The Power of Information

Greg Satell, Innovation Excellence, July 10, 2012

What’s the big deal about the information economy anyway?  Surely, information has played a role in commerce since ancient times.  What’s changed?

One reason for the confusion is that we’re not used to making the distinction between information and knowledge.  Go to Istanbul’s ancient Grand Bazaar and you’ll instantly grasp that the traders know a lot, but not much that they can easily share even if they want to. That, it turns out, makes all the difference.

The emergence of information as a storable, fungible entity is transforming our economy and our society in ways that we scarcely realize.  It’s making us richer, smarter and even healthier.  What’s more, its impact is accelerating, so we’ll see a lot more change in the coming decades than we have in the past.  In fact, we’re just getting started.

A Theory of Information

If you had to put a date on it, the digital age truly began in 1948.  It was in that year that two seismic events happened, both at Bell Labs.  The first and the more famous was the invention of the transistor, which forms the basis for our present (albeit soon to be defunct) digital technology paradigm.

The lesser known, but in some ways more important event, was Claude Shannon’s groundbreaking paper A Mathematical Theory of Communication, which launched information theory out of thin air, seemingly with no precursor.

Although not widely publicized or understood at the time, it has become central to all the digital technology we use today.

The basic idea was that information isn’t a function of content, but rather a lack of randomness, which can be broken down to a single unit – a choice between two alternatives.  Much like a coin toss which lacks information while in the air, but takes on a level of certainty when it lands, information arises when ambiguity disappears.

He called this unit, a “binary digit” or a bit and much like the pound, quart, meter or liter, has become such a basic unit of measurement that it’s hard to imagine our modern world without it.

Storage and Transfer

In the late 1940’s, Shannon’s colleague at Bell Labs, Richard Hamming became frustrated that computer errors continually ruined his work.  He built upon Shannon’s paper and created Hamming code, small bits of extra information that would allow computers to detect and correct errors.

Of course, that increased the amount of information that needed to be processed.  No problem, information theory also shows us how to compress information by eliminating redundancies.  Common technologies that we have come to use everyday, like JPEG and MP3 are, in fact compression techniques that have their roots in Shannon’s 1948 paper.

It is storage and transfer that make the information age so different from what we knew in the past.  In contrast to the knowledge that a bazaar trader possesses, computers can duplicate and transfer information an infinite number of times, with as little error as we choose.

Accelerating Returns

The ability to store and transfer information efficiently has an interesting side effect – we can improve at an exponential pace.  Returns to our efforts not only increase, they accelerate.  The most famous example is Moore’s Law, which says that processing speeds double about every 18 months.

 

Look at the chart above and notice the logarithmic scale.  From 2000 to 2010, the number of transistors on a single chip increased from 10 million to a billion.  That’s a hundred times more than the increase the previous decade and nearly a million times more than the decade before that.  In the next ten years, we can expect it to increase 100 -fold again.

What’s even more amazing and also of paramount importance, is that the principle isn’t exclusive to processing, but applies to every facet of information technology, from storage to bandwidth to power consumption, everywhere you look, efficiency continues to improve exponentially.

The Information Invasion

Here’s where it gets really interesting.  As information technology becomes more widely deployed, the information content of other products and services increases and they begin to follow the same exponential trends.  Take a look at genome sequencing:

As sequencing genomes became less of a pure biological science and more of an information science, the pace of advancement changed drastically enabling a whole new field of bioinformatics that will revolutionize medicine.

Similar trends are being played out across almost every industry you can think of.  In manufacturing, new technologies like 3D printing and (eventually) programmable matter are creating what The Economist calls a third industrial revolution.  Even when you go and buy a box of cereal at Wal-Mart, a good portion of the retail price is made up of informationally dense logistics.

Ray Kurzweil spoke volumes when he said that in the future “all technologies will essentially become information technologies, including energy.”

From Belief to Observation

As the informational content of products and services increases and returns accelerate, lots of good things happen.  Science fiction becomes engineered fact.  The unthinkable will become commonplace.  Incomes will rise while poverty falls.  Seemingly intractable problems will be solved and dire needs will be met.

Yet there is also quite a bit that is unsettling.  As technological cycles shorten, business models will have shorter life spans. The internalized experiences that we have come to regard as intuition will fail us more often.  Our ability to plan will diminish and the need to experiment (and fail) will increase.

And that’s what’s disconcerting.  This new information economy doesn’t run on beliefs or even, to a certain extent on ambition, but algorithms which, powered by ever more abundant processing power, test and accept or discard a dizzying multitude of possibilities, the results of which can be retrieved and recombined with other experiments.

The power of information means that we are no longer required to believe, only to imagine, test and observe.

Amazon economy: From warehouse to powerhouse

Barney Jopson, The Financial Times, July 8, 2012

Once just a book seller, the company provides vital logistics for many but it seems set to face sterner political scrutiny 

GoVacuum is a fast-growing ecommerce business whose annual sales have gone from under $2m seven years ago to $10m today, a fivefold expansion that surpasses the overall growth of US internet shopping. Masterminded by founder Bill Anand, who arrived from New Delhi in 1974, such growth underlines the importance of small business to the world’s biggest economy and commands kudos in an ecommerce sector where Amazon seems to hog the spoils.

But the success of GoVacuum and its 27 staff has not come in spite of the online behemoth from Seattle. It has come because of Amazon.

Most consumers still view Amazon as an online book retailer. Some are surprised to find it sells much more than the single product with which Jeff Bezos, its founder and chief executive, started in 1994. But the store front is just the tip of an iceberg.

Behind the scenes, Mr Bezos is turning Amazon into a back office infrastructure provider that sells access to a digital market place with millions of customers, to petabytes of server space and to state-of-the-art warehouse facilities serving myriad forms of commerce, including GoVacuum’s.

“They are investing in areas that touch so many people’s lives they’re becoming about as important as utilities,” says Sachin Anand, GoVacuum’s business development manager and Bill’s son. 

Amazon has long thrived by overturning the way people shop, but its shift into infrastructure is extending its power as a disruptive force to how business is structured. It is revolutionising the way entrepreneurs can create start ups, or revive staid companies, by letting them plug their ideas into pay-as-you-go systems that cost a fraction of the investment they would need to build such infrastructure alone. “They allowed us to achieve a scale that on our own we wouldn’t have been able to,” says Sachin Anand.

This has lifted Amazon’s economic influence beyond its tech peers Apple, Google and Facebook and taken it into the realm of network businesses such as stock exchanges, power grid operators, credit card processors and shipping lines.

But its emergence as a new and largely unregulated steward of such vital commercial plumbing creates tensions and dangers for clients and parts of the US economy. 

Individual companies must weigh the benefits of joining Amazon’s “ecosystem” against the costs of losing autonomy and the dual role Amazon plays as both a rival retailer and a service provider that is able to see their sales and inventory – critical details for any business.

At a macro level, hub businesses such as Amazon improve efficiency, which is good news for small players, says Marco Iansiti, a professor at Harvard Business School. “The bad news is your destiny is shared with a bunch of other people,” he says. “If Amazon has a catastrophe, if the website goes down, then a lot of people are affected. It is a source of systemic risk that we didn’t have before.”

Amazon is notoriously opaque and its shift into services is making a growing proportion of its business less conspicuous to the public eye, just as it becomes more critical than ever to the US consumer economy.

Unbundling the Corporation

In a series starting on Monday, the Financial Times is charting Amazon’s ability to reinvent industries, drawing on over 50 interviews with former employees, clients, software providers, rival retailers, academics, bankers and consultants.

What emerges is a voracious company whose no-frills technology and pragmatism have helped it build a commanding role in ecommerce, while also shaping its reaction to strident criticism over not collecting US sales tax, its first big political battle.

But Amazon has flaws: it remains heavily dependent on its founder, it has not kept up with the advance of digital music and movies, and it has yet to convince many investors that it will ever make a substantial profit. Its growing influence is also likely to face more political scrutiny. 

In his latest letter to shareholders in April, Mr Bezos said: “We are creating powerful self-service platforms that allow thousands of people to boldly experiment and accomplish things that would otherwise be impossible or impractical.”

Its services have already produced hybrid businesses where Amazon runs marketing, customer relationships, payments, computing, logistics and distribution, leaving executives to do nothing but find or make good products, both physical and digital.

In effect Amazon is “unbundling” the corporation – a concept coined by McKinsey management consultants in the late 1990s – by letting companies shed supposedly “core” processes that conventional wisdom says should be combined within a single entity. Doing so, however, ties their cost bases inescapably to Amazon.

A key attraction for Amazon, which made a meagre $631m net profit on revenue of $48bn last year, is that it is more profitable to earn service fees than to buy and sell products itself.

Beyond pure retail, one per cent of all consumer internet traffic touches Amazon-managed infrastructure, according to DeepField Networks, a computing group. Amazon’s Kindle Direct Publishing platform has enabled writers to produce ebooks.

Amazon Web Services, a cloud computing business, has become an engine behind digital content companies including Spotify, a digital music service, and Netflix, a video streaming service. Outages, however, have downed some client websites twice this month and last April. Spotify and Netflix also compete against Amazon’s Cloud Player and Instant Video services, but Amazon says information about cloud clients is not shared within its empire.

Still, unease among some large retailers about using Amazon services is evident. Target, a US discount chain, last year stopped running its website on Amazon’s systems and brought the job in-house. Marks and Spencer, a UK retailer, is doing the same. Toys R Us dropped Amazon as its webstore operator in 2006 after a judge ruled it had broken a contract by selling toys from other merchants on Amazon.com. 

As overlord of a mini-economy, Amazon must tread a fine line. “How do you create a healthy ecosystem in which you are not the only guy making money?” says Suresh Kotha, a professor at the University of Washington in Seattle.

“At some point people will switch if you become too powerful and start milking them,” he says. “If this happens … the ecosystem will slowly start to lose momentum.”

Mining clients’ data

GoVacuum last year sold its final store, in a small town in Virginia. The business that remains is partly fused with Amazon. Without it, says Sachin Anand, GoVacuum would have fewer sales, a narrower product range, a more unwieldy warehouse and less satisfied customers.

Between 40 and 50 per cent of GoVacuum’s revenue comes from Amazon’s market place, an online bazaar where it pays a 15 per cent commission on each sale to access US consumers – 85m unique users in May alone, according to ComScore research – who use Amazon as the Google of product search and visit its site at a rate of 32 a second. 

The deliveries are done by Fulfillment by Amazon, a distribution service, which is why the hoovers on the storehouse pallet are just passing through GoVacuum’s drab facility en route to an Amazon warehouse. They will be stored and shipped to customers for fees equal to roughly 10 per cent of each sale, depending on size and weight – a rate that is cheaper than any of the alternatives.

Amazon processes the payments of GoVacuum customers, keeps their email addresses to itself, and handles after-sales support. GoVacuum even uses the databases of Amazon Web Services to store information.

But Amazon’s commitment to its own front-end retail business is not waning. Sachin Anand has watched it appearing to treat GoVacuum as a laboratory where it can identify popular products then start to sell them itself at lower prices. “There are vague frustrations with the market place that, because they have visibility into what you are selling, they can quickly become your competitor,” says Mr Anand. “There’s no part of the agreement that says they can’t do that. When we started, Amazon wasn’t in our category at all.”

It should be no surprise. Amazon is famously hard-nosed and an unrivalled data miner, enjoying a privileged view of hordes of shoppers buying everything from detergent and shoes to pet food and patio furniture.

Diane Buzzeo, the founder of Ability Commerce, a software group that retailers use to manage their presence on Amazon, says: “They do look at your product mix and then go to the manufacturer and say: ‘Hey, why don’t we sell this direct?’ It’s amazing they haven’t been hit with some kind of non-competition lawsuit.”

One toy seller who did not want to be named says: “The work we and others have done has paved the way for Amazon to understand what sells and what doesn’t.” He tries to stay ahead by finding new designs and new manufacturers. “We have to protect ourselves against Amazon, which has perfect knowledge of everything.”

By contrast, eBay, a rival, uses the fact that it does not have its own retail business as a selling point.

In the first quarter, Amazon said its 2m-plus registered sellers – ranging from companies to dads emptying their garages – accounted for 39 per cent of unit sales by volume, up from 33 per cent a year ago.

Not all of them are bothered about competing against Amazon, which declined to make any of its executives available for comment. Jack Sheng, co-founder of eForCity, a seller of electronic accessories, says it does not unfairly monopolise customers and that, on its site, data on the most popular products and every seller’s line-up is publicly available. “Everyday I can tell you all our competitors are looking at our sales and entering our space,” he says. “Just suck it up.”

One investment banker says Amazon’s position is reminiscent of Goldman Sachs’ dual role as a broker and trader at the centre of capital markets. “People complain about conflicts of interest. But you still have to do business with them.”

Why services make sense

Like Goldman, Amazon has a ruthless reputation. This was sharpened in the eyes of Main street rivals last December when it gave a discount to shoppers who used its smartphone app to record a product’s price in a store then bought it from Amazon instead.

But its move into infrastructure has received little scrutiny, partly because unlike the cool products of Apple, Google and Facebook, Amazon focuses on dull-but-difficult tasks such as managing product listings and delivering boxes.

These are the foundation of its services. Shel Kaphan, who was Mr Bezos’s first Amazon employee and left in 1999, explains the shift in its business by noting that “platforms” are a favourite concept of software people and also hark back to Mr Bezos’s time at DE Shaw, a quantitative hedge fund in New York.

“Since Jeff came from Wall Street and worked at a company that was at the middle of markets the concept of a market place was always floating around. It’s the idea that being in the middle of everything helps you scoop up pennies and nickels,” he says.

With Amazon’s operating margin a mere 1.5 per cent in the past quarter – compared to Walmart’s 5.7 per cent – the shift into services could also solve what hedge fund manager David Einhorn has alluded to as the Amazon “riddle”: can it make serious profits?

In addition to low prices and free shipping, huge investments in warehouses and data centres have kept a lid on profitability. Its services enable it to make money from them.

Colin Sebastian, analyst at Robert W Baird, says overall Amazon probably breaks even selling products from its own inventory, but makes an operating margin of around 10 per cent on third party sales.

Mr Bezos’s talk of creating services to help entrepreneurs also has a whiff of venture capitalism, says Joel Bines, a consultant at AlixPartners. “But he’s not backing 10 companies, he’s backing thousands. And when one of them becomes a massive success, he continues to take his toll.”

Dependence discomforts

One such business, RJF Books and More, gets 100 per cent of its sales from Amazon and consists simply of its systems plus Bob Frank and his wife. From homes in Minneapolis and Phoenix, the couple drive around America buying interesting merchandise – from Tudor history books to bath soap – which they load into boxes in their car and send to an Amazon warehouse when full.

Mr Frank started the business after he was laid off as a software salesman and raised $1,000 by selling his own books on Amazon. Now his annual revenue is in six figures.

It would have been impossible without Amazon taking care of the selling, shipping, customer service, payments and complaints. “Sometimes I wish there was some other competition, because competition is always good … But no one else can do it as well as they can,” Mr Frank says.

Amazon says it focuses on helping sellers compete with other retailers, itself included, but the harsh truth is that most on its market place are expendable because someone can always fill their shoes. The sellers sign up freely and no one is accusing Amazon of breaking any laws.

The law, however, has not caught up with the evolution of technology platforms and competition lawyers are eternally vigilant. 

“In any of these models there’s a tension,” says Prof Iansiti at Harvard. “How you behave makes a big difference. Are you adding value to the ecosystem? Are you abusing your market power?”

Platforms tend to be winner-takes-all games where one contender eventually reaches a critical mass and everyone then gravitates to it. Amazon’s scale means entrepreneurs are finding it increasingly attractive to join and harder to leave, but signs of wariness among some businesses are growing.

GoVacuum, for example, is sprucing up its own website and building its presence on other market places run by eBay, Sears and Best Buy.

“There is the concern of the dependency issue,” says Mr Anand. “It would be easy to say ‘Hey, we are only going to focus on Amazon’, but there has to be some diversification.”

Mr Bezos has created a transaction engine that is reinventing the way entrepreneurs can do business. If his ambition is to colonise the entire infrastructure of consumption, it is not likely to be Amazon’s technological skills that set the limits, but the world’s willingness to accept him.

The "Algorithmic Revolution" in healthcare.

How Connected Health, Public-Private Cooperation, And Big Data Can Revolutionize Health Care

Guest Post by Dr. Jody Ranck,  Forbes, July 6, 2012

 

As we contemplate the fallout from the Supreme Court’s decision in NFIB v. Sebelius on the Obama Administration’s Affordable Care Act, it is useful to think beyond the din of the punditry on what the decision means in an electoral cycle and consider seriously the looming crisis in healthcare.

Our health system—or better, anti-system—consumes nearly $2 trillion annually and does not deliver the value that it should.  Those who think we have the best system in the world come up against the cold hard facts (if they matter in political debate anymore) of health outcomes, which indicate that we’re ranked 37^th in the world and trending downward, not improving.  Despite one of the most robust ecosystems for innovation in biotechnology and information technology that the world has ever seen, converting this into tangible health outcomes is an “innovation space” that we’ve yet to get a handle on.

There is hope and promising signs that these tools can be mobilized to make a dent in some of the major challenges we face such as obesity, diabetes, environmental health, and a host of other areas if we can bring politics, technology, and both the public and private sectors into the right relationship in the coming years.  

Technology can’t do it alone.  Nor will antiquated political hubris of “death panels” and market fundamentalism fix health care.  In my new book, “Connected Health: How Mobiles, Cloud and Big Data Will Reinvent Healthcare”, I explore how this might happen if we can think more strategically and engage in more grassroots efforts to engage with the “Algorithmic Revolution” in healthcare.

Mobile Health (mHealth)

Today we find mainstream publications almost daily reporting on new mobile health, or mHealth, apps to help you manage your fitness, diet or health condition.   There are over 10,000 health apps in the iTunes store, making them one of the fastest-growing categories, and it is estimated that 2012 will reach at least 247 million people who have downloaded at least one app, up from 124 million in 2011, according to the analyst firm Research2Guidance.  

The firm estimates that the world market for mHealth apps will reach over $1.2 billion this year.  These apps run the gamut from diabetes, exercise and fitness apps (Runkeeper, Fitbit), dieting (Lose It; the wireless weight scales designed by Withings), heart rate monitors (Azumio), sleep trackers (Zeo), mood trackers (Mood Tracker) and a host of peripherals that attach to your phone to provide everything from diagnostics for diseases to EKGs, eye exams (EyeNetra), and even microscopes.

Some mHealth apps use game dynamics to motivate collective action as in the case of the University of California-based CITRIS’ Pwning Asthma Triggers app that helps residents of high-risk neighborhoods identify the sources of industrial pollution that trigger asthma.  One of the most interesting startups coming out of MIT is Ginger.io, a platform that enables you to combine patient data with passive data collected on one’s cellphone.  Passive data provides insights on how individuals are using the device and can help one better understand social and psychological factors that can determine outcomes.   There are many who are looking to mobiles to help improve one of the sticky problems of healthcare: behavioral change that is often required to manage multiple chronic diseases.  Successful mHealth apps will have to demonstrate the ability to move the dial on behavioral change to warrant investment in coming years.

The Internet of Things, Health 2.0 and Mobiles as Health Hubs

Beyond mobile devices, there is the rapidly growing use of sensors that are connected to the Internet.  The Internet of Things, as it is commonly known, can also play an important role in future health systems.  From real-time air pollution sensors (the focus of a recent EPA Challenge, MyAir, MyHealth) and radiation sensors (Cosm (formerly Pachube)) to the growing market for wearables (Misfit Wearables, Zephyr Technology), we’re increasingly entering the early years of the Health Internet of Things.  That is, sensors connected to the Internet that can transmit data related to health.   When you combine this with the use of social networks for health, including platforms such as PatientsLikeMe.com, CureTogether, and general use of Twitter and Facebook for exchanging health information or organizing campaigns for health, the amount of health data and networks related to health and healthcare is exploding.

There is also the increasing number of individuals who use a variety of devices and social media platforms to track data about their health and fitness, experiment, and exchange insights.  Widely known as the Quantified Self, this movement is rapidly moving beyond early adopters in Silicon Valley and beginning to have an influence in even how consumer device manufacturers think about future health and fitness devices.  Qualcomm Life, perhaps anticipating the mainstreaming of aspects of the Quantified Self movement, has focused on creating the technology to integrate the range of devices in a simple, easy-to-use hub, the 2net Hub, that can make it easier for consumers to connect devices and data in their homes.

A Tipping Point: Democratizing Health Through Participatory Medicine

The exciting part of this ecosystem is not just the range of devices and impressive range of indicia we can monitor, measure, and analyze with the devices in our pockets, many of which did not exist with this level of functionality even 4-5 years ago.  Increasingly the Health 2.0 movement, or those interested in the use of social media and connected health to radically change the traditional top-down orientation of healthcare, view these technologies as a way to democratize health and medicine.  Calls for participatory medicine, or more patient-physician and even health system collaboration, are frequent, and there is even a Society for Participatory Medicine to debate and share information on the movement.  “ePatients” are challenging the traditional medical encounter armed with data they’ve tracked on a mobile or iPhone app and with the latest research or information gleaned from platforms that provide access to clinical trials and data.  Sometimes referred to as “cyberchondriacs”, the message is becoming clearer that we are reaching a tipping point where the practice of medicine and public health are going to change for good.  Information and data flows are no longer a one-way street.

Government and the Need for Public-Private Cooperation

In contrast to many in the field of health technology, I do not necessarily see a single trajectory for where this is heading.  There are many major technological, social, and political roadblocks and obstacle courses to navigate in the coming years.  First, the most difficult challenge is the political rhetoric in the United States concerning healthcare.  The recent Supreme Court decision on the Affordable Care Act and the popular politics leading up to the decision and for the indefinite future actually have little or nothing to do with existing healthcare that actually exists.  There are no death panels; universal coverage is not socialism; and no country outside of places like Somalia and the Eastern Congo believe that the government should not have a place in healthcare.

We live in a time when we’ll soon be spending one (1) out of every five (5) dollars of our GDP on healthcare.  Our employer-sponsored healthcare system makes no business sense and, as stated at the beginning of this article, fails so many who die and suffer needlessly.  Fragmentation and an ad hoc employer-based anti-system is expensive and leaves too many people without insurance, which ends up being a driver for even inflationary healthcare expenditures.

While conservatives bemoan the Supreme Court’s decision to uphold the Affordable Care Act  under Congress’s taxing power, they ought to look at how dysfunction in the system is far more taxing to the average person’s wallet.  Too often the call for “consumer-driven healthcare” becomes a euphemism for cost-shifting to consumers rather than improved quality and cost-effective care.  Even in systems with universal coverage, there are vibrant private sector health care businesses.  From a technological perspective, the government has a vital role in areas such as standards, core infrastructure development, and regulatory frameworks to protect everything from privacy to the security of your health data. The right mix of regulatory standards and frameworks can actually help businesses flourish.  We need a great deal of policy innovation in healthcare to keep pace with technological change. Innovation is not just about simplistic calls for cutting “big government”.

In support of this last point we are have already seen how creative government can support the growth of innovative companies in healthcare and also create jobs.  Health and Human Services (HHS) launched its Health Data Initiative two years ago to open up government health data to entrepreneurs and citizens.  This is health data our tax dollars pay for, but which was left in formats that rendered it not very user-friendly until the Initiative was launched.  An event, the Health Datapalooza, was launched to bring entrepreneurs, venture capitalists, and governments together.  The 2010 event had 48 participants.  In early June 2012, the most recent Datapalooza had over 240 companies and 1,500 attendees and was a dramatic demonstration of how government open data initiatives armed with an entrepreneurial focus can create jobs and new products and services that can have a big impact on health.  Polarized ideological posturing won’t solve the health crisis.

We also have significant technological challenges around the issue of interoperability.  Having the ability to choose different devices and technologies to collect and store our data and to be able to move that data from one health plan to another or across healthcare providers is no easy feat.  Most large healthcare IT programs in the 1990s–early 2000s were very capital intensive, lengthy technology implementations that are expensive to replace.  This has made many healthcare providers wary of new technologies.  One of the issues that both Republicans and Democrats agreed on was the incentive for electronic medical record (EMR) adoption in the stimulus package.  In theory this serves as the foundation for creating data liquidity in the future.  In reality, the practice of interoperability is challenging and is as much a human and organizational challenge as a technological one.  The right approach to interoperability is urgently needed and can help reduce uncertainty and risk for medical device manufacturers and even for those working down stream in the area of analytics.  At the Open mHealth Project (where I’m an advisory board member), we are working on filling in some of these gaps that challenge our ability to create personalized and optimized data ecosystems through an open source architecture.  We believe this will reduce the amount of effort and resources to develop devices and data analytics for evaluating and inferring the impact of interventions.

Data privacy, ethics, and ownership are emerging issues that we need to address as well. The emergence of Big Data allows us to mine and analyze massive amounts of data—both structured (e.g., traditional health services research) and unstructured (e.g., data from Twitter, Facebook, or much of the data in an EMR)—and has brought the issue of privacy to the fore.  It is now possible to de-anonymize data from a large national anonymized survey, for example.  The World Economic Forum recently issued a paper that argues that personal data has now become a new asset class.  Jerry Michalski, in addition, recounts some of the emerging ethical issues that Big Data raises – from the ways data can be used against us (a big issue in healthcare) to making us over-confident in projections and forecasts (as the financial crisis and numerous banking fiascos in recent months, e.g., J.P.Morgan, have demonstrated).  Yet there is much to be gained here from early outbreak detection to creating new efficiencies in health systems and better clinical decision-making with personalized health data.

Much work needs to be done in this area to make sure that the circle of beneficiaries of Big Data is wide and includes the marginalized.  I’ve been advocating the creation of a Global Data Alliance, a public-private partnership that can bring the best minds on data together to create the policy and ethical frameworks that build trust and value across the stakeholder value chain.  A key feature of the Alliance would be to foster public-private partnerships to address market failures in the data value chain, from food systems to healthcare systems and insurance.  Of the utmost importance are innovations that focus on products and services for low-income populations to mitigate their risk.  Risk mitigation, particularly in healthcare, should not be the preserve of the top one percent.  We need to build on Robert Kirkpatrick’s notion of “data philanthropy” for tangible social businesses in the healthcare space that are capable of improving health outcomes and changing risk equations for the most needy.

Last week’s Supreme Court decision is only the beginning of a long road to forge a healthier nation. We have the technological tools to create efficiencies and build transformative health systems. Now we need the human interoperability and creativity to realize the visions the technologists.  That will require political leadership and imagination that has become a scarce resource at a time when we need it most.

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Dr. Jody Ranck has worked on health policy and health technology for over 20 years as a researcher and strategist.  Much of his work has been in global health working on programs in Africa and South Asia.  He has worked extensively with both private sector and government agencies in the area of technology forecasting and innovation strategy.  He recently served on a committee at the Institute of Medicine on the use of information technologies and is an analyst for GigaOM, which recently published his book, “Connected Health: How Mobiles, Cloud and Big Data Will Reinvent Healthcare”.

This article is available online at:
http://www.forbes.com/sites/benkerschberg/2012/07/06/how-connected-health-public-private-cooperation-and-big-data-can-revolutionize-health-care/

Big Data for the Poor

Quentin Hardy, The New York Times, July 5, 2012

ZestCash, founded in 2006, had a winning idea, in principle. It made loans to borrowers with poor credit scores by using complex data analysis to better determine their credit quality. But now it has decided to move out of the business of making loans and instead concentrate on selling its analysis to other lenders serving poorer and marginal borrowers.

“Our mission was to use big data to save the under-banked billions of dollars in high fees,” said Douglas Merrill, a former senior executive at Google who was a co-founder of ZestCash. “We think that will work better if we get our technology into the hands of established providers.”

Mr. Merrill said his company, which has been renamed ZestFinance, has already licensed its technology to Spotloan, a nationwide subprime lender. ZestFinance is also in discussions with two large credit card companies, and with several nonprofits interested in delivering financial services to the under-banked, he said.

Mr. Merrill declined to name these companies and nonprofits. His co-founder, Shawn Budde, was previously with Capital One, where he managed the subprime credit card portfolio.

ZestCash’s idea was to serve the millions of working people in America with limited access to good credit. While some are risky bets for a loan, others are poor in data like long-established work records, or a history of paying conventional credit card loans. When they need money fast, a common recourse is the payday lenders often found in strip malls, who give cash but charge triple-digit annual percentage rates.

Some say these lenders are predatory; they counter that they need to charge high interest to cover their risk. Unlike most payday lenders, ZestCash structured its loans for full repayment of principal and interest before it would grant someone another loan.

ZestCash aimed to move beyond the 40-year-old system of credit scoring by introducing new variables. For example, paying half of one’s income in an expensive city like San Francisco might be a sign of conventional spending, while paying the same amount in cheaper Fresno could indicate profligacy. Giving up a prepaid cellphone, often the phone of necessity for the poor or credit-risky, meant losing a phone number, which indicates a willingness to lose social connections. That was a warning sign. Careful reading of ZestCash’s terms and conditions, which the company could watch by tracking cookies, meant that someone was taking a loan seriously, not just rushing to get the money. That was a positive sign.

Regulation and marketing expenses seem to have played a role in the failure of the company’s business model. Acting as an online alternative to traditional payday lenders, ZestCash spent years getting licenses in just a few states, including Utah and Missouri. While payday lenders charged higher interest rates than ZestCash, they also have locations everywhere, promoting awareness. ZestCash had to pay more for marketing to compete.

Spotloan, the first customer of ZestFinance, is owned by the Turtle Mountain band of Chippewa Indians, in Belcourt, N.D. As a Native American corporation, the company is subject to different regulation than ZestCash was, and may be able to roll out the technology quicker.

Mr. Merrill said conventional credit card companies were looking at his technology both for new ways of spotting risks and as a means to broaden their client bases. “All the lenders are data poor,” he said. “We hope we can make them data rich, insight rich.”

Science in three dimensions: The print revolution

Three-dimensional printers are opening up new worlds to research.

Nicola Jones,  Nature,  July 4, 2012

Christoph Zollikofer witnessed the first birth of a Neanderthal in the modern age. In his anthropology lab at the University of Zurich, Switzerland, in 2007, the skull of a baby Homo neanderthalensis emerged from a photocopier-sized machine after a 20-hour noisy but painless delivery of whirring motors and spitting plastic. This modern miracle had endured a lengthy gestation: it took years for Zollikofer's collaborators to find suitable bones from a Neanderthal neonate, analyse them with a computed-tomography (CT) scanner and digitally stitch them together on the computer screen. The labour, however, was simple: Zollikofer just pressed 'print' on his lab's US$50,000 three-dimensional (3D) printer.

A pioneer in the use of 3D printing for research, Zollikofer started 20 years ago with a prototype that was even more expensive and required toxic materials and solvents — limitations that put off most scientists. But now newer, cheaper technology is catching on. Just as an inkjet printer sprays ink onto a page line by line, many modern 3D devices spray material — usually plastic — layer by layer onto a surface, building up a shape. Others fuse solid layers out of a vat of liquid or powdered plastic, often using ultraviolet or infrared light. 

Any complex shape can be printed, sometimes with the help of temporary scaffolding that is later dissolved or chipped away. These days, personal kits go for as little as $500, says Terry Wohlers, a consultant and market analyst based in Fort Collins, Colorado — although industrial systems cost an average of $73,000. Last year, he says, nearly 30,000 printers were sold worldwide, with academic institutions buying one-third of those in the $15,000–30,000 price range.

Early adopters are using the technology to investigate complex molecules, fashion custom lab tools, share rare artefacts and even print cardiac tissue that beats like a heart. At palaeontology and anthropology conferences, more and more people are carrying printouts of their favourite fossils or bones. “Anyone who thinks of themselves as an anthropologist needs the right computer graphics and a 3D printer. Otherwise it's like being a geneticist without a sequencer,” says Zollikofer.

The printouts are yielding insights that are not possible with more conventional methods. Neanderthal neonate fossils, for example, are extremely rare, so Zollikofer did not want to risk copying his fragile specimen with the usual plaster-casting methods. With the printout, however, Zollikofer could explore the logistics of Neanderthal births. Along with the neonate skull, he printed out an adult, female Neanderthal pelvis and literally re-enacted a delivery. 

Some researchers had speculated that Neanderthals' wide hips made labour easier than it is for modern humans, but Zollikofer's experiment showed that the bigger skulls of Neanderthal neonates counteracted that advantage (M. S. Ponce de León et al. Proc. Natl Acad. Sci. USA 105, 13764–13768; 2008). Like humans today, Neanderthals had the biggest heads — and brains — possible at birth, giving them a jump-start on development.

In his work, Zollikofer swaps back and forth between printed models and virtual ones. The computer models are good for calculating volumes or piecing together bone fragments — researchers can position them in space without gravity causing them to fall. But with the virtual models, he says, “you lose the sensation of touch, and even a notion of the size of the fossils”. The physical models are far better for seeing how pieces should fit together in the first place, he adds.

Molecular playground

Chemists and molecular biologists have long used models to get a feel for molecular structures and make sense of X-ray and crystallography data. Just look at James Watson and Francis Crick, who in 1953 made their seminal discovery of DNA's structure with the help of a rickety construction of balls and sticks.

These days, 3D printing is being used to mock up far more complex systems, says Arthur Olson, who founded the molecular graphics lab at the Scripps Research Institute in La Jolla, California, 30 years ago. These include molecular environments made up of thousands of interacting proteins, which would be onerous-to-impossible to make any other way. With 3D printers, Olson says, “anybody can make a custom model”. But not everybody does: many researchers lack easy access to a printer, aren't aware of the option or can't afford the printouts (which can cost $100 or more).

Yet Olson says that these models can bring important insights. When he printed out one protein for a colleague, they found a curvy 'tunnel' of empty space running right through it. The conduit couldn't be seen clearly on the computer screen, but a puff of air blown into one side of the model emerged from the other. Determining the length of such tunnels can help researchers to work out whether, and how, those channels transport molecules. Doing that on the computer would have required some new code; with a model, a bit of string did the trick.

Software that lets researchers twist and turn such structures on a computer screen is extremely useful, says Olson, but inadequate. Even the most advanced software will let two atoms occupy the same space. And tinkering with molecules inside a computer is a grind — it takes time for the computer to re-render an object after every turn, and interpreting the pictures requires mental effort. Fiddling with a physical model, on the other hand, is more like play. “I don't have to think about it; I just do it,” says Olson.

Olson is now trying to meld the tactile advantages of 3D printing with computer power: he has tagged printed models with small paper labels that can be recognized by a webcam, to create an 'augmented reality' view. In this way, a user can play with a physical model, while at the same time using the computer to explore aspects such as the potential energy of a given molecular arrangement. Olson is also looking forward to using printers that can more easily swap between rigid and bendable materials, so as to better replicate molecular behaviour such as protein folding.

The cellular matrix

Printer 'inks' aren't limited to plastic. Biologists have been experimenting with printing human cells — either individually or in multi-cell blobs — that fuse together naturally. These techniques have successfully produced blood vessels and beating heart tissue. The ultimate dream of printing out working organs is still a long way off — if it proves possible at all. But in the short term, researchers see potential for printing out 3D cell structures far more life-like than the typical flat ones that grow in a Petri dish.

For example, Organovo, a company based in San Diego, California, has developed a printer to build 3D tissue structures that could be used to test pharmaceuticals. The most advanced model it has created so far is for fibrosis: an excess of hard fibrous tissue and scarring that arises from interactions between an organ's internal cells and its outer layer. The company's next step will be to test drugs on this system. “It might be the case that 3D printing isn't the only way to do this, but it's a good way,” says Keith Murphy, a chemical engineer and chief executive of Organovo.

Other groups are using 3D printing of plastic or collagen to construct scaffolds on which cells can grow. Carl Simon, a biologist with the biomaterials group at the US National Institute of Standards and Technology in Gaithersburg, Maryland, says that the intricacies of scaffold shape can help to determine how cells grow, or how stem cells differentiate into different cell types. With 3D printing, researchers have a very controlled way to play with different scaffold configurations to see which work best. One problem, however, is that most 3D printers can produce details on the scale of only tens to hundreds of micrometres, whereas cells sense differences at the single-micrometre level. Top-quality printers can currently achieve 100-nanometre resolutions by using very short laser bursts to cure plastics, says Neil Hopkinson, an engineer who works with 3D printing at the University of Sheffield, UK, but this is “still very much in the lab”.

Custom tools

In the meantime, basic plastic 3D printers are starting to allow researchers to knock out customized tools. Leroy Cronin, a chemist at the University of Glasgow, UK, grabbed headlines this year with his invention of 'reactionware' — printed plastic vessels for small-scale chemistry (M. D. Symes et al. Nature Chem. 4, 349–354; 2012). Cronin replaced the 'inks' in a $2,000 commercially available printer with silicone-based shower sealant, a catalyst and reactants, so that entire reaction set-ups could be printed out. The point, he says, is to make customizable chemistry widely accessible. His paper showed how reactionware might be harnessed to produce new chemicals or to make tiny amounts of specific pharmaceuticals on demand. For now, other chemists see the idea as a clever gimmick, and are waiting to see what applications will follow.

Researchers in other fields have found a more immediate use for the technology. Philippe Baveye, an environmental engineer at Rensselaer Polytechnic Institute in Troy, New York, uses 3D printing to make custom parts for a permeameter — a device used to measure the flow of water through soils. Although commercially available devices are fine for routine work, he has often had to design his own for more precise research — a task that previously required many hours on a lathe. Printing, he says, is much easier.

Perhaps more importantly, Baveye can share his product just by publishing the design file. “The idea of being able to reproduce experiments described in the literature is taking on a new meaning,” he says.

Others agree that the real power of 3D printing lies in its ability to put science into the hands of the many. Cronin wants to enable anyone — whether in the far corners of Africa or in outer space — to print their own tiny drug factory. Museums can already distribute exact copies of rare or delicate fossils as widely as they wish. And students can print out whatever molecule they're trying to come to grips with. “Through 3D printing,' says Olson, “the ability to make physical models has become democratized.”

Automate or Perish

Successful businesses will be those that optimize the mix of humans, robots, and algorithms.

Antonio Regalado, Technology Review, July 5, 2012

In Automate This, a book due out next month, author and entrepreneur Christopher Steiner tells the story of stockbroker Thomas Peterffy, the creator of the first automated Wall Street trading system. Using a computer to execute trades, without humans entering them manually on a keyboard, was controversial in 1987—so controversial that Nasdaq pressured him to unplug from its network. Then, with a wink, Peterffy built an automated machine that could tap out the trades on a traditional keyboard—technically obeying Nasdaq rules. Peterffy made $25 million in 1987 and is now a billionaire.

Today, automated trading bots account for nearly three-quarters of U.S. equity trading by volume. Trading houses plow millions into fiber optics and microwave dishes so their algorithms can send trades a millisecond faster than the next guys'. And although the first trading robot was built 25 years ago, most of the change on Wall Street has occurred during just the last few years. When it comes to automation, we may be in the elbow of an exponential curve. 

In this month's Technology Review business report, we look at what it takes to succeed at the cutting edge of automation. Consider Amazon. The company not only automated book buying but also turned the computer systems it built to do so into a service called Amazon Web Services, making them available for anyone wanting to repeat the feat. 

And now Amazon's founder, Jeff Bezos, is placing new bets on automation. In March, Amazon paid $775 million for Kiva Systems, a company that makes robotic dollies that zip across warehouse floors carrying shelves full of goods. Kiva found it was more productive to have the humans who "pick, pack, and stow" items stay in one place and let intelligent shelves come to them. Among other reasons, Amazon said, it bought the robotics firm because the technology offered the chance to reduce labor requirements at its dozens of warehouses. 

This is an example of what is going on in the economy more broadly. As the MIT economist David Autor has argued, the job market is being "hollowed out." High-wage, high-skill employment is still being created—and so are many poorly compensated service industry jobs for food preparers, home care aides, and others. It's the jobs in the middle that are disappearing: certain clerical, sales, and administrative jobs and some on factory floors. 

Now a combination of growing computing power and advances in data crunching mean automation is primed to threaten not just tax preparers and travel agents but higher-rung jobs such as those in the medical and legal professions, where software can increasingly do things like analyze images and understand speech more accurately and in more contexts than ever before. Any work that is repetitive or fairly well structured is open to full or partial automation. Being human confers less and less of an advantage these days. 

Some economists believe automation may explain why U.S. economic output has grown since 2007 while the number of jobs has fallen. That kind of dislocation is unusual. The U.S. economy has evolved from agriculture to manufacturing to service industries. Each time jobs were destroyed in one sector, they were replaced elsewhere. Data from the Bureau of Labor Statistics provide some clues to what the next economy will look like. Among the 10 fastest-growing new job categories between 2009 and 2011, seven have the word "computer" or "software" in them.

Some say what's taking shape is a more productive symbiosis between man and machine—and successful businesses will be the ones that optimize it. Rodney Brooks, founder of ReThink Robotics in Boston, believes that a new type of general-purpose robot could reinvigorate manufacturing. The machines he's building aren't hardwired for any one job; they're flexible, so many types of businesses could use them for a variety of production tasks. The company's aim is to democratize automation the way the PC did for computing, spurring similar efficiency gains.

There's definitely good news here: more people than ever have access to affordable, powerful tools that can help them and their businesses become more productive. Take Todd Ruback, a privacy lawyer in Warren, New Jersey, who handles legal paperwork for companies that have lost sensitive data like credit card numbers. The job involves filing forms and notifying consumers in dozens of states, each with slightly different laws and deadlines. He's been field-testing software made by a company called Co3 Systems that automates much of the process. It walks attorneys through what they need to do and prints out the right form letters for each state. 

Ruback estimates that the software cuts the time it takes him to handle a case by 10 to 20 percent. But don't lawyers bill by the hour? Why would Ruback want something that makes it all go faster? It's pretty simple, he says. The software makes him more efficient. And if he doesn't automate, the other guy will.

Big Thinkers, Big Data, and "Flawed" Economics

Mark Thoma, The Fiscal Times, July 3, 2012

After attending last year’s Economics Nobel Laureates Meeting in Lindau, Germany, I was very critical of what I heard from the laureates at the meeting.  The conference is intended to bring graduate students together with the Nobel Prize winners to learn about fruitful areas for future research. Yet, with all the challenges the Great Recession posed for macroeconomic models, very little of the conference was devoted to anything related to the Great Recession. And when it did come up, the comments were “all over the map.” And some, such as Ed Prescott, were particularly appalling as they made very obvious political statements in the guise of economic analysis. I felt bad for the students who had come to the conference hoping to gain insight about where macroeconomics was headed in the future.

I am back at the meetings this year, but the topic is physics, not economics, and it’s pretty clear that most physicists think they have nothing to learn from lowly economists. That’s true even when they are working on problems in economics and finance.

But they do have something to learn. Macroeconomic models are far from perfect, but there are still important lessons within them. One important lesson comes from Robert Lucas, a Nobel laureate in economics, and it relates directly to the influx of physicists into the financial sector.

For the most part, the empirical tools used by physicists working in finance are theoretical data grubbing techniques that search for exploitable correlations in historical data. For example, if there is an asset price that is correlated with an observable variable, hopefully with a lag, then price movements can be predicted – the higher the correlation the better the prediction – and used to make profit or hedge against risk. But as the Lucas critique points out, relying on these correlations can be dangerous since they can change when people try to exploit them. 

Physicists are used to thinking about inanimate objects that do not react to policies or experiments that might affect them. To take a simple but illustrative example, suppose you tell your TV that you are about to throw a brick through the screen. Even after being warned, the TV will just sit there and be destroyed as the brick is hurled its way. But if you tell humans you are about to throw a brick at them, and the threat has some credibility, they will take action to avoid harm. People, unlike the objects physicists are used to working with, won’t just stand there and let the brick hit them in the head. This introduces additional complexity that is not present in the models that physicists rely upon – modeling humans is much harder than modeling brainless physical processes.

Old fashion economic models were much like the models that physicists use. That is, expectations were modeled in a way that did not allow people to move out of the way of the brick even if they knew it was coming. They were just like the TV. For example, policies that stimulated the economy by lowering real wages could not be offset and undermined by workers demanding wage increases. Because of this, policy was maximally effective. 

In modern macroeconomic models expectations are modeled in a more reasonable way, and agents are able to take action to avoid the harmful byproducts of macroeconomic policy. In present day Classical models, agents are sufficiently nimble to avoid harm as they dance effortlessly out of the way when the brick is tossed. In these models, policy doesn’t matter at all. In New Keynesian models agents fully anticipate policies, but frictions prevent them from fully avoiding harm. It’s similar to one of those bad dreams where you know the monster is coming, but your feet just won’t move fast enough to get out of the way. In New Keynesian models, sluggishness allows the brick to deliver a glancing, but not fatal blow – that is, policy is effective, but not as effective as when agents do not move at all.

The point is that when analysts from physics or anywhere else attempt to exploit correlations in the data, someone will be harmed – the profits come from somewhere – and people will take action to insulate themselves from those losses. The actions they take will differ from the historical correlations, and in order to understand and predict what will actually happen a theoretical model of some sort is needed. At the very least, physicists need to understand that people are different from the inanimate objects they are used to modeling, and failing to account for how agents will respond to an attempt to exploit historical correlations can lead to large – sometimes very large – analytical errors. 

I am not arguing that the influx of physicists into finance caused the crisis. But the failure to understand and appreciate what economic theory has to offer, even with all its flaws, exacerbated losses and amplified the downturn. Physicists shouldn’t forget that economists, even with their imperfect theories, can help them avoid these big and costly mistakes.

Your E-Book Is Reading You

Digital-book publishers and retailers now know more about their readers than ever before. How that's changing the experience of reading.

Alexandra Alter, The Wall Street Journal, July 19, 2012

It takes the average reader just seven hours to read the final book in Suzanne Collins's "Hunger Games" trilogy on the Kobo e-reader—about 57 pages an hour. Nearly 18,000 Kindle readers have highlighted the same line from the second book in the series: "Because sometimes things happen to people and they're not equipped to deal with them." And on Barnes & Noble's Nook, the first thing that most readers do upon finishing the first "Hunger Games" book is to download the next one.

For centuries, reading has largely been a solitary and private act, an intimate exchange between the reader and the words on the page. But the rise of digital books has prompted a profound shift in the way we read, transforming the activity into something measurable and quasi-public. Eben Shapiro explains on Lunch Break.

In the past, publishers and authors had no way of knowing what happens when a reader sits down with a book. Does the reader quit after three pages, or finish it in a single sitting? Do most readers skip over the introduction, or read it closely, underlining passages and scrawling notes in the margins? Now, e-books are providing a glimpse into the story behind the sales figures, revealing not only how many people buy particular books, but how intensely they read them.

We Know What You Read

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As a collected by digital publisher Coliloquy from romance-novel readers, has a European accent and is in his 30s with black hair and green eyes.

For centuries, reading has largely been a solitary and private act, an intimate exchange between the reader and the words on the page. But the rise of digital books has prompted a profound shift in the way we read, transforming the activity into something measurable and quasi-public.

The major new players in e-book publishing—Amazon, Apple and Google—can easily track how far readers are getting in books, how long they spend reading them and which search terms they use to find books. Book apps for tablets like the iPad, Kindle Fire and Nook record how many times readers open the app and how much time they spend reading. Retailers and some publishers are beginning to sift through the data, gaining unprecedented insight into how people engage with books.

Publishing has lagged far behind the rest of the entertainment industry when it comes to measuring consumers' tastes and habits. TV producers relentlessly test new shows through focus groups; movie studios run films through a battery of tests and retool them based on viewers' reactions. But in publishing, reader satisfaction has largely been gauged by sales data and reviews—metrics that offer a postmortem measure of success but can't shape or predict a hit. That's beginning to change as publishers and booksellers start to embrace big data, and more tech companies turn their sights on publishing.

Barnes & Noble, which accounts for 25% to 30% of the e-book market through its Nook e-reader, has recently started studying customers' digital reading behavior. Data collected from Nooks reveals, for example, how far readers get in particular books, how quickly they read and how readers of particular genres engage with books. Jim Hilt, the company's vice president of e-books, says the company is starting to share their insights with publishers to help them create books that better hold people's attention.
The stakes are high for the company as it seeks a greater share of the e-book market. Sales of Nook devices rose 45% this past fiscal year, and e-book sales for the Nook rose 119%. Overall, Nook devices and e-books generated $1.3 billion, compared to $880 million the previous year. Microsoft recently invested $300 million for a 17.6% stake of the Nook.

Mr. Hilt says that the company is still in "the earliest stages of deep analytics" and is sifting through "more data than we can use." But the data—which focuses on groups of readers, not individuals—has already yielded some useful insights into how people read particular genres. Some of the findings confirm what retailers already know by glancing at the best-seller lists. 
For example, Nook users who buy the first book in a popular series like "Fifty Shades of Grey" or "Divergent," a young-adult series by Veronica Roth, tend to tear through all the books in the series, almost as if they were reading a single novel.

Barnes & Noble has determined, through analyzing Nook data, that nonfiction books tend to be read in fits and starts, while novels are generally read straight through, and that nonfiction books, particularly long ones, tend to get dropped earlier. Science-fiction, romance and crime-fiction fans often read more books more quickly than readers of literary fiction do, and finish most of the books they start. Readers of literary fiction quit books more often and tend skip around between books.

Those insights are already shaping the types of books that Barnes & Noble sells on its Nook. Mr. Hilt says that when the data showed that Nook readers routinely quit long works of nonfiction, the company began looking for ways to engage readers in nonfiction and long-form journalism. They decided to launch "Nook Snaps," short works on topics ranging from weight loss and religion to the Occupy Wall Street movement.

Pinpointing the moment when readers get bored could also help publishers create splashier digital editions by adding a video, a Web link or other multimedia features, Mr. Hilt says. Publishers might be able to determine when interest in a fiction series is flagging if readers who bought and finished the first two books quickly suddenly slow down or quit reading later books in the series.

"The bigger trend we're trying to unearth is where are those drop-offs in certain kinds of books, and what can we do with publishers to prevent that?" Mr. Hilt says. "If we can help

The Future of Smart Systems

Janna Anderson and Lee Rainie,  Pew Internet and American Life Project, June 29, 2012

Overview

By 2020, experts think tech-enhanced homes, appliances, and utilities will spread, but many of the analysts believe we still won’t likely be living in the long-envisioned ‘Homes of the Future.’

Hundreds of tech analysts foresee a future with “smart” devices and environments that make people’s lives more efficient.

But they also note that current evidence about the uptake of smart systems is that the costs and necessary infrastructure changes to make it all work are daunting. And they add that people find comfort in the familiar, simple, “dumb” systems to which they are accustomed.

Some 1,021 Internet experts, researchers, observers, and critics were asked about the “home of the future” in an online, opt-in survey. The result was a fairly even split between those who agreed that energy- and money-saving “smart systems” will be significantly closer to reality in people’s homes by 2020 and those who said such homes will still remain a marketing mirage.

About the Survey

The survey results are based on a non-random, opt-in, online sample of 1,021 Internet experts and other Internet users, recruited via email invitation, Twitter or Facebook from the Pew Research Center’s Internet & American Life Project and the Imagining the Internet Center at Elon University. Since the data are based on a non-random sample, a margin of error cannot be computed, and the results are not projectable to any population other than the experts in this sample.