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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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.
We are entering a second machine age. This in itself should not be too surprising or alarming. When we phrase it that way—in the dry vocabulary of economics—it almost sounds unappealing. As we stress in our concluding chapter. Meso po tamia. Because he was interested in co mpariso ns between the West Euro pe. This is a troubling development for many reasons.
The final section—chapters 12 through 15—discusses what interventions will be appropriate and effective for this age. In Our economic goals should be to maximize the bounty while mitigating the negative effects of the spread.
Ho wever. Overall so cial develo pment is simply the sum o f these fo ur numbers. We average the two sco res. Spread has been increasing in recent years. This same perio d is called by o thers the Seco nd Industrial Revo lutio n. Each o f these is co nverted into a number that varies o ver time fro m zero to By the time we pulled back into the parking lot. Even in the lowest-wage countries there are no human computers.
If you examine their inner workings. Their circuitry can be interpreted in the language of ones and zeroes. The is not always a predictable or calm environment. When one of the Googlers hit the button that switched the car into fully automatic driving mode while we were headed down Highway The car recognized all the surrounding vehicles. Initially we had visions of cruising in the back seat of a car that had no one in the front seat.
The word was originally a job title. Why not? What is it about their work that makes it harder to digitize than what human computers used to do? Since we were now part of the ongoing Chauffeur experiment. But the software doing the driving was aware that cars and trucks driven by humans do have blind spots. At highway speeds the consequences of driving mistakes can be serious ones. Back then. The autonomous car braked smoothly in response. Their hundreds of hours in the car had convinced them that it could handle a little stop-and-go traffic.
Excellent research and analysis. So we sat in the back while two members of the Chauffeur team rode up front. In their book Levy and Murnane offered a way to think about which tasks fell into each category. In any sensible economic system.
But digital novelists are not yet available. In principle. The car performed flawlessly. IN How they reached this conclusion. Computers in the early twentieth century were people. Doing so might freak out pedestrians and other drivers.
Over the course of decades. It was a car without blind spots. All the while the Googlers in the front seat never stopped their conversation or showed any nervousness. We were staring at the screen. According to the authors. Levy and Murnane go on to highlight other types of knowledge work that can also be expressed as rules.
One occurred when a person was driving the Chauffeur car. Using his kno wledge. Algorithms do. Since computers are really good at following rules. At one end are tasks like arithmetic that require only the application of well-understood rules. So the decision about whether or not to give someone a mortgage can be effectively boiled down to a rule.
But articulating this kno wledge and embedding it in so ftware fo r all but highly structured situatio ns are at present eno rmo usly difficult tasks. Computers Are Good at Following Rules.
The results were less than encouraging. By the time we took our ride in the summer of the Chauffeur project had grown into a small fleet of vehicles that had collectively logged hundreds of thousands of miles with no human involvement and with only two accidents. Expressed in words. Google announced in an October blog post that its completely autonomous cars had for some time been driving successfully.
Algorithms are simplifications. The authors cite driving a vehicle in traffic as an example of such as task. As the philosopher Michael Polanyi famously observed. Fifteen entrants performed well enough in a qualifying run to compete in the main event. In the agency announced its first Grand Challenge. Our brains are extraordinarily good at taking in information via our senses and examining it for patterns. The truck driver [has] the schema to reco gnize what [he is] co nfro nting. As they write.
Co mputers canno t easily substitute fo r humans in [jo bs like driving]. The authors put information processing tasks—the foundation of all knowledge work—on a spectrum. As the driver makes his left turn against traffic.
Then in the past few years it became sudden. After a meeting in San Francisco. Responded with directions to the company Lake Superior X-Ray. The software underlying Siri.
You can also make OpenTable reservations. After Siri had been out for about eight months. Cutting-edge research explaining why they were not coming anytime soon was outpaced by cutting-edge science and engineering that brought them into existence.
Kyle Wagner of technology blog Gizmodo listed some of its most useful capabilities: This science and engineering accelerated rapidly. Chauffeur car.
Levy and Murnane highlight complex communication as a domain that would stay on the human side in the new division of labor. It can come to your aid exactly when you need it. Good Listeners and Smooth T alkers In addition to pattern recognition. Self-driving cars went from being the stuff of science fiction to on-the-road reality in a few short years. In these cases. Progress on some of the oldest and toughest challenges associated with computers.
Confronted with an Escherian maze of elevated highways. This is the kind of everyday task for which voice commands can actually be incredibly useful. They write that. On the same trip that afforded us some time in an autonomous car. Just when our meeting at Google. In other words.
We had a portable GPS device with us. Responded with a movie theater search on Yelp.
Just with the obvious Siri criterion: If it actually works. Please try again in a little while. Computers are now combining pattern matching with complex communication to quite literally beat people at their own games. We wanted to speak a question and hear and see because a map was involved a reply. Questions cover a wide range of topics. Almost everyone knows the answers to some of the questions in a given episode. There is less chance you could have made progress translating this sentence or any other into Hungarian.
Apple and other companies have made robust natural language processing technology available to hundreds of millions of people via their mobile phones. A person who speaks more than one language can usually translate between them with reasonable accuracy.
In an initial trial. Siri provided exactly the natural language interaction we were looking for. Even if your French is rusty. It was a supercomputer called Watson. Players also have to be simultaneously fast. But Google will attempt a translation of text from any of these languages into any other. Automatic translation services. And in tasks like translation from one language to another. We could have pulled over. Responding correctly to clues like these requires mastery of pattern matching and complex communication.
And winning at Jeopardy! During the shows, Watson competed against Ken Jennings and Brad Rutter, two of the best knowledge workers in this esoteric industry. Jennings won Jeopardy! Lickel saw that a match between Jennings and a Jeopardy! Since Jeopardy! In December , shortly after the project started, when Watson was tuned to try to answer 70 percent of the time a relatively aggressive approach it was only able to come up with the right response approximately 15 percent of the time.
Jennings, in sharp contrast, answered about 90 percent of questions correctly in games when he buzzed in first in other words, won the right to respond 70 percent of the time. The Watson team kept working until mid-January of , when the matches were recorded for broadcast in February, but no one knew how well their creation would do against Jennings and Rutter.
Watson trounced them both. Watson was also extremely fast, repeatedly buzzing in before Jennings and Rutter to win the right to answer questions. In the first of the two games played, for example, Watson buzzed in first 43 times, then answered correctly 38 times.
Jennings and Rutter combined to buzz in only 33 times over the course of the same game. A final important area where we see a rapid recent acceleration in digital improvement is robotics—building machines that can navigate through and interact with the physical world of factories, warehouses, battlefields, and offices.
Here again we see progress that was very gradual, then sudden. The word robot entered the English language via the Czech play, R. A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. But one of those two communities has raced far ahead of the other.
At a presentation to a live audience in Tokyo, ASIMO attempted to walk up a shallow flight of stairs that had been placed on the stage. As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.
More than ten million Roombas have been sold, but none of them is going to straighten the magazines on a coffee table. When it comes to work in the physical world, humans also have a huge flexibility advantage over machines. For example, the circuit board must show up in exactly the same orientation every time. Companies download specialized machines for tasks like these, have their engineers program and test them, then add them to their assembly lines. Each time the task changes—each time the location of the screw holes move, for example— production must stop until the machinery is reprogrammed.
Rethinking Factory Automation Rodney Brooks, who co-founded iRobot, noticed something else about modern, highly automated factory floors: And a lot of the work they do is repetitive and mindless.
On a line that fills up jelly jars, for example, machines squirt a precise amount of jelly into each jar, screw on the top, and stick on the label, but a person. In Brooks founded a new company, Rethink Robotics, to pursue and build untraditional industrial automation: Baxter is instantly recognizable as a humanoid robot. To train Baxter, you grab it by the wrist and guide the arm through the motions you want it to carry out. These and many other design features make working with this automaton a natural, intuitive, and nonthreatening experience.
When we first approached it, we were nervous about catching a robot arm to the face, but this apprehension faded quickly, replaced by curiosity. Most conveyor belts and assembly lines do not operate at full human speed; they would tire people out if they did. Baxter has a few obvious advantages over human workers.
It can work all day every day without needing sleep, lunch, or coffee breaks. And it can do two completely unrelated things at once; its two arms are capable of operating independently. Kiva robots look like metal ottomans or squashed R2-D2s.
They scuttle around buildings at about knee-height, staying out of the way of humans and one another. After these workers grab the products they need, the robot whisks the shelf away and another shelf-bearing robot takes its place. Software tracks where all the products, shelves, robots, and people are in the warehouse, and orchestrates the continuous dance of the Kiva automatons.
The company builds robots aimed at supporting American troops in the field by, among other things, carrying heavy loads over rough terrain. Its BigDog, which looks like a giant metal mastiff with long skinny legs, can go up steep hills, recover from slips on ice, and do other very dog-like things. Balancing a heavy load on four points while moving over an uneven landscape is a truly nasty engineering problem, but Boston Dynamics has been making good progress.
As a final example of recent robotic progress, consider the Double, which is about as. The primary technical go al o f the DRC is to develo p gro und ro bo ts capable o f executing co mplex tasks in dangero us.
And these innovations are not just lab demos. On Star Trek. Instead of trotting through rough enemy terrain. As many other examples show. Many saw the Grand Challenge as instrumental in accelerating progress with autonomous vehicles.
The camera. DARPA is asking the robotics community to build and demonstrate highfunctioning humanoid robots by the end of Co mpetito rs in the DRC are expected to fo cus o n ro bo ts that can use standard to o ls and equipment co mmo nly available in human enviro nments. These stories are all generated by algorithms without human involvement. They enable their users to simultaneously access and generate huge amounts of information as they move around. According to an initial specification supplied by the agency.
The Double itself acts as the legs. On the Star Trek television series. They also work as media players.
In recent times they have been able to generate grammatically correct but meaningless sentences. The co nsensus estimate remains unchanged o ver the past mo nth. Instead of just putting ink on paper. At the San Francisco headquarters of Autodesk. The answer was that a laser had built up each layer by fusing powdered metal. But then digital progress became sudden after being gradual for so long.
In particular. None of them could figure out how the lattice was produced. This happened in multiple areas. The Wall Street co nsensus is 80 cents per share. And instead of ink. Analysts pro ject revenue to fall 0. Trebek reads answers and the co ntestants have to state the questio n that wo uld give rise to this answer. The bowl had an elaborate lattice pattern on its sides. Heinz A quality first quarter earnings anno uncement co uld push shares o f H. The next three chapters will discuss each of these in turn.
Innovators reasoned that there is nothing stopping printers from depositing layers one on top of the other. Was it a fluke—a confluence of a number of lucky one-time advances? Each layer is very thin—somewhere around onetenth of a millimeter—but over time a three-dimensional object takes shape. In the near future. Demonstration projects have even shown that the technique could be used to build concrete houses.
Fo r the year. Autodesk CEO Carl Bass is part of the large and growing community of additive manufacturing hobbyists and tinkerers. In o ther wo rds. Fo rbes Earning Preview: And because of the way it is built up.
J repo rted earnings o f 78 cents per share. August The co mplexity fo r minimum co mpo nent co sts has increased at a rate o f ro ughly a facto r o f two per year. Moore predicted this state of affairs would continue. Gordon Moore is best known for a prediction he made. There was no period of time when cars got twice as fast or twice as fuel efficient every year or two for fifty years. So how has the computer industry kept up this amazing pace of improvement? There are two main reasons.
He wrote that. Moore observed that over the relatively brief history of his industry this amount had doubled each year: This bold statement forecast circuits that would be more than five hundred times as powerful in as they were in Certainly o ver the sho rt term this rate can be expected to co ntinue.
When it became difficult to cram integrated circuits more tightly together. In Moore revised his estimate upward from one year to two. When communications traffic threatened to outstrip the capacity even of fiber-optic cable. They have to do with how many electrons per second can be put through a channel etched in an integrated circuit. Those laws describe how the universe works. Henry Samueli. Over the lo nger term. Logarithmic graphs have a wonderful property: To make it more obvious.
Exponential growth like this is fast growth. As Intel executive Mike Marberry puts it. Think of it as a steady drumbeat in the background of the economy. Over and over again brilliant tinkering has found ways to skirt the limitations imposed by physics. To highlight it better. Imagine that Erik gives Andy a tribble. Every day each tribble gives birth to another tribble.
But the same phenomenon—the daily doubling of tribbles—has been going on the whole time with no accelerations or disruptions. It seems to show that all the action occurs in the last couple of days. This is fine for many purposes but.
Charting the Power of Constant Doubling Once this doubling has been going on for some time. To see this. A geek would say in this case that the tribble family is experiencing exponential growth.
This view emphasizes the steadiness of the doubling over time rather than the large numbers at the end. He aims not only to illustrate the power of sustained exponential growth but also to highlight the point at which the numbers start to become so big they are inconceivable: After thirty-two squares.
We also lose sense of how quickly numbers like these appear as exponential growth continues.
The Second Machine Age (Summary)
The game of chess originated in present-day India during the sixth century CE. In the second half of the chessboard. It was as they headed into the seco nd half o f the chessbo ard that at least o ne o f them go t into tro uble. Because of this. And the invento r co uld still retain his head. Inventor and futurist Ray Kurzweil retells an old story to drive this point home.
Kurzweil tells the story of the inventor and the emperor in his book The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Impoverished Emperors. Four billion does not necessarily outstrip our intuition. Among many other things. In some versions of the story. But the empero r co uld still remain an empero r. A pile of rice this big would dwarf Mount Everest. They show up as straight lines and their speed is easier to evaluate. The ruler was so impressed by the difficult.
If his request were fully honored. Headless Inventors. We experience it when harvesting grain. After thirty-two of these doublings, U. That was in Of course, this calculation is just a fun little exercise, not anything like a serious attempt to identify the one point at which everything changed in the world of corporate computing. You could easily argue with the starting point of and a doubling period of eighteen months.
Changes to either assumption would yield a different break point between the first and second halves of the chessboard. We present this calculation here because it underscores an important idea: In other words, things get weird in the second half of the chessboard.
And like the emperor, most of us have trouble keeping up. One of the things that sets the second machine age apart is how quickly that second half of the chessboard can arrive. But the exponents were relatively small, so it only went through about three or four doublings in efficiency during that period.
In the second machine age, the doublings happen much faster and exponential growth is much more salient. The innovations we described in the previous chapter—cars that drive themselves in traffic; Jeopardy! What does digital progress look like on a logarithmic scale? And there would really be no way to graph them all together—the numbers involved are just too different. Logarithmic scaling takes care of these. To reach this speed it used eight hundred kilowatts per hour, about as much as eight hundred homes would.
By , it had reached 1. Nine years later another computer hit 1. But instead of simulating nuclear explosions, it was devoted to drawing them and other complex graphics in all their realistic, real-time, three-dimensional glory. It did this not for physicists, but for video game players.
The Second Machine Age by Erik Brynjolfsson Download PDF, EPUB
This computer was the Sony PlayStation 3, which matched the ASCI Red in performance, yet cost about five hundred dollars, took up less than a tenth of a square meter, and drew about two hundred watts. The PlayStation 3 sold approximately 64 million units. Exponential progress has made possible many of the advances discussed in the previous chapter. As all of these innovations show, exponential progress allows technology to keep racing ahead and makes science fiction reality in the second half of the chessboard.
Not Just for Computers Anymore: As is the case with the ASCI Red and the PlayStation 3, the Cray2 supercomputer introduced in and iPad 2 tablet introduced in had almost identical peak calculation speeds.
But the iPad also had a speaker, microphone, and headphone jack. It had two cameras; the one on the front of the device was Video Graphics Array VGA quality, while the one on the back could capture high-definition video. Both could also take still photographs, and the back camera had a 5x digital zoom.
The tablet had receivers that allowed it to participate in both wireless telephone and Wi-Fi networks. It also had a GPS receiver, digital compass, accelerometer, gyroscope, and light sensor. It had no builtin keyboard, relying instead on a high-definition touch screen that could track up to eleven points of contact simultaneously. They became, in essence, computer chips.
Digital gear for recording sounds was in use by the s, and an Eastman Kodak engineer built the first modern digital camera in And when it introduced a new iPad the following year, the rear. It also allows them to do things that previously seemed out of reach. Researchers in artificial intelligence have long been fascinated some would say obsessed with the problem of simultaneous localization and mapping, which they refer to as SLAM.
For the great majority of humans, SLAM happens with minimal conscious thought. But teaching machines to do it has been a huge challenge. Researchers thought a great deal about which sensors to give a robot cameras? In November Microsoft first offered the Kinect sensing device as an addition to its Xbox gaming platform.
The Kinect could keep track of two active players, monitoring as many as twenty joints on each. It accomplished this with digital sensors including a microphone array which pinpointed the source of sound better than a single microphone could , a standard video camera, and a depth perception system that both projected and detected infrared light. Several onboard processors and a great deal of proprietary software converted the output of these sensors into information that game designers could use.
The Kinect sold more than eight million units in the sixty days after its release more than either the iPhone or iPad and currently holds the Guinness World Record for the fastestselling consumer electronics device of all time. SIGGRAPH is the largest and most prestigious gathering devoted to research and practice on digital graphics, attended by researchers, game designers, journalists, entrepreneurs, and most others interested in the field.
In a video shown at SIGGRAPH , a person picks up a Kinect and points it around a typical office containing chairs, a potted plant, and a desktop computer and monitor. In real time, Kinect draws a three-dimensional map of the room and all the objects in it, including a coworker. The device knows where it is in the room at all times, and even knows how virtual ping-pong balls would bounce around if they were dropped into the scene.
This rig. Cheap and powerful digital sensors are essential components of some of the science-fiction technologies discussed in the previous chapter. A Google autonomous car incorporates several sensing technologies. David Hall. Sometimes a difference in degree in other words. The story of the second half of the chessboard alerts us that we should be aware that enough exponential progress can take us to astonishing places. It generates about 1.
At the end of the paper describing their work. A self-hack is o ne carried o ut by the co mpany that made the gear in the first place. With Kintinuous. In less than a year. All of these would have been unworkably expensive. The Baxter robot has multiple digital cameras and an array of force and position detectors.
When given to capable technologists. Traditionally, such cool hunting ended with the name of a band, restaurant, place to visit, TV show, book, or movie. In the digital age, sentences like these frequently end with the name of a website or a gadget. Our cars have navigation systems and our iPhones can give driving directions through the Maps application. We could not see a need for yet another howdo-I-get-there technology. As Matt patiently explained, using Waze is like bringing a Ducati to a drag race against an oxcart.
As the company website explains: The idea fo r Waze o riginated years ago , when Ehud Shabtai. Ehud to o k matters into his o wn hands. His go al? To accurately reflect the ro ad system, state o f traffic and all the info rmatio n relevant to drivers at any given mo ment. Yes, they know your precise location thanks to a network of twenty-four geosynchronous GPS satellites built and maintained by the U. They also know about roads—which ones are highways, one-way streets, and so on—because they have access to a database with this information.
The things a driver really wants to know about—traffic jams, accidents, road closures, and other factors that affect travel time—escape a traditional system. This route will include major roads and highways, since they have the highest speed limits. Andy should instead seek out all the sneaky little back roads that longtime commuters know about. Even if he starts out on back roads, his well-meaning GPS will keep rerouting him onto the highway. Shabtai recognized that a truly useful GPS system needed to know more than where the car was on the road.
It also needed to know where other cars were and how fast they were moving. When the first smartphones appeared he saw an opportunity, founding Waze in along with Uri Levine and Amir Shinar.
As more and more smartphones run the application, therefore, Waze gets a more and more complete sense of how traffic is flowing throughout a given area. Instead of just a static map of roads, it also has always current updates on traffic conditions.
Its servers use the map, these updates, and a set of sophisticated algorithms to generate driving directions. That Waze gets more useful to all of its members as it gets more members is a classic example of what economists call a network effect—a situation where the value of a resource for each of its users increases with each additional user. In July of the company reported that it had doubled its user base to twenty million people in the previous six months.
Waze makes GPS what it should be for drivers: It instantly turns you into the most knowledgeable driver in town. The service relies on vast numbers of powerful but cheap devices the smartphones of its users , each of them equipped with an array of processors, sensors, and transmitters. It only became feasible in the past few years because of accumulated digital power increases and cost declines.
As we saw in chapter 3, exponential improvement in computer gear is one of the three fundamental forces enabling the second machine age. Waze also depends critically on the second of these three forces: Waze, for example, uses several streams of information: We thought we understood digitization pretty well based on the work of Shapiro, Varian, and others, and based on our almost constant exposure to online content, but in the past few years the phenomenon has evolved in some unexpected directions.
It has also exploded in volume, velocity, and variety. This surge in digitization has had two profound consequences: This chapter will explore the fascinating recent history of digitization. Like so many other modern online services, Waze exploits two of the well-understood and unique economic properties of digital information: Rival goods, which we encounter every day, can only be consumed by one person or thing at a time. If the two of us fly from Boston to California, the plane that takes off after us cannot use our fuel.
The digitized music itself, however, is nonrival. But if the two of us want to dip into Twenty Thousand Leagues Under the Sea at the same time, we either have to find another copy or Andy has to make a copy of the book he owns. In either case, making that copy would not be cheap. But if Andy has acquired a digital copy of the book, with a couple keystrokes or mouse clicks he can create a duplicate, save it to a physical disk, and give the copy to Erik.
Unlike photocopies, bits cloned from bits are usually exactly identical to the original. Copying bits is also extremely cheap, fast, and easy to do. While the very first copy of a book or movie might cost a lot to create, making additional copies cost almost nothing.
Like most people, we pay a flat fee for Internet access at home and on our mobile devices MIT pays for our access at work. Unlike goods made of atoms, goods made of bits can be replicated perfectly and. Making things free, perfect, and instant might seem like unreasonable expectations for most products, but as more information is digitized, more products will fall into these categories.
They make use of paired sets of documents that were translated, often at considerable expense, by a human from one language into another. For example, the European Union and its predecessor bodies have since issued all official documents in all the main languages of its member countries, and the United Nations has been similarly prolific in writing texts in all six of its official languages.
This is exactly what a service like Google Translate does. When it gets an English sentence and a request for its German equivalent, it essentially scans all the documents it knows about in both English and German, looking for a close match or a few fragments that add up to a close match , then returns the corresponding German text.
But what would happen to the digital world if information were no longer costly to produce? What would happen if it were free right from the start? The same is true for countless websites, blogs, discussion boards, forums, and other sources of online information. Their creators expect no direct monetary reward and offer the information free of charge.
When Shapiro and Varian published Information Rules in , the rise of such usergenerated content, much of which is created without money changing hands, had yet to occur. Blogger, one of the first weblog services, debuted in August , Wikipedia in January , and Friendster, an early social networking site, in Friendster was soon eclipsed by Facebook, which was founded in and has since grown into the most popular Internet site in the world.
Siri, for example, improves itself over time by analyzing the ever-larger collection of sound files its users generate when interacting with the voice recognition system. After all, we humans like to share and interact.
Machine-to-machine M2M communication is a catch-all term for devices sharing data with one another over networks like the Internet. As we move deeper into the second machine age. When its set of prefixes was expanded in at the nineteenth General Conference on Weights and Measures. According to a July story in the New York Times. As of March Google had scanned more than twenty million books published over several centuries.
According to Cisco Systems. Erik asked Google if he could access data about its search terms. Researchers have had similar success using newly available digital data in other domains. A team led by Rumi Chunara of Harvard Medical School found that tweets were just as accurate as official reports when it came to tracking the spread of cholera after the earthquake in Haiti.
Cisco predicts that global Internet Protocol traffic will reach 1. Erik and his doctoral student Lynn Wu. They found their simple model worked. He reasoned that if a couple is going to move to another city and download a house. Their model linked changes in search-term volume to later housing sales and price changes.
Are all of these exa. One of the main reasons we cite digitization as a main force shaping the second machine age is that digitization increases understanding. It does this by making huge amounts of data readily accessible.
T he Data Explosion The digitization of just about everything—documents. A while back Erik guessed that data about Internet searches might signal future changes in housing sales and prices around the country. Technology research firm IDC estimates that there were 2. Waze is a great example here.
These innovators made progress by adding social and sensor data to an existing system. The founders of Waze realized that as digitization advanced and spread they could overcome the shortcomings of traditional GPS navigation. Hal Varian. Waze users help each other by providing information on everything from accidents to police speed traps to cheap gas. And finally. The service is built on multiple layers and generations of digitization. One of our favorite quotes of his is.
Waze makes extensive use of sensor data. In-car navigation systems that use only the first two generations of digital data—maps and GPS location information—have been around for a while.
The next chapter explains why this is. The first and oldest layer is digital maps. Among other things. They can be extremely useful. He also has a way with words. According to Gordon.
Three novelties were central here: In his book The Great Stagnation. At the risk of being flippant. This was due not only to the original Industrial Revolution. Why We Should Be Worried: Economists love to argue with one another.
And like us. While puzzling at the time. The gro wth o f pro ductivity o utput per ho ur slo wed markedly after Growth stalled out. Innovation is how this productivity growth happens. All that remained after were seco nd-ro und impro vements.
But innovation is also the most important force that makes our society wealthier. His work left him convinced that innovation is slowing down. As Gordon shows. But once the internal combustion engine ran out of fuel.
Gordon emphasizes—as do we—the role of new technologies in driving economic growth. We care about innovation so much not simply because we like new stuff. As the novelist William Makepeace Thackeray observed. Most in the profession would agree with Joseph Schumpeter. According to Robert Gordon: The first industrial ro bo t was intro duced by General Mo to rs in It also lit factories. The cotton gin.
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. 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 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. An iPad 2 from , smaller and lighter than an issue of Vanity Fair and as easily slipped into a briefcase, matched the processor speed of a room-sized Cray-2 supercomputer from It cost well under a thousand dollars.
The defining machines of the age steam engines then, chip- based computers now have reached technological maturity and spread widely through society, enabling those who possess them to do things that would have seemed utterly fantastic only a decade earlier. The result, now as then, will be radical, foundational social and economic change. Chapters explore why innovation in computers and other digital technologies has accelerated in the last decade and will probably continue to do so for the foreseeable future.
Our goal as a society, Brynjolffson and McAfee argue, should be to maximize the bounty while limiting the spread; the final section of the book, Chapters , contains their recommendations for achieving that goal. They present all of it in clear, conversational prose studded with pop culture references, quotes from experts in a dozen different fields, and unexpected juxtapositions of the real and the imaginary.
The authors excel at explaining complex ideas clearly. The discussion of technological unemployment—an idea coined by John Maynard Keynes in the s—shows how the shift from a mechanical to a digital world turned it from a debatable concept into an inescapable side- effect of technological progress.
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.One excellent way to do this is to involve more people in this testing process.
Logarithmic graphs have a wonderful property: In aggregate. This led to factories and mass production. Gordon and Cowen see the invention of powerful technologies as central to economic progress. Meso po tamia. As with earlier GPTs. Intangible Assets Just as free goods rather than physical products are an increasingly important share of consumption.