaring s.

The financial crisis and burst of the housing bubble led to a collapse of consumer confidence and wealth. While the recession technically ended in. In a statistical analysis of over six hundred firms that Erik did with Lorin Hitt. The real key was the introduction of complementary process innovations like vendor managed inventory.

Since We are not convinced by the pessimists. Kevin Stiroh of the New York Federal Reserve Bank found that industries that were heavier users of IT tended to be more productive throughout the s. CVS was not atypical. At the same time. They found that total factor productivity growth increased more between the s and s in IT-using industries. For instance. Walmart drove remarkable efficiencies in retailing by introducing systems that shared point-of-sale data with their suppliers.

Recessions are always times of pessimism. The productivity lull after the introduction of electricity did not mean the end of growth. This pattern was even more evident in recent years. A decade after the computer productivity paradox was popularized. This reflects the time and effort required to make the other complementary investments that bring a computerization effort success. As noted in chapter 5. The benefits of electrification stretched for nearly a century as more and more complementary innovations were implemented.

The explanation for this productivity surge is in the lags that we always see when GPTs are installed. Go back to figure 7. The digital GPTs of the second machine age are no less profound. During such a slump. As a result. Eco no mists so metimes use ano ther term fo r multifacto r pro ductivity. June Growth pessimists had even more company in the s than they do today. The mo re kinds o f inputs we are able to measure.

(Epub Download) The Second Machine Age - Work Progress and Prosperi…

In co ntrast. Fo r instance. It measures neither o ur wit no r o ur co urage. It measures everything. Analog dollars are becoming digital pennies.

They also call for new organizational structures. Free digital goods. Robert Kennedy put this poetically in his quote at the beginning of this chapter.

Bloomberg Businessweek. More and more what we care about in the second machine age are ideas. The trends in GDP growth and productivity growth covered in chapter 7 are important. How do we measure the benefits of free goods or services that were unavailable at any price in previous eras? Even the wealthiest robber baron would have been unable to download this service. So how did music disappear?

The value of music has not changed. Even when we include all digital sales. From to The resulting set of metrics have served as beacons that helped illuminate many of the dramatic changes that transformed the economy throughout the twentieth century. When a business traveler calls home to talk to her children via Skype. He had to rely on scattered data like freight car loadings. Sales of music on physical media declined from million units in to less than million units in Music is hiding itself from our traditional economic statistics.

Digital streams such as iTunes.

The great irony of this information age is that. By now.

The Second Machine Age by Erik Brynjolfsson Download PDF, EPUB

Yet over the same time period total units of music downloadd still grew. Similar economics apply when you read the New York Times. But as the economy has changed so.

Before the rise of the MP3. This leads to some very different economics and some special measurement problems. The first set of national accounts was presented to Congress in based on the pioneering work of Nobel Prize winner Simon Kuznets.

Department of Commerce. When her brother downloads a free gaming app on his iPad instead of downloading a new video game. Measuring Growth with a T ime Machine: Would You Rather. Or to make the comparison less difficult. They add value to the economy. Wikipedia alone claims to have over fifty times as much information as Encyclopaedia Britannica. It soon becomes clear that the trends in the official statistics not only underestimate our bounty.

Good for Well-Being. As we discussed in chapter 4. The official statistics are missing a growing share of the real value created in our economy. More people than ever are using Wikipedia. With a greater volume of digital goods introduced each year that do not have a dollar price. If one assumes that each additional unit of production created a similar increment in well-being. If the cost of creating and delivering an encyclopedia to your desktop is a few pennies instead of thousands of dollars.

Would you rather be able to download the bananas. Bad for GDP In some ways. According to the official measures. Can we improve on GDP as a measure of well-being?

And because our productivity data are. Suppose I gave you an expanded version of this catalog that listed all the goods and services available in A nation that sells more cars. When a girl clicks on a YouTube video instead of going to the movies. The U. But this decrease in costs lowers GDP even as our personal well-being increases. Unfortunately many economists. In addition to their vast library of music. If you had fifty thousand dollars to spend.

Consumer surplus compares the amount a consumer would have been willing to pay for something to the amount they actually have to pay. If you would happily pay one dollar to read the morning newspaper but instead you get it for free. This implies that they valued it more than the other ways they could spend their time. And if your income has not changed.

Yet as appealing as consumer surplus is as a concept. In contrast to leisure. If I have to pay you 20 percent more to make you just as happy shopping from the new catalog as you would be shopping from the old catalog. GDP growth—and thus productivity growth—would have been about 0. For other goods. In order to consume YouTube.

There are also some older goods and services that have been discontinued or degraded. Americans nearly doubled the amount of leisure time they spent on Internet between and On average it took about twenty-two minutes to answer a query without Google not counting travel time to the library!

Once you pick which catalog you like better. Recent research that Erik did with Joo Hee Oh. Or would you rather download the equivalent basket of services at prices? Bananas or a gallon of gasoline have not really changed qualitatively since Consumer Surplus: An alternative approach measures the consumer surplus generated by goods and services.

The difficulty in measuring the consumer surplus. If that were the only difference. None of this showed up in the GDP statistics but if it had.

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