Wladawsky-Berger :U.S. Intelligence Community Forecasts Digital-Driven Future

WSJ, January 11, 2013, 3:31 PM ET

U.S. Intelligence Community Forecasts Digital-Driven Future

       
Irving Wladawsky-Berger

Guest Contributor

The digital technology revolution is transforming every single business and industry around the world. But, beyond that, it is having a transformational impact on economies and societies around the world, as we transition from the industrial society of the past couple of centuries to a new kind of information-based society. What will our world look like over the next couple of decades? A new report by the National Intelligence Council, Global Trend 2030: Alternative Worlds provides a well constructed framework for reflecting on what the world might be like over the next 15 to 20 years. In particular, the report identifies the most important megatrends of our transforming world, all of them highly influenced one way or another by advances in digital technologies. It is critical for companies to understand the nature of these inevitable long term changes, avoid being left behind by faster moving competitors, and figure out how to leverage these changes to grow their business around the world.

The NIC  is the center for mid- and long-term strategic thinking within the U.S. Intelligence Community. Every four years, it develops the Global Trends report to provide the White House and the intelligence community with a framework for long-range strategic policy assessment. Its latest report - Global Trend 2030: Alternative Worlds (GT2030) –was released this past December.

“This report is intended to stimulate thinking about the rapid and vast geopolitical changes characterizing the world today and possible global trajectories during the next 15 to 20 years,” it states in the Executive Summary. “As with the NIC’s previous Global trends reports, we do not seek to predict the future –which would be an impossible feat–but instead provide a framework for thinking about possible futures and their implications.”

GT2030 has identified four overarching megatrends that are expected to shape and transform the world over the next couple of decades: individual empowerment; the diffusion of power; demographic patterns; and the growing nexus among food, water, energy and climate change. These megatrends are pretty knowable. They are well underway today.

But, they could lead to radically different worlds depending on how they interact with what the report calls game-changers, each of which raises unanswerable questions about the very different directions the megatrends might follow. GT2030 explores in detail six such game-changers: a crisis-prone,volatile global economy; governments inability to adapt to a fast changing world; the potential for increased conflicts; wider regional instabilities; the impact of new technologies; and the future role of the United States.

For the final step, the GT2030 team worked with McKinsey to analyze the many alternative scenarios that can occur based on the complex interactions between the four megatrends and the six game-changers. They used McKinsey’s sophisticated Global Growth Model, to model these various scenarios, and picked the four most likely ones. They then developed a somewhat fictionalized vision and storyline for each of the four alternative worlds:

·        stalled engines - globalization stalls and interstate conflicts increase;

·        fusion - worldwide cooperation on a number of issues led by the US and China;

·        gini-out-of-the-bottle: economic inequalities dominate, leading to increased social tensions and global conflicts; and

·        nonstate world - nonstate actors collaborate to confront global challenges leading to a more stable and socially cohesive world.

I found the GT2030 report quite interesting. It does indeed provide a well constructed framework for reflecting on what the world might be like over the next 15 to 20 years. And in particular, it is a good framework for thinking about our continuing digital technology revolution and its transformational impact on economies and societies around the world. We are going through a period of major change, as we transition from the industrial society of the past couple of centuries to a new kind of information-based society. What will our world look like over the next couple of decades?

The impact of new technologies is one of the six game-changers, focused on the question: “Will technological breakthroughs be developed in time to boost economic productivity and solve the problems caused by a growing world population, rapid urbanization, and climate change?”. But, in fact, technological changes play a major role in each of the four megatrends. Let’s take a closer look.

Individual Empowerment: Individual empowerment will accelerate owing to poverty reduction, growth of the global middle class, greater educational attainment, widespread use of new communications and manufacturing technologies, and health-care advances.

Over the past couple of centuries, the Industrial Revolution has led to major improvements in the standard of living around the world. According to economist Richard Steckel, from 1820 to 1998 the overall GDP per capita of the world increased by a factor of 8.6, with different regions experiencing widely different increases. GDP per capita went up by a factor of 3.3 in Africa and India, and 5.5 in China. But in the more industrialized countries, whose economies strongly benefited from the technological and scientific advances of the Industrial Revolution, GDP per capita grew at a much faster rate. Western European countries realized more than a ten-fold increase, the U.S. a factor of 21.7, and Japan 30.5.

These advances have led to a growing middle class of over a billion or so people, mostly concentrated in the industrialized countries. At the same time, over a billion people in less developed economies still live in extreme poverty. But, thanks to individual empowerment, which GT2030 thinks might well be the most important megatrend, this situation is rapidly changing.

“Significant numbers of people have been moving from well below the poverty threshold to relatively closer to it due to widespread economic development. Absent a global recession, the number of those living in extreme poverty is poised to decline as incomes continue to rise in most parts of the world. The number could drop by about 50% between 2010 and 2030, according to some models. . . Under most scenarios–except the most dire–significant strides in reducing extreme poverty will be achieved by 2030. . .”

“Middle classes most everywhere in the developing world are poised to expand substantially in terms of both absolute numbers and the percentage of the population that can claim middle-class status during the next 15 to 20 years. Even the more conservative models see a rise in the global total of those living in the middle class from the current 1 billion or so to over 2 billion people. Others see even more substantial rises with, for example, the global middle class reaching 3 billion people by 2030.”

Digital technologies have been playing a central role in this global individual empowerment. Since the mid-1990s, the Internet has been giving rise to a truly global digital economy, one connecting people and companies all over the world. In the last five years, our continuing technology advances are bringing the empowerment benefits of the digital revolution to just about everyone in the planet.

Three such advances particularly stand out. The explosive growth of increasingly powerful, inexpensive and smart mobile devices; the rise of cloud computing, which is enabling the economical distribution of sophisticated services and apps to all those devices; and ubiquitous, broadband wireless networks linking it all together. Together, these advances are giving rise to an Internet-based platform for digital, inclusive innovations which is lifting people out of extreme poverty as well as significantly expanding the world’s middle class.

Diffusion of Power: There will not be any hegemonic power. Power will shift to networks and coalitions in a multipolar world.

There are two major aspects to this megatrend. Economic and political power is shifting from North America and Western Europe to the faster-growing economies in the East and South. National power is getting distributed to countries with rising GDPs and populations, not just China, India and Brazil, but also regional players like Columbia, Indonesia, Nigeria, South Africa and Turkey.

“The shift in national power is only half the story and may be overshadowed by an even more fundamental shift in the nature of power,” observes the report. “By 2030, no country–whether the U.S., China, or any other large country–will be a hegemonic power. Enabled by communications technologies, power almost certainly will shift more toward multifaceted and amorphous networks composed of state and nonstate actors that will form to influence global policies on various issues. Leadership of such networks will be a function of position, enmeshment, diplomatic skill, and constructive demeanor. Networks will constrain policymakers because multiple players will be able to block policymakers’ actions at numerous points.”

The nonstate actors will include large cities and urban regions, multinational companies, nongovernmental organizations, academic institutions and empowered ad-hoc communities. Social media, big data and other advanced technologies, will enable these groups to collaborate with each other as well as with national governments to confront global challenges. Given the polarized populations and national governments in the U.S. and other large countries, such a distributed model of governance may well emerge as the most reasonable way to get things done.

Demographic Patterns: The demographic arc of instability will narrow. Economic growth might decline in aging countries. Sixty percent of the world’s population will live in urbanized areas; migration will increase.

Technology must play a major role to help find affordable solutions to the challenges posed by a growing, increasingly urban population, expected to rise from 7.1 to 8.3 billion people in 2030, 60% of whom will live in cities compared to 50% today.

In addition, the median age of almost all countries is rising rapidly, especially in the more advanced economies. A large percentage of their populations will be over 65 years, posing major challenges to health care and social benefit programs. Technological innovations are required to help provide high quality, affordable health services to an aging population, as well as the proper environment to enable them to work longer and postpone retirement.

Food, Water, Energy Nexus: Demand for these resources will grow substantially owing to an increase in the global population. Tackling problems pertaining to one commodity will be linked to supply and demand for the others.

With billions rising out of poverty and joining the middle class, we can expect an increased demand for natural resources as well as for products and services of all kinds. But, meeting these demands and hopefully unleashing an age of prosperity will only be possible in an economy based on sustainable production and consumption patterns.

“An expanding middle class and swelling urban populations will increase pressures on critical resources -particularly food and water–but new technologies–such as vertical farming in high-rise structures which also reduce transportation costs–could help expand needed resources. Food and water security is being aggravated by changing weather conditions outside of expected norms.”

“We are not necessarily headed into a world of scarcities, but policymakers and their private sector partners will need to be proactive to avoid scarcities in the future. . . The questions will be whether management of critical resources becomes more effective, the extent to which technologies mitigate resource challenges, and whether better governance mechanisms are employed to avoid the worst possible outcomes.”

In its opening page, the Global Trends 2030 report compares our current times to the dawn of the Industrial Age. “We are living through a similar transformative period in which the breadth and scope of possible developments–both good and bad –are equal to if not greater than the aftermath of the political and economic revolutions of the late 18th century.”

It then summarizes our current times with the famous opening lines used by Charles Dickens as he wrote about the late 18th century period of A Tale of Two Cities:

It was the best of times, it was the worst of times . . . it was the spring of hope, it was the winter of despair . . . we were all going direct to Heaven, we were all going direct the other way . . .

Irving Wladawsky-Berger is a former vice-president of technical strategy and innovation at IBM. He is a strategic advisor to Citigroup and is a regular contributor to CIO Journal.

How Tech Could Help Joe Biden Win the Gun Fight

How Tech Could Help Joe Biden Win the Gun Fight

By MAUREEN MACKEY, The Fiscal Times

January 13, 2013

In the wake of the tragic events in Newtown, Connecticut, President Obama has made reducing gun violence in this country a priority, while the firepower and big bucks of the National Rifle Association have suggested putting armed guards in every school across the U.S.  But what has gotten little attention lately in the heated debates on guns and gun rights is the critical role the knowledge revolution can play in the issue.

On Friday, Vice President Joe Biden, leader of a task force on gun violence, said his group is examining new technology that would prevent people from firing guns that they did not purchase themselves.

Biden said his group “will be meeting with technology experts because, to overstate the case ... a lot could change if, for example, every gun purchased could only be fired by the person who purchased it. That technology exists, but it’s extremely expensive.”

The vice president, due to send a report to the White House on Tuesday, said that if the devices “were available with every weapon sold, there’s significant evidence that it may very well curtail what happened up in Connecticut... Had the young man [Adam Lanza] not had access to his mother’s arsenal, he may or may not have [done] what he did,” Biden said.

He was referring to what’s known as smart gun technology, which would enable the weapons, through embedded sensors in the grip, to recognize the fingerprints of a gun owner. There are opponents and skeptics, of course. New Jersey famously tried and failed in the past decade to enact a smart gun law, and the Violence Policy Center, a group working to stop gun-related deaths and injuries, has said the feasibility of such technology is still speculative.

But what’s not questionable is the other ways the knowledge revolution can help solve the problem of gun violence by responding much more rapidly to events as they occur, potentially saving lives and organizing critical knowledge that can be used to reduce and prevent gun crime in the future.

Case in point: A firm called ShotSpotter, a leader in the gunshot detection field, is actively using acoustic sensors placed at intervals throughout a given neighborhood to record the sound of gunfire. Then, using computers, it can triangulate the sound to pinpoint the sources of those blasts on a map.

The privately held company, recently profiled in the new book The Human Face of Big Data  by Rick Smolan and Jennifer Erwitt, can spare police the long, trying and often expensive searches for perpetrators and victims. Interestingly, the technology is also showing that in dangerous neighborhoods across the U.S., only a tiny fraction of all gunshots fired – perhaps 10 percent – are even reported at all.

RELATED:   How You’re Shaping the Future Through Big Data

Imagine, for example, that this technology resulted in massive amounts of data – known as Big Data – and was used to identify terrorist cells in the U.S. or elsewhere where illegal “training camps” prepare terrorists for their nefarious deeds. If law enforcement identified unusual activity in multiple areas from the data, an alert could go out, potentially thwarting a disaster.

“Today, more than 70 cities around the country are using ShotSpotter and its security technologies to accurately pinpoint the locations of shootings within their jurisdictions,” say Smolan and Erwitt. “The technology has become one of the most powerful tools available to crime fighters – and represents a classic example of the power of Big Data.”

Marc Goodman, a global security expert, says that when it comes to combating violence in our society, “the Big Data revolution holds the promise of empowering all of us with knowledge, products and services that will make our lives measurably better.”

At ShotSpotter’s offices in Mountain View, California, the staff – using sophisticated acoustic sensors, high-speed telecommunications and powerful computers – can pinpoint with great accuracy the starting point of a given gunshot. But the abilities go beyond just nailing that gunshot to a spot on a street map: The technology can also deliver the precise outside location, all in a matter of seconds.

RELATED:  19 Unbelievable Facts About Guns in America

The benefits to crime fighters are critical: Instead of police arriving at a crime scene about a half-hour after reports of gunfire, they can be there in under two or three minutes. The near-instant analysis of data means that law enforcement personnel are often able to respond to an event as it is happening – perhaps even in time to catch the criminal or to save the life of a gunshot victim.

ShotSpotter says that for over a decade, public safety agencies have used its data-driven solutions “to provide them with timely and precise event detection and actionable intelligence to aid their proactive anti-crime strategies.”

To protect ourselves from future threats by gun-toting killers and other criminals, Goodman, the security expert, says “we will have to fight this battle on two fronts, both of them driven by technology and the Big Data revolution.” The first line of attack, he says, “will be an arms race between traditional law enforcement and the criminal world,” which he says will mostly take place in high-powered crime command centers in major cities and use powerful new predictive data analytics.

But “the second tool,” says Goodman in an essay in The Human Face of Big Data, “will have to be broader, deeper, and much more scalable than traditional police work. That tool is all of us. I am increasingly convinced that we will be much better off having millions of average citizens approaching the problem of public safety as a group and seeing what we can do, [rather] than merely leaving the problem to a select group of government officials.”

The tools we need, he adds, “are already in our hands: crowdsourcing, citizen journalism and investigation, cloud computing, and global wireless broadband communications. Add to that the emerging analytical tools of Big Data and billions of new sensors about the world spewing data – and all of the pieces will be in place to create a powerful counterbalance to those” who would try to harm us, even in our own backyards.

This is the third in a series of articles about the ability of Big Data to change lives, economies, governments and much more, as captured by Rick Smolan and Jennifer Erwitt in The Human Face of Big Data. Read more about their project here.

Read more at http://www.thefiscaltimes.com/Articles/2013/01/13/How-Joe-Biden-Could-Win-the-Gun-Fight-with-Technology.aspx#IOTfqKi0VAMz5Z3E.99

ECONOMIST: Innovation pessimism

Innovation pessimism

Has the ideas machine broken down?

The idea that innovation and new technology have stopped driving growth is getting increasing attention. But it is not well founded

Jan 12th 2013 | from the print edition

http://www.economist.com/news/briefing/21569381-idea-innovation-and-new-technology-have-stopped-driving-growth-getting-increasing/print

BOOM times are back in Silicon Valley. Office parks along Highway 101 are once again adorned with the insignia of hopeful start-ups. Rents are soaring, as is the demand for fancy vacation homes in resort towns like Lake Tahoe, a sign of fortunes being amassed. The Bay Area was the birthplace of the semiconductor industry and the computer and internet companies that have grown up in its wake. Its wizards provided many of the marvels that make the world feel futuristic, from touch-screen phones to the instantaneous searching of great libraries to the power to pilot a drone thousands of miles away. The revival in its business activity since 2010 suggests progress is motoring on.

So it may come as a surprise that some in Silicon Valley think the place is stagnant, and that the rate of innovation has been slackening for decades. Peter Thiel, a founder of PayPal, an internet payment company, and the first outside investor in Facebook, a social network, says that innovation in America is “somewhere between dire straits and dead”. Engineers in all sorts of areas share similar feelings of disappointment. And a small but growing group of economists reckon the economic impact of the innovations of today may pale in comparison with those of the past.

Some suspect that the rich world’s economic doldrums may be rooted in a long-term technological stasis. In a 2011 e-book Tyler Cowen, an economist at George Mason University, argued that the financial crisis was masking a deeper and more disturbing “Great Stagnation”. It was this which explained why growth in rich-world real incomes and employment had long been slowing and, since 2000, had hardly risen at all (see chart 1). The various motors of 20th-century growth—some technological, some not—had played themselves out, and new technologies were not going to have the same invigorating effect on the economies of the future. For all its flat-screen dazzle and high-bandwidth pizzazz, it seemed the world had run out of ideas.

Glide path

The argument that the world is on a technological plateau runs along three lines. The first comes from growth statistics. Economists divide growth into two different types, “extensive” and “intensive”. Extensive growth is a matter of adding more and/or better labour, capital and resources. These are the sort of gains that countries saw from adding women to the labour force in greater numbers and increasing workers’ education. And, as Mr Cowen notes, this sort of growth is subject to diminishing returns: the first addition will be used where it can do most good, the tenth where it can do the tenth-most good, and so on. If this were the only sort of growth there was, it would end up leaving incomes just above the subsistence level.

Intensive growth is powered by the discovery of ever better ways to use workers and resources. This is the sort of growth that allows continuous improvement in incomes and welfare, and enables an economy to grow even as its population decreases. Economists label the all-purpose improvement factor responsible for such growth “technology”—though it includes things like better laws and regulations as well as technical advance—and measure it using a technique called “growth accounting”. In this accounting, “technology” is the bit left over after calculating the effect on GDP of things like labour, capital and education. And at the moment, in the rich world, it looks like there is less of it about. Emerging markets still manage fast growth, and should be able to do so for some time, because they are catching up with technologies already used elsewhere. The rich world has no such engine to pull it along, and it shows.

This is hardly unusual. For most of human history, growth in output and overall economic welfare has been slow and halting. Over the past two centuries, first in Britain, Europe and America, then elsewhere, it took off. In the 19th century growth in output per person—a useful general measure of an economy’s productivity, and a good guide to growth in incomes—accelerated steadily in Britain. By 1906 it was more than 1% a year. By the middle of the 20th century, real output per person in America was growing at a scorching 2.5% a year, a pace at which productivity and incomes double once a generation (see chart 2). More than a century of increasingly powerful and sophisticated machines were obviously a part of that story, as was the rising amount of fossil-fuel energy available to drive them.

But in the 1970s America’s growth in real output per person dropped from its post-second-world-war peak of over 3% a year to just over 2% a year. In the 2000s it tumbled below 1%. Output per worker per hour shows a similar pattern, according to Robert Gordon, an economist at Northwestern University: it is pretty good for most of the 20th century, then slumps in the 1970s. It bounced back between 1996 and 2004, but since 2004 the annual rate has fallen to 1.33%, which is as low as it was from 1972 to 1996. Mr Gordon muses that the past two centuries of economic growth might actually amount to just “one big wave” of dramatic change rather than a new era of uninterrupted progress, and that the world is returning to a regime in which growth is mostly of the extensive sort (see chart 3).

Mr Gordon sees it as possible that there were only a few truly fundamental innovations—the ability to use power on a large scale, to keep houses comfortable regardless of outside temperature, to get from any A to any B, to talk to anyone you need to—and that they have mostly been made. There will be more innovation—but it will not change the way the world works in the way electricity, internal-combustion engines, plumbing, petrochemicals and the telephone have. Mr Cowen is more willing to imagine big technological gains ahead, but he thinks there are no more low-hanging fruit. Turning terabytes of genomic knowledge into medical benefit is a lot harder than discovering and mass producing antibiotics.

The pessimists’ second line of argument is based on how much invention is going on. Amid unconvincing appeals to the number of patents filed and databases of “innovations” put together quite subjectively, Mr Cowen cites interesting work by Charles Jones, an economist at Stanford University. In a 2002 paper Mr Jones studied the contribution of different factors to growth in American per-capita incomes in the period 1950-93. His work indicated that some 80% of income growth was due to rising educational attainment and greater “research intensity” (the share of the workforce labouring in idea-generating industries). Because neither factor can continue growing ceaselessly, in the absence of some new factor coming into play growth is likely to slow.

The growth in the number of people working in research and development might seem to contradict this picture of a less inventive economy: the share of the American economy given over to R&D has expanded by a third since 1975, to almost 3%. But Pierre Azoulay of MIT and Benjamin Jones of Northwestern University find that, though there are more people in research, they are doing less good. They reckon that in 1950 an average R&D worker in America contributed almost seven times more to “total factor productivity”—essentially, the contribution of technology and innovation to growth—that an R&D worker in 2000 did. One factor in this may be the “burden of knowledge”: as ideas accumulate it takes ever longer for new thinkers to catch up with the frontier of their scientific or technical speciality. Mr Jones says that, from 1985 to 1997 alone, the typical “age at first innovation” rose by about one year.

A fall of moondust

The third argument is the simplest: the evidence of your senses. The recent rate of progress seems slow compared with that of the early and mid-20th century. Take kitchens. In 1900 kitchens in even the poshest of households were primitive things. Perishables were kept cool in ice boxes, fed by blocks of ice delivered on horse-drawn wagons. Most households lacked electric lighting and running water. Fast forward to 1970 and middle-class kitchens in America and Europe feature gas and electric hobs and ovens, fridges, food processors, microwaves and dishwashers. Move forward another 40 years, though, and things scarcely change. The gizmos are more numerous and digital displays ubiquitous, but cooking is done much as it was by grandma.

Or take speed. In the 19th century horses and sailboats were replaced by railways and steamships. Internal-combustion engines and jet turbines made it possible to move more and more things faster and faster. But since the 1970s humanity has been coasting. Highway travel is little faster than it was 50 years ago; indeed, endemic congestion has many cities now investing in trams and bicycle lanes. Supersonic passenger travel has been abandoned. So, for the past 40 years, has the moon.

Medicine offers another example. Life expectancy at birth in America soared from 49 years at the turn of the 20th century to 74 years in 1980. Enormous technical advances have occurred since that time. Yet as of 2011 life expectancy rested at just 78.7 years. Despite hundreds of billions of dollars spent on research, people continue to fall to cancer, heart disease, stroke and organ failure. Molecular medicine has come nowhere close to matching the effects of improved sanitation.

To those fortunate enough to benefit from the best that the world has to offer, the fact that it offers no more can disappoint. As Mr Thiel and his colleagues at the Founders Fund, a venture-capital company, put it: “We wanted flying cars, instead we got 140 characters.” A world where all can use Twitter but hardly any can commute by air is less impressive than the futures dreamed of in the past.

The first thing to point out about this appeal to experience and expectation is that the science fiction of the mid-20th century, important as it may have been to people who became entrepreneurs or economists with a taste for the big picture, constituted neither serious technological forecasting nor a binding commitment. It was a celebration through extrapolation of then current progress in speed, power and distance. For cars read flying cars; for battlecruisers read space cruisers.

Technological progress does not require all technologies to move forward in lock step, merely that some important technologies are always moving forward. Passenger aeroplanes have not improved much over the past 40 years in terms of their speed. Computers have sped up immeasurably. Unless you can show that planes matter more, to stress the stasis over the progress is simply a matter of taste.

Mr Gordon and Mr Cowen do think that now-mature technologies such as air transport have mattered more, and play down the economic importance of recent innovations. If computers and the internet mattered to the economy—rather than merely as rich resources for intellectual and cultural exchange, as experienced on Mr Cowen’s popular blog, Marginal Revolution—their effect would be seen in the figures. And it hasn’t been.

As early as 1987 Robert Solow, a growth theorist, had been asking why “you can see the computer age everywhere but in the productivity statistics”. A surge in productivity growth that began in the mid-1990s was seen as an encouraging sign that the computers were at last becoming visible; but it faltered, and some, such as Mr Gordon, reckon that the benefits of information technology have largely run their course. He notes that, for all its inhabitants’ Googling and Skypeing, America’s productivity performance since 2004 has been worse than that of the doldrums from the early 1970s to the early 1990s.

The fountains of paradise

Closer analysis of recent figures, though, suggests reason for optimism. Across the economy as a whole productivity did slow in 2005 and 2006—but productivity growth in manufacturing fared better. The global financial crisis and its aftermath make more recent data hard to interpret. As for the strong productivity growth in the late 1990s, it may have been premature to see it as the effect of information technology making all sorts of sectors more productive. It now looks as though it was driven just by the industries actually making the computers, mobile phones and the like. The effects on the productivity of people and companies buying the new technology seem to have begun appearing in the 2000s, but may not yet have come into their own. Research by Susanto Basu of Boston College and John Fernald of the San Francisco Federal Reserve suggests that the lag between investments in information-and-communication technologies and improvements in productivity is between five and 15 years. The drop in productivity in 2004, on that reckoning, reflected a state of technology definitely pre-Google, and quite possibly pre-web.

Full exploitation of a technology can take far longer than that. Innovation and technology, though talked of almost interchangeably, are not the same thing. Innovation is what people newly know how to do. Technology is what they are actually doing; and that is what matters to the economy. Steel boxes and diesel engines have been around since the 1900s, and their use together in containerised shipping goes back to the 1950s. But their great impact as the backbone of global trade did not come for decades after that.

Roughly a century lapsed between the first commercial deployments of James Watt’s steam engine and steam’s peak contribution to British growth. Some four decades separated the critical innovations in electrical engineering of the 1880s and the broad influence of electrification on economic growth. Mr Gordon himself notes that the innovations of the late 19th century drove productivity growth until the early 1970s; it is rather uncharitable of him to assume that the post-2004 slump represents the full exhaustion of potential gains from information technology.

And information innovation is still in its infancy. Ray Kurzweil, a pioneer of computer science and a devotee of exponential technological extrapolation, likes to talk of “the second half of the chess board”. There is an old fable in which a gullible king is tricked into paying an obligation in grains of rice, one on the first square of a chessboard, two on the second, four on the third, the payment doubling with every square. Along the first row, the obligation is minuscule. With half the chessboard covered, the king is out only about 100 tonnes of rice. But a square before reaching the end of the seventh row he has laid out 500m tonnes in total—the whole world’s annual rice production. He will have to put more or less the same amount again on the next square. And there will still be a row to go.

Erik Brynjolfsson and Andrew McAfee of MIT make use of this image in their e-book “Race Against the Machine”. By the measure known as Moore’s law, the ability to get calculations out of a piece of silicon doubles every 18 months. That growth rate will not last for ever; but other aspects of computation, such as the capacity of algorithms to handle data, are also growing exponentially. When such a capacity is low, that doubling does not matter. As soon as it matters at all, though, it can quickly start to matter a lot. On the second half of the chessboard not only has the cumulative effect of innovations become large, but each new iteration of innovation delivers a technological jolt as powerful as all previous rounds combined.

The other side of the sky

As an example of this acceleration-of-effect they offer autonomous vehicles. In 2004 the Defence Advanced Research Projects Agency (DARPA), a branch of America’s Department of Defence, set up a race for driverless cars that promised $1 million to the team whose vehicle finished the 240km (150-mile) route fastest. Not one of the robotic entrants completed the course. In August 2012 Google announced that its fleet of autonomous vehicles had completed some half a million kilometres of accident-free test runs. Several American states have passed or are weighing regulations for driverless cars; a robotic-transport revolution that seemed impossible ten years ago may be here in ten more.

That only scratches the surface. Across the board, innovations fuelled by cheap processing power are taking off. Computers are beginning to understand natural language. People are controlling video games through body movement alone—a technology that may soon find application in much of the business world. Three-dimensional printing is capable of churning out an increasingly complex array of objects, and may soon move on to human tissues and other organic material.

An innovation pessimist could dismiss this as “jam tomorrow”. But the idea that technology-led growth must either continue unabated or steadily decline, rather than ebbing and flowing, is at odds with history. Chad Syverson of the University of Chicago points out that productivity growth during the age of electrification was lumpy. Growth was slow during a period of important electrical innovations in the late 19th and early 20th centuries; then it surged. The information-age trajectory looks pretty similar (see chart 4).

It may be that the 1970s-and-after slowdown in which the technological pessimists set such store can be understood in this way—as a pause, rather than a permanent inflection. The period from the early 1970s to the mid-1990s may simply represent one in which the contributions of earlier major innovations were exhausted while computing, biotechnology, personal communication and the rest of the technologies of today and tomorrow remained too small a part of the economy to influence overall growth.

Other potential culprits loom, however—some of which, worryingly, might be permanent in their effects. Much of the economy is more heavily regulated than it was a century ago. Environmental protection has provided cleaner air and water, which improve people’s lives. Indeed, to the extent that such gains are not captured in measurements of GDP, the slowdown in progress from the 1970s is overstated. But if that is so, it will probably continue to be so for future technological change. And poorly crafted regulations may unduly raise the cost of new research, discouraging further innovation.

Another thing which may have changed permanently is the role of government. Technology pessimists rarely miss an opportunity to point to the Apollo programme, crowning glory of a time in which government did not simply facilitate new innovation but provided an ongoing demand for talent and invention. This it did most reliably through the military-industrial complex of which Apollo was a spectacular and peculiarly inspirational outgrowth. Mr Thiel is often critical of the venture-capital industry for its lack of interest in big, world-changing ideas. Yet this is mostly a response to market realities. Private investors rationally prefer modest business models with a reasonably short time to profit and cash out.

A third factor which might have been at play in both the 1970s and the 2000s is energy. William Nordhaus of Yale University has found that the productivity slowdown which started in the 1970s radiated outwards from the most energy-intensive sectors, a product of the decade’s oil shocks. Dear energy may help explain the productivity slowdown of the 2000s as well. But this is a trend that one can hope to see reversed. In America, at least, new technologies are eating into those high prices. Mr Thiel is right to reserve some of his harshest criticism for the energy sector’s lacklustre record on innovation; but given the right market conditions it is not entirely hopeless.

Perhaps the most radical answer to the problem of the 1970s slowdown is that it was due to globalisation. In a somewhat whimsical 1987 paper, Paul Romer, then at the University of Rochester, sketched the possibility that, with more workers available in developing countries, cutting labour costs in rich ones became less important. Investment in productivity was thus sidelined. The idea was heretical among macroeconomists, as it dispensed with much of the careful theoretical machinery then being used to analyse growth. But as Mr Romer noted, economic historians comparing 19th-century Britain with America commonly credit relative labour scarcity in America with driving forward the capital-intense and highly productive “American system” of manufacturing.

The view from Serendip

Some economists are considering how Mr Romer’s heresy might apply today. Daron Acemoglu, Gino Gancia, and Fabrizio Zilibotti of MIT, CREi (an economics-research centre in Barcelona) and the University of Zurich, have built a model to study this. It shows firms in rich countries shipping low-skill tasks abroad when offshoring costs little, thus driving apart the wages of skilled and unskilled workers at home. Over time, though, offshoring raises wages in less-skilled countries; that makes innovation at home more enticing. Workers are in greater demand, the income distribution narrows, and the economy comes to look more like the post-second-world-war period than the 1970s and their aftermath.

Even if that model is mistaken, the rise of the emerging world is among the biggest reasons for optimism. The larger the size of the global market, the more the world benefits from a given new idea, since it can then be applied across more activities and more people. Raising Asia’s poor billions into the middle class will mean that millions of great minds that might otherwise have toiled at subsistence farming can instead join the modern economy and share the burden of knowledge with rich-world researchers—a sharing that information technology makes ever easier.

It may still be the case that some parts of the economy are immune, or at least resistant, to some of the productivity improvement that information technology can offer. Sectors like health care, education and government, in which productivity has proved hard to increase, loom larger within the economy than in the past. The frequent absence of market pressure in such areas reduces the pressure for cost savings—and for innovation.

For some, though, the opposite outcome is the one to worry about. Messrs Brynjolfsson and McAfee fear that the technological advances of the second half of the chessboard could be disturbingly rapid, leaving a scourge of technological unemployment in their wake. They argue that new technologies and the globalisation that they allow have already contributed to stagnant incomes and a decline in jobs that require moderate levels of skill. Further progress could threaten jobs higher up and lower down the skill spectrum that had, until now, seemed safe.

Pattern-recognition software is increasingly good at performing the tasks of entry-level lawyers, scanning thousands of legal documents for relevant passages. Algorithms are used to write basic newspaper articles on sporting outcomes and financial reports. In time, they may move to analysis. Manual tasks are also vulnerable. In Japan, where labour to care for an ageing population is scarce, innovation in robotics is proceeding by leaps and bounds. The rising cost of looking after people across the rich world will only encourage further development.

Such productivity advances should generate enormous welfare gains. Yet the adjustment period could be difficult. In the end, the main risk to advanced economies may not be that the pace of innovation is too slow, but that institutions have become too rigid to accommodate truly revolutionary changes—which could be a lot more likely than flying cars.

Big Data and "Insourcing"

Quote: “US paper mills and oil refineries will enjoy the cheap gas bonanza but not much production in these sectors is likely to shift to US shores.The more important technological jolt comes under the heading of “big data”. On Friday an exhaustive survey of management practices at 30,000 US manufacturing establishments was released. Two of the authors, Nick Bloom and John Van Reenen, had previously shown that US companies were, on average, better managed than foreign rivals.”

http://www.ft.com/cms/s/0/6709cc5c-58ed-11e2-b59d-00144feab49a.html#ixzz2HUdQ9FCP

American industry is on the move

By Sebastian Mallaby

Last year Jeff Immelt, the boss of General Electric, declared that outsourcing was “mostly outdated as a business model”. GE’s venerable Appliance Park in Louisville, Kentucky, is opening a string of new assembly lines to build refrigerators, water heaters and washing machines, bringing home jobs from China and Mexico. President Barack Obama has trumpeted this wave of “insourcing”, while Hal Sirkin of the Boston Consulting Group foretells a US “manufacturing renaissance”. Even as the news from Washington reeks of heedless brinkmanship, the news from the people who actually make stuff sounds refreshingly hopeful.

How real is this renaissance? It is tempting to dismiss it out of hand. Manufacturing has experienced a steady relative decline in just about all advanced economies. Between 1980 and 2010, German manufacturing value added fell from 30 per cent of gross domestic product to 21 per cent, according to World Bank data, while Japan’s fell from 27 per cent to 19 per cent. But there are a few exceptions. After its financial crisis in 1992, Sweden boosted manufacturing value added as a share of output and held on to the gains for more than a decade.

Click to enlarge

The question is whether Sweden’s conditions exist in the US. The first requirement is a weak currency. After its peak in 1992 Sweden’s real effective exchange rate fell 27 per cent, according to the Bank for International Settlements. Since the dollar peaked in 2002, it has fallen 21 per cent, enough to make a major difference. In 2000 US wages were almost 22 times higher than China’s. By 2015 that multiple will have declined to four.

The other Swedish ingredient is a productivity boom. In 1995 Sweden joined the EU and opened its economy to foreign investment. The country’s industrial champions responded by investing twice as much in vocational training as their EU rivals and restructuring aggressively. Between 1996 and 2009, this yielded a cumulative boost to manufacturing productivity of 57 per cent, according to the OECD. By contrast, Germany managed only 17 per cent.

If Sweden sounds impressive, here is the surprise: over the same period American manufacturers piled up an even larger productivity gain of 69 per cent. Again, competition contributed: the US joined the North American Free Trade Agreement and the World Trade Organisation, and its continent-sized economy generates plenty of internal competition. But in the US case, the impetus from trade and competition has been powerfully reinforced by a jolt from technology.

Despite much fashionable chatter, this is not mainly about fracking. The new extraction technology has cut the price of natural gas in the US to a fraction of the Asian level, but, as the McKinsey Global Institute observed recently, the industries that are most energy-intensive are not actually very trade-intensive. US paper mills and oil refineries will enjoy the cheap gas bonanza but not much production in these sectors is likely to shift to US shores.

The more important technological jolt comes under the heading of “big data”. On Friday an exhaustive survey of management practices at 30,000 US manufacturing establishments was released. Two of the authors, Nick Bloom and John Van Reenen, had previously shown that US companies were, on average, better managed than foreign rivals. A striking conclusion of their study is that US manufacturers continue to get better, particularly when it comes to capturing and analysing data on everything from customer behaviour to production-line efficiencies. And there is plenty of scope to improve further. A minority of survey respondents embraced most state-of-the-art management incentives and monitored performance against clear targets. But a quarter of respondents adopted fewer than half of these practices.

So the stage is at least half set for a US manufacturing revival, even if obstacles – poor education, poor infrastructure – remain. But what might a revival mean? Not, unfortunately, a cure for unemployment. Since a trough in January 2010, the US has generated just over half a million new manufacturing jobs but the bounce mostly reflects the collapse during the recession. For an advanced economy to create manufacturing employment independently of a cyclical rebound is almost unheard of. Even as it boosted manufacturing as a share of output between 1993 and 2007, Sweden lost almost a 10th of its manufacturing jobs.

But a manufacturing turnround is clearly desirable. Precisely because manufacturing workers can be displaced by machines, it is factories that drive productivity: in the US, manufacturing accounted for about 17 per cent of output between 1995 and 2005, yet contributed 37 per cent of economywide productivity gains, according to McKinsey. Higher productivity means higher pay for surviving employees: American manufacturing workers are on average paid better than American service workers. And consumers benefit from the productivity windfall. Since 1985 the quality-adjusted price of US durables has scarcely budged while the cost of services has more than doubled.

A US manufacturing renaissance is possible, not certain. But Americans are right to celebrate the early indicators – from Siemens, which has just begun shipping US-made turbines to Saudi Arabia; from Toyota, which exports US-made cars to 21 countries; and of course from that chief insourcer, GE’s Mr Immelt.

The writer is a senior fellow at the Council on Foreign Relations and an FT contributing editor

Impact of Social media

Quote: “The impact of new technologies is invariably misjudged because we measure the future with yardsticks from the past.”

January 5, 2013

Can Social Media Sell Soap?

By STEPHEN BAKER

http://www.nytimes.com/2013/01/06/opinion/sunday/can-social-media-sell-soap.html?_r=0&pagewanted=print

ONE morning in mid-December, Pope Benedict XVI gazed down on an iPad and composed his first tweet. From a marketing perspective, it was about time. While the pontiff had been issuing his traditional encyclicals online, other world leaders were venturing further, onto Facebook and Twitter. The Dalai Lama, for example, was already spreading his wisdom in 140-character packets to more than five million followers. And as people retweeted his posts, his messages winged through social media, reaching tens of millions. How could the Vatican resist such marketing magic?

Growing legions of marketing consultants are pushing social media as the can’t-miss future. They argue that pitches are more likely to hit home if they come from friends on Facebook, Twitter, Tumblr or Google+. That’s the new word of mouth, long the gold standard in marketing. And the rivers of data that pour into these networks fuel the vision of precision targeting, in which ads are so timely and relevant that you welcome them. The hopes for such a revolution have fueled a market frenzy around social networks — and have also primed them for a fall.

The drama swirls around data. In the “Mad Men” depiction of an advertising firm in the ’60s, the big stars don’t sweat the numbers. They’re gut followers. Don Draper pours himself a finger or two of rye and flops on a couch in his corner office. He thinks. His job is to anticipate the needs and desires of fellow human beings, and to answer them with ideas. What slogan would light up the eyes of the dour airline executive, or the dog food people? Fellow humanists dominate Don Draper’s rarefied world, while the numbers people, two or three of them crammed into dingier offices, pore over Nielsen reports and audience profiles.

In the last decade however, those numbers people have rocketed to the top. They build and operate the search engines. They’re flexing their quantitative muscles at agencies and starting new ones. And the rise of social networks, which stream a global gabfest into their servers, catapults these quants ever higher. Their most powerful pitches aren’t ideas but rather algorithms. This sends many of today’s Don Drapers into early retirement. Others, paradoxically, hunt down new work on social networks like LinkedIn.

Yet this year has brought renewed hope for the humanists — or at least a satisfying burst of schadenfreude. Facebook made its public offering in May at a valuation of $104 billion, only to see the share price tumble as many began to doubt the network’s potential as a medium for paid ads. Corporate advertisers are devoting only a modest 14 percent of their online budgets to social networks. According to comScore, a firm that tracks online activity, e-commerce soared 16 percent from last year, to nearly $39 billion this holiday season. But advertising from social networks appeared to play only a supporting role. I.B.M. researchers found that on the pivotal opening day of the season, Black Friday, a scant 0.68 percent of online purchases came directly from Facebook. The number from Twitter was undetectable. Could it be that folks aren’t in a buying mood when hanging out digitally with their friends?

A more likely answer is this: When big new phenomena arrive on the scene, it’s hard to know what to count. We’ve seen this before. During the dot-com bubble in the late ’90s, investors threw billions at Internet start-ups that promised to deliver targeted ads to millions of viewers, or “eyeballs.” But eyeballs didn’t produce dollars, and the high-flying market crashed. Many naysayers gleefully concluded that the Internet itself had failed.

Yet as these cyberskeptics crowed, a company called Overture Services was pioneering an innovative advertising application for the new medium. When Web surfers carried out searches, it turned out, they welcomed related ads. And if they clicked on one, the advertiser paid the search engine. Google soon implemented this system on a mammoth scale and turned clicks into dollars. Advertisers could calculate their return on investment down to the penny. In this domain, the insights of a Mad Man counted for nothing. Search ran on numbers. The quants rushed in.

While the rise of search battered the humanists, it also laid a trap that the quants are falling into now. It led to the belief that with enough data, all of advertising could turn into quantifiable science. This came with a punishing downside. It banished faith from the advertising equation. For generations, Mad Men had thrived on widespread trust that their jingles and slogans altered consumers’ behavior. Thankfully for them, there was little data to prove them wrong. But in an industry run remorselessly by numbers, the expectations have flipped. Advertising companies now face pressure to deliver statistical evidence of their success. When they come up short, offering anecdotes in place of numbers, the markets punish them. Faith has given way to doubt.

This leads to exasperation, because in a server farm packed with social data, it’s hard to know what to count. What’s the value of a Facebook “like” or a Twitter follower? What do you measure to find out? In this way, marketing resembles other hot spots of data research, including brain science and genomics. In each one, scientists are combing through petabytes of data, trying to discern whether certain genes or groups of neurons cause something or simply correlate with it. It’s not clear, because these are immensely complex systems with millions of variables — much like our social networks. Even as researchers swim in data that previous generations would have swooned over, they struggle to answer crucial questions regarding cause and effect. What action can I take to get the response I want?

Debates rage as quants accuse one another of counting the wrong things. Take I.B.M.’s Black Friday study. While the numbers indicate that few shoppers clicked directly from a social network to buy a laptop or a fridge, some may have seen ads that later led to a purchase. If so, valuable influence went unmeasured. “I.B.M. is looking at a single point in time,” says Dan Neely, the chief executive of Networked Insights, a marketing analytics company. Neely’s team followed Macy’s Black Friday campaign on Twitter, which started weeks before the big day; it generated a viral flurry on the network, he says. Clearly, many big advertisers are still believers: last week, Facebook shares got a boost from reports that Walmart, Samsung and other boldfaced names have recently stepped up social-media advertising.

But gauging the effectiveness of these ads is still a challenge. “It’s hard to measure influence,” says Steve Canepa, I.B.M.’s general manager for media and entertainment.

That, in fact, may be the ultimate lesson to draw from the social media marketing miracle that wasn’t. The impact of new technologies is invariably misjudged because we measure the future with yardsticks from the past.

Dave Morgan, a pioneer in Internet advertising and the founder of Simulmedia, an ad network for TV, points to the early years of electricity. In the late 19th century, most people associated the new industry with one extremely valuable service: light. That was what the marketplace understood. Electricity would displace kerosene and candles and become a giant of illumination. What these people missed was that electricity, far beyond light, was a platform for a host of new industries. Over the following years, entrepreneurs would come up with appliances — today we might call them “apps” — for vacuuming, laundry and eventually radio and television. Huge industries grew on the electricity platform. If you think of Apple in this context, it’s a $496 billion company that builds the latest generation of electricity apps.

Social networks, like them or not, are fast laying out a new grid of personal connections. Even if this matrix of humanity sputters in advertising and marketing, it’s bound to spawn new industries in consulting, education, collaborative design, market research, media and loads of products and services yet to be imagined. Maybe, just maybe, it will even be able to sell soap.

Stephen Baker is a technology journalist who blogs at thenumerati.net, and the author of “Final Jeopardy: Man vs. Machine and the Quest to Know Everything.”

Alan Davidson: Is Google Like Gas or Like Steel?

Bruce D. Brown and Alan B. Davidson, The New York Times, January 4, 2013

AFTER a two-year investigation, the Federal Trade Commission concluded this week that Google’s search practices did not violate antitrust law. Those who wanted to see an epic battle like the one the government fought with Microsoft in the 1990s were sorely disappointed. But the analogy to the browser war of the Web’s early days was never the right one. It failed to capture the dangers free speech would have faced if regulators had agreed with Google’s critics.

The theories that many critics advanced — that search must be “neutral” because it is akin to a public utility, or that computer-generated search results are not speech and therefore not protected under the First Amendment — would have undermined free press principles across the Internet. That the F.T.C. decision permits Google to continue to use its judgment in analyzing search requests and presenting pertinent results is a victory for online expression and is consistent with First Amendment law since the 1940s.

Seven decades ago, a lawsuit against The Associated Press applied antitrust rules to the media and was resolved in a way that ultimately protected First Amendment interests. This case was always a better parallel than Microsoft to the F.T.C. investigation of Google. Like Google today, The A.P. had extraordinary influence. Then as now there were questions about whether something more than common antitrust law should govern companies that play such an important role in the delivery of information to the public.

Back then, the Justice Department alleged that A.P. bylaws allowed its member papers to impede local competitors by denying them access to The A.P.’s expansive news network. A trial court agreed but applied a theory far broader than routine antitrust law. It held that news was not an “ordinary” product like “steel” governed solely by antitrust, but rather something more “vital” because it was “clothed with a public interest.”

In other words, the trial court wanted to treat the mass media like a public utility, which carried considerable consequences. For example, while it would be illegal under antitrust law for a large steel company to conspire with competitors to fix prices, that company has no obligation to sell to every carmaker that wants steel. A public utility, on the other hand, has to serve everyone in the marketplace equally. Applying that standard to The A.P. would have opened the door to far broader regulation and could, in theory, have meant something as absurd as requiring newspapers to cover every press release or publish every letter to the editor.

When the case reached the Supreme Court in 1945, the modern understanding of the First Amendment, with its insistence on an independent news media, had yet to take shape. So it was with great significance that — even though The A.P. lost its appeal and had to allow more access to its services — the court steered entirely clear of the public-utility model. It looked instead to standard antitrust law in finding The A.P.’s conduct to be a classic restraint on trade.

The court went further in setting down a marker that to this day restrains government regulation of the media. Justice Hugo L. Black, who would become a leading champion of the First Amendment, wrote that nothing in the ruling could “compel A.P. or its members to permit publication of anything which their ‘reason’ tells them should not be published.”

This began a historic run in which the court transformed the media into an institution with the autonomy to serve as a check on government power. The First Amendment as we know it would look very different if public utility obligations had been forced onto the press that day.

If The A.P. was concerned about a regulator in every newsroom, Google was concerned about a regulator in every algorithm.

Advocates of aggressive action against Google saw the computer algorithms behind search as a utility that should be heavily regulated like the gas or electricity that flows into our homes. But search engines need to make choices about what results are most relevant to a query, just as a news editor must decide which stories deserve to be on the front page. Requiring “search neutrality” would have placed the government in the business of policing the speech of the Internet’s information providers. To quote Justice Black, it would have made search engines publish those results “which their ‘reason’ tells them should not be published.”
Others argued that the F.T.C. did not need to be guided by First Amendment concerns at all because search results are created by computers, not by human beings. Yet computers “speak” in many ways today. Lawmakers could have used F.T.C. precedent against Google to regulate the content of Amazon’s book recommendations, the locations on Bing’s maps, the news stories that trend on Facebook and Twitter, and many other online expressions of social and political importance.

The F.T.C. resisted these harmful theories, and as a result speakers all over the Internet won. But that doesn’t mean Google is exempt from regulation. The First Amendment is not a grant of immunity for any business, and antitrust scrutiny does not end where editorial judgment begins. But the A.P. case shows that antitrust laws can be enforced while protecting the right of a free press to print what it chooses and nothing more.

This makes regulation of the media difficult. But regulating speech should not be easy, like regulating a public utility, but hard, as the F.T.C. has correctly found.

Bruce D. Brown is the executive director of the Reporters Committee for Freedom of the Press and a lecturer at the University of Virginia Law School. Alan B. Davidson is a visiting scholar at M.I.T.’s Technology and Policy Program and a former director of public policy for the Americas at Google.

Approaching Illness as a Team

The New York Times, December 25, 2012

The Cleveland Clinic, long considered a premier medical system, is gaining new renown for innovation in improving the quality of care while holding down costs.

In its most fundamental reform, the clinic in the past five years has created 18 “institutes” that use multidisciplinary teams to treat diseases or problems involving a particular organ system, say the heart or the brain, instead of having patients bounce from one specialist to another on their own.

The Neurological Institute, for example, provides both inpatient or outpatient care for those 
with strokes and brain tumors, as well as those with epilepsy, multiple sclerosis, depression and sleep disorders, among other conditions.

On a recent visit, we observed one such team, consisting of a neurosurgeon, a neurologist, a neuroradiologist, a neurologist with advanced training in intensive care, a physical and rehabilitation doctor, a medical resident, a physical therapist and a nurse. As they made rounds from patient to patient, they had a portable computer that displayed electronic medical records so that the whole team could see how the patient was doing and plan the course of care for the day.

This team approach can improve the quality of care because all the experts are involved in deciding the best treatment option, which can save time and money. The neurological team, by consensus, has been better able to determine which acute stroke patientsneed a risky and expensive treatment that involves threading a catheter through an artery in the leg up into the brain to destroy a clot. It cut the use of that treatment in half, reducing costs and deaths and improving outcomes.

The Cleveland Clinic has strong leverage to drive such reforms because its staff physicians are salaried and are granted only one-year contracts and subjected to annual performance reviews. Those reviews apply measures of quality, like patient improvement, patient satisfaction and cost reductions. It raises the pay of those who get high marks, reduces the pay of poor performers and even terminates some doctors who fall short. This approach could become more widespread as more hospitals and doctors move toward the salary-based model.

Data analysis to evaluate how well treatments work is also a big part of the medical practice. For instance, the clinic analyzed outcomes for heart surgery patients and found that those who had received blood transfusions during surgery had higher complication rates afterward and a lower long-term survival rate. As a result, it has adopted strict guidelines that limit the use of transfusions.

Such judgments about a treatment’s effectiveness are made by doctors, not by financial administrators, so they tend to be accepted. One analysis found that suturing could be done as well with a $5 silk stitch as with a $400 staple, leading to a big drop in the use of the staples. At the same time, the clinic has also carried out simpler reforms, like improving sterile conditions, which has reduced catheter-related bloodstream infections by more than 40 percent and urinary tract infections by 50 percent. All this has happened in a remarkably short time. Patients seem to like the treatment they get. A federal government survey of patient opinion last fall found that 80 percent of the patients gave the Cleveland Clinic a high rating over all and 84 percent would recommend it to others, well above the state and national averages in the 69 percent to 71 percent range.

Still, many patients are clearly unhappy. A series this year about confusing medical bills and unexpectedly high charges by The Plain Dealer of Cleveland elicited hundreds of patients’ complaints mostly directed against the clinic, because it had reclassified off-campus physician practices and health centers as hospital outpatient facilities and tacked on a “facility fee” for services previously billed at lower doctor’s office rates. The clinic says the added fees are justified because it provides better quality controls and health information technologies in its outpatient units than that available in a typical doctor’s office.

Medicare’s spending per patient at the clinic for an episode of illness that requires hospitalization is below the national median, suggesting that the clinic’s cost-cutting efforts are working. The University HealthSystem Consortium, an alliance of the nation’s leading nonprofit academic medical centers and teaching hospitals, gave the clinic one of its “rising star” awards in September for significant improvements over the previous year in quality, patient safety and clinical effectiveness, an indication that its quality efforts are taking hold.
The Cleveland Clinic’s progress in restructuring itself, said Michael Porter, a Harvard professor who analyzes health care delivery and organizational change, is “light speed” compared with other institutions. The clinic is “a model of where we need to go,” he said, “Not perfect, not done, but far along.”

Are We All Being Fooled by Big Data?

Michael Moritz, Linked In, January 03, 2013

When, on a summer Sunday morning in 1987, three hundred thousand people crammed onto the central span of San Francisco’s Golden Gate Bridge, they came perilously close to participating in the largest accident in American history. The bridge's engineers had made copious calculations and had designed it to sway nearly 28 feet and shoulder the burden of hundreds of vehicles. But nobody had ever predicted that a gigantic crowd of pedestrians, attracted by the fiftieth anniversary of its opening, would be stuck between its towering pylons unable to move in any direction. As a result, the bridge flattened out and came within whiskers of straining every last fiber of its vermilion superstructure.

The consequences of faulty data, wonky forecasts, ill-conceived opinions, loose predictions, incorrect assumptions and, in the case of the Golden Gate Bridge, an improbable event form the backbone of Nate Silver’s absorbing new book, The Signal and the Noise: Why Most Predictions Fail but Some Don’t. This book, written by the voice behind the popular election forecasting blog, FiveThirtyEight, now licensed by the New York Times, is a reminder that while data doesn’t lie, it does allow people to deceive themselves and others. In some cases it's a question of the bigger the data, the grander the deception.

These days our entire lives revolve around predictions. Government departments project the cost of health exchanges, the rate of economic growth, next year’s crop yields, the future birth rate and the arms buildup of unfriendly countries. Websites and retailers anticipate what we want to find and buy; oil companies gauge the best sites for drilling; pharmaceutical companies assess the probable efficacy of molecules on a disease; while, in the background, the bobble-heads on television incessantly spew out largely irrelevant and inaccurate forecasts. In the meantime, we busy ourselves with personal projections. How long will our commute take? When will the turkey be golden? How much will the price of a stock rise? What will the future value be of a law degree?

Some of these forecasts are surprisingly accurate while others are shockingly dismal. Silver, who has become the Woody Allen of statisticians, explains the reasons. Like many others, the 34-year-old Silver became fascinated with numbers because of a boyhood devotion to baseball. Unlike his peers, Silver – after a brief and frustrating spell as a consultant – instinctively returned to the challenges of numbers. He took up internet poker (only to eventually discover that the odds were not in his favor) and, also started to unravel the riddles presented by data.
There are events that – at least on the surface – defy forecast: things that are so outlandish or improbable that, for most people at one time or another, they seem inconceivable. Think of Pearl Harbor, 9/11, Fukushima, a black President of the United States or Apple as the world’s most valuable company. Yet all, to varying extents, were possible to predict if people had been able to separate the important from the trivial (a.k.a. the signal from the noise) and make the giant leap of faith which converts the improbable into the possible. While Silver provides a supple assessment of the reasons we struggle to comprehend these sorts of possibilities, the majority of his book is devoted to an often-hilarious account of how we deal with more mundane challenges.

About ten years ago, Silver developed a system for predicting the performance of batters and hitters for Baseball Prospectus. The exercise helped him develop his approach to predictions. It is no coincidence that Silver fastened on both baseball and politics. In each pursuit there is an enormous trove of accurate, historical information. The baseball fiend can immerse himself in minutiae such as hits, on-base percentages and pitches thrown, while the political junkie can stare at votes recorded, demographic shifts and polling results. Silver gradually discovered that in baseball the data, while essential, could be made richer with the judicious application of human judgment. This must have come as a reassuring endorsement for baseball scouts whose usefulness had been much maligned in the years following the publication of Moneyball, Michael Lewis’ much-read book about the way data had helped Billy Beane transform the Oakland A’s. After all, it is difficult for a machine to measure the determination, pluck, grit (and wandering eye or fondness for drink) of a baseball player.

The same goes for politics, the field in which Silver made his reputation with his accurate predictions about the 2008 races (which he subsequently burnished in 2012). Here he bases many of his predictions on the averages of poll results conducted by others. This, he has discovered, provides more accurate forecasts for election nights than reliance on a single pollster, no matter how sterling the reputation. When Silver does stray from the received wisdom, he does so with caution and says, “The further I move away from consensus, the stronger my evidence has got to be … that I have things right.” This is an observation worth dwelling upon because it helps explain why most people have such trouble making the correct decision about an unconventional selection or the path less trodden. Making a decision frowned upon by a committee or a popular opinion is a lonely place to be.

Accurate information married with human judgment is the best ally for the prognosticator. This explains why some forecasts, such as those for hurricanes, are so good and others, such as economic predictions, are so poor. Thanks to a knowledge of past catastrophes, satellite photography, weather balloons and airplanes that fly into the eye of the storms, the National Hurricane Center can predict the path and severity of hurricanes with remarkable certainty several days in advance of when they collide with land. This information, enhanced by the analysis of scientists, has improved the National Hurricane Center’s forecasting accuracy by 350% in the past 25 years. The fact that 1,833 people died when Hurricane Katrina swamped New Orleans is not because of faulty forecasting, but mainly because the city’s Mayoral office hesitated about ordering a compulsory emergency evacuation until it was too late. According to Silver, weather forecasting for the subsequent two or three days, at least as promulgated by the National Weather Service (before it falls into the buffoonish hands of the local TV weathermen for whom ratings are more important than accuracy), is also something that can be counted on.

Economic forecasting is another matter. Part of the reason that predictions about hurricanes and the weather have improved is that scientists, mathematicians and programmers can build computer models from accurate molecular data of cloud formations. The same is not true for the economy where attempts to capture every calorie of economic endeavor are much harder. Even the U.S. government – irrespective of whether a Democrat or Republican is at the helm – has proved woefully inept at forecasting overall GDP growth let alone more refined measures. It’s not uncommon for economic forecasters to fail to predict recessions even after they are already underway. It’s a wonder that any bank or company bothers to keep an economist on the payroll. They all might be better off employing the descendants of Carnac the Magnificent, the soothsayer from the East once played by Johnny Carson.

While economists have plenty of excuses, the same does not go for the rating agencies that, prior to the housing collapse, so conspicuously labeled the thousands of mortgages they bundled together as relatively riskless. Even if you are prepared to accept that officials at S&P, Moody’s and Fitch were merely guilty of a failure of judgment – as opposed to criminal collusion – they made the colossal mistake of not recognizing the consequences of uncertainty (a risk that is hard to measure): the close correlation between all these mortgages. They did not understand that they had designed a monstrous, nationwide pileup of concrete, glass and wood. It’s no coincidence that these same rating agencies are today all involved in designing a future economic calamity: the implosion of municipal, state and corporate pension obligations. In this case they are even more culpable because they are willfully ignoring copious amounts of stock market data which, if heeded, would instantly catapult these pension systems into default.

If an economist might deserve some pity, it’s a teaspoonful compared to what should be given to those charged with making an accurate prediction of the timing and strength of an earthquake. These hapless devils don’t have accurate pictures of geological formations dozens of miles below the earth’s crust or reams of data supplied by probes latched to different striations. Nonetheless, in our data-drenched age, the geologist is still somehow expected to provide certainty about a cataclysmic event that may last a matter of seconds. That’s especially true in Italy, where, in the wake of the 2009 quake that killed over 300 people in the central Italian town of L’Aquila, six scientists and a government official were found guilty of manslaughter and sentenced to six years in jail for not protecting their neighborhood. The sentencing magistrates, like the Japanese in the ninth century, must just believe that earthquakes can be accurately predicted from the behavior of catfish.

Longterm perspective: A Timeline of Information History

A Timeline of Information History

This timeline presents significant events and developments in the innovation and management of information and documents from cave paintings (ca 30,000 BC) to the present. To keep recent electronic developments from dominating the listing, only the most significant digital innovations are included.

Big Data Quotes of 2012

Gil Press, Forbes, January 1, 2012 

 “The data fabric is the next middleware”–Todd Papaioannou

 “…once the database is big enough, it will also let parents compare their baby with fellow users’ babies of the same age and gender. [LinkedIn data scientist Monica] Rogati imagines that this crowdsourcing will provide an early-warning system to help parents determine what is and isn’t out of the ordinary… It will be a way, she says matter-of-factly, ‘to debug your baby for problems’”–Mya Frazier

“One of the issues of social networking silos is that they have the data and I don’t … There are no programmes that I can run on my computer which allow me to use all the data in each of the social networking systems that I use plus all the data in my calendar plus in my running map site, plus the data in my little fitness gadget and so on to really provide an excellent support to me”–Tim Berners-Lee

“With too little data, you won’t be able to make any conclusions that you trust.  With loads of data you will find relationships that aren’t real… Big data isn’t about bits, it’s about talent”–Douglas Merrill

“Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?”–Steve Lohr