Gartner: Social business efforts largely unsuccessful so far

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Gartner: Social business efforts largely unsuccessful so far

Some 80 percent of social business software projects "will not achieve intended benefits" through 2015, according to the analyst firm

By Chris Kanaracus

January 29, 2013 04:49 PM ET

IDG News Service - Many large companies are embracing internal social networks, but for the most part, they're not getting much from them, according to analyst firm Gartner.

By 2016, some 50% of enterprises "will have internal Facebook-like social networks," and 30% of these will be considered to be as crucial as email and telephones, a Gartner study announced on Tuesday states.

However, through 2015, 80% "of social business efforts will not achieve the intended benefits due to inadequate leadership and an overemphasis on technology," according to the report.

That's because social software doesn't work like an ERP (enterprise resource planning) application, which uses a "push" mentality, wherein workers "were trained on an app and then were expected to use it," Gartner said.

Social software involves a "'pull' approach, one that engages workers and offers them a significantly better way to work," the report adds. "In most cases, they can't be forced to use social apps -- they must opt-in."

A series of conditions must be present for social software to be successful, including a "meaningful and specific purpose," "a critical mass of colleagues actively using it," and integration with other applications workers are using, Gartner said.

The report comes after the enterprise social software market has already undergone some significant changes. Many independent providers have been snapped up by larger players, while other big vendors such as Oracle have developed social networking software of their own.

CRM (customer relationship management) vendor Salesforce.com has gone even further, reorienting its general strategy around the theme of social business.

While most companies' social software projects lack maturity, "lessons are already being learned and better practices are emerging," Gartner said.

Still, customers just starting out with social software should be careful when they choose their first pilot, "since it will set the tone for subsequent initiatives," the report adds.

Chris Kanaracus covers enterprise software and general technology breaking news for The IDG News Service. Chris' email address is Chris_Kanaracus@idg.com

GE to IBM: Watch your Data, We Are Coming

General Electric, the massive industrial conglomerate, will not be content to let IT leaders like IBM and Google hog all the glory in the internet of things era.

Barb Darrow, GigaOm, January 25, 2013

It sure looks like General Electric — the conglomerate that builds stuff ranging from appliances to jet engines — is spending a ton of time and resources to boost its profile in high (as opposed to “low”) tech. In fact it looks like it’s waging a massive PR campaign to show that it is not some grimy industrial relic but a force at the cutting edge of big data and “the internet of things.” If you don’t believe it, just download its November report on the industrial internet, which we covered here.

The latest evidence of this push? An interview with William Ruh, VP of software for GE Research, in ComputerWeekly.com. In the piece, Ruh appeared to take a veiled swipe IBM — which loves to portray itself as the thought leader in bleeding-edge tech and the kingpin in tech patents. (For the record, in 2012 GE came in ninth in patents with a total of 1,652 compared to IBM’s 6,478 — but who’s counting?)

Ruh said the airline industry has gathered tons of data about how jet engines have performed over the past two decades and that historical data should help guide predictive maintenance going forward. Ruh told ComputerWeekly:

“In emerging markets, we are seeing dirt and sandy environments … How are these affecting aero engines? [Business intelligence] cannot answer this. Nor can a supercomputer … Watson cannot tell me when this machine part will break.”

Watson is IBM’s much-hyped computer that boasts human-like thought processes and beat the human champion in Jeopardy a few years back.


GE is banking on the growing acknowledgement that machine data — information generated and collected by the types of industrial gear it makes — gives it an entry into the booming world of big data. That’s probably why GE CEO Jeff Immelt has been cropping up in a lot of interesting venues, including in an interview with Om Malik last month. And why GE came to San Francisco to announce its “Industrial Internet Quests” and tap into the wealth of software and data expertise there. As my colleague Katie Fehrenbacher put it at the time, the quest “calls on developers, data scientists and designers to make algorithms and applications that can increase productivity for the health and aviation sectors” — all sectors where GE plays.
It may be easy for folks in the valley to forget that GE has thousands of its own software developers on staff and builds sophisticated medical imaging and other high-tech gear: it does have credibility. And, at a time when the emphasis on making and building actual products is more valued, GE has lessons to teach.
The conglomerate obviously wants to be seen as a leader in this realm and won’t be content to let the likes of IBM hog all the glory in the internet of things era. After all, it builds an awful lot of those “things.”

Bill gates on Metrics and Big Data

From the fight against polio to fixing education, what's missing is often good measurement and a commitment to follow the data. We can do better. We have the tools at hand.

Bill Gates, The Wall Street Journal, January 25, 2013

By custom, many Ethiopian parents won't name a child for weeks, in case the baby dies. Sebsebila Nassir, pictured above with a health worker, named her newborn daughter Amira—'princess' in Arabic—on her immunization card the day she was born.

We can learn a lot about improving the 21st-century world from an icon of the industrial era: the steam engine.

Harnessing steam power required many innovations, as William Rosen chronicles in the book "The Most Powerful Idea in the World." Among the most important were a new way to measure the energy output of engines and a micrometer dubbed the "Lord Chancellor" that could gauge tiny distances.

Such measuring tools, Mr. Rosen writes, allowed inventors to see if their incremental design changes led to the improvements—such as higher power and less coal consumption—needed to build better engines. There's a larger lesson here: Without feedback from precise measurement, Mr. Rosen writes, invention is "doomed to be rare and erratic." With it, invention becomes "commonplace."

In the past year, I have been struck by how important measurement is to improving the human condition. You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal—in a feedback loop similar to the one Mr. Rosen describes.

This may seem basic, but it is amazing how often it is not done and how hard it is to get right. Historically, foreign aid has been measured in terms of the total amount of money invested—and during the Cold War, by whether a country stayed on our side—but not by how well it performed in actually helping people. Closer to home, despite innovation in measuring teacher performance world-wide, more than 90% of educators in the U.S. still get zero feedback on how to improve.

An innovation—whether it's a new vaccine or an improved seed—can't have an impact unless it reaches the people who will benefit from it. We need innovations in measurement to find new, effective ways to deliver those tools and services to the clinics, family farms and classrooms that need them.

I've found many examples of how measurement is making a difference over the past year—from a school in Colorado to a health post in rural Ethiopia. Our foundation is supporting these efforts. But we and others need to do more. As budgets tighten for governments and foundations world-wide, we all need to take the lesson of the steam engine to heart and adapt it to solving the world's biggest problems.

One of the greatest successes in terms of using measurement to drive global change has been an agreement signed in 2000 by the United Nations. The Millennium Development Goals, supported by 189 nations, set 2015 as a deadline for making specific percentage improvements across a set of crucial areas—such as health, education and basic income. Many people assumed the pact would be filed away and forgotten like so many U.N. and government pronouncements. The decades before had brought many well-meaning declarations to combat problems from nutrition to human rights, but most lacked a road map for measuring progress. However, the Millennium goals were backed by a broad consensus, were clear and concrete, and brought focus to the highest priorities.

When Ethiopia signed on to the Millennium goals in 2000, the country put hard numbers to its ambition to bring primary health care to all of its citizens. The concrete goal of reducing child mortality by two-thirds created a clear target by which to measure success or failure. Ethiopia's commitment attracted a surge of donor money toward improving the country's primary health-care services.

With help from the Indian state of Kerala, which had built a successful network of community health-care posts, Ethiopia launched its own program in 2004 and today has more than 15,000 health posts staffed by 34,000 workers. (This is one of the greatest benefits of measurement—the ability it gives government leaders to make comparisons across countries and then learn from the best.)

Last March, I visited the Germana Gale Health Post in the Dalocha region of Ethiopia, where I saw charts of immunizations, malaria cases and other data plastered to its walls. This information goes into a system—part paper-based and part computerized—that helps government officials see where things are working and to take action in places where they aren't. In recent years, data from the field have helped the government respond more quickly to outbreaks of malaria and measles. Perhaps even more important, the government previously didn't have any official record of a child's birth or death in rural Ethiopia. It now tracks those metrics closely.

The health workers provide most services at the posts, though they also visit the homes of pregnant women and sick people. They ensure that each home has access to a bed net to protect the family from malaria, a pit toilet, first-aid training and other basic health and safety practices. All these interventions are quite simple, yet they've dramatically improved the lives of people in this country.

Consider the story of one young mother in Dalocha. Sebsebila Nassir was born in 1990, when about 20% of all children in Ethiopia did not survive to see their fifth birthdays. Two of Sebsebila's six siblings died as infants. But when a health post opened its doors in Dalocha, life started to change. Last year when Sebsebila became pregnant, she received regular checkups. On Nov. 28, Sebsebila traveled to a health center where a midwife was at her bedside during her seven-hour labor. Shortly after her daughter was born, a health worker gave the baby vaccines against polio and tuberculosis.

According to Ethiopian custom, parents wait to name a baby because children often die in the first weeks of life. When Sebsebila's first daughter was born three years ago, she followed tradition and waited a month to bestow a name. This time, with more confidence in her new baby's chances of survival, Sebsebila put "Amira"—"princess" in Arabic—in the blank at the top of her daughter's immunization card on the day she was born. Sebsebila isn't alone: Many parents in Ethiopia now have the confidence to do the same.

Ethiopia has lowered child mortality more than 60% since 1990, putting the country on track to achieve the Millennium goal of lowering child mortality two-thirds by 2015, compared with 1990. Though the world won't quite meet the goal, we've still made great progress: The number of children under 5 years old who die world-wide fell to 6.9 million in 2011, down from 12 million in 1990 (despite a growing global population).
Another story of success driven by better measurement is polio. Starting in 1988, global health organizations (along with many countries) established a goal of eradicating polio, which focused political will and opened purse strings to pay for large-scale immunization campaigns. By 2000, the virus had nearly been wiped out; there are now fewer than 1,000 cases world-wide.

But getting rid of the very last cases is the hardest part. In order to stop the spread of infections, health workers have to vaccinate nearly all children under the age of 5 multiple times a year in polio-affected countries. There are now just three countries that have not eliminated polio: Nigeria, Pakistan and Afghanistan. I visited northern Nigeria four years ago to try to understand why eradication is so difficult there. I saw that routine public health services were failing: Fewer than half the kids were getting vaccines regularly. One huge problem was that many small settlements in the region were missing from vaccinators' hand-drawn maps and lists documenting the locations of villages and numbers of children.

To fix this, the polio workers walked through all high-risk areas in the northern part of the country, which enabled them to add 3,000 previously overlooked communities to the immunization campaigns. The program is also using high-resolution satellite images to create even more detailed maps. As a result, managers can now allocate vaccinators efficiently.

What's more, the program is piloting the use of phones equipped with a GPS application for the vaccinators. Tracks are downloaded from the phone at the end of the day so managers can see the route the vaccinators followed and compare it to the route they were assigned. This helps ensure that areas that were missed can be revisited.

I believe these kinds of measurement systems will help us to finish the job of polio eradication within the next six years. And those systems can be used to help expand routine vaccination and other health activities, which means the legacy of polio eradication will live beyond the disease itself.

Another place where measurement is starting to lead to vast improvements is in education.

In October, Melinda and I sat among two dozen 12th-graders at Eagle Valley High School near Vail, Colo. Mary Ann Stavney, a language-arts teacher, was leading a lesson on how to write narrative nonfiction pieces. She engaged her students, walking among them and eliciting great participation. We could see why Mary Ann is a master teacher, a distinction given to the school's best teachers and an important component of a teacher-evaluation system in Eagle County.

Ms. Stavney's work as a master teacher is informed by a three-year project our foundation funded to better understand how to build an evaluation and feedback system for educators. Drawing input from 3,000 classroom teachers, the project highlighted several measures that schools should use to assess teacher performance, including test data, student surveys and assessments by trained evaluators. Over the course of a school year, each of Eagle County's 470 teachers is evaluated three times and is observed in class at least nine times by master teachers, their principal and peers called mentor teachers.

The Eagle County evaluations are used to give a teacher not only a score but also specific feedback on areas to improve and ways to build on their strengths. In addition to one-on-one coaching, mentors and masters lead weekly group meetings in which teachers collaborate to spread their skills. Teachers are eligible for annual salary increases and bonuses based on the classroom observations and student achievement.
The program faces challenges from tightening budgets, but Eagle County so far has been able to keep its evaluation and support system intact—likely one reason why student test scores have improved in Eagle County over the past five years.

I think the most critical change we can make in U.S. K–12 education, with America lagging countries in Asia and Northern Europe when it comes to turning out top students, is to create teacher-feedback systems that are properly funded, high quality and trusted by teachers.

And there are plenty of other areas where our ability to measure can improve people's lives in powerful ways—areas where we are falling short, unnecessarily.

In poor countries, we still need better ways to measure the effectiveness of the many government workers providing health services. They are the crucial link bringing tools such as vaccines and education to the people who need them most. How well trained are they? Are they showing up to work? How can measurement enable them to perform their jobs better?

In the U.S., we should be measuring the value being added by colleges. Currently, college rankings are focused on inputs—the scores and quality of students entering college—and on judgments and prejudices about a school's "reputation." Students would be better served by measures of which colleges were best preparing their graduates for the job market. They then could know where they would get the most for their tuition money.

In agriculture, creating a global productivity target would help countries focus on a key but neglected area: the efficiency and output of hundreds of millions of small farmers who live in poverty. It would go a long way toward reducing poverty if we had public scorecards showing how developing-country governments, donors and others are helping those farmers.

And if I could wave a wand, I'd love to have a way to measure how exposure to risks like disease, infection, malnutrition and problem pregnancies impact children's potential—their ability to learn and contribute to society. Measuring that could help us quantify the broader impact of those risks and help us tackle them.
The lives of the poorest have improved more rapidly in the past 15 years than ever before. And I am optimistic that we will do even better in the next 15 years. The process I have described—setting clear goals, choosing an approach, measuring results, and then using those measurements to continually refine our approach—helps us to deliver tools and services to everybody who will benefit, be they students in the U.S. or mothers in Africa. Following the path of the steam engine long ago, thanks to measurement, progress isn't "doomed to be rare and erratic." We can, in fact, make it commonplace.

Has Big Data Reached Its Moment of Disillusionment?

Aril Hasseldahl, The Wall Street Journal, January 24, 2013


Last year was a year when the phrase “Big Data” was all over the place. Dig through the troves of data your business generates, the thinking goes, and some useful business intelligence falls out. That, at least, is the idea, and there are numerous companies — some startups, some big and established players — trying to build business plans on different aspects of that idea.

But in the course of being reduced to a simple buzzy phrase, Big Data as a concept implies some expectations, some realistic, some undoubtedly not. The research house Gartner has a phrase for this tendency as well: The Hype Cycle.

The Hype Cycle goes like this: A new technology that promises to fundamentally “change everything” gets talked up incessantly in the press and at industry events and often also in research reports. At some point the chatter peaks, and expectations reach a fever pitch. Soon, maybe a year or two after it all started to build and some money has been spent and everything that was supposed to have changed for the better actually hasn’t, the narrative focus turns negative. What seemed so brilliant and earthshaking 18 months ago, seems in restrospect to have been an ill-advised waste of time, money and attention.

This is what Gartner calls the “Trough of Disillusionment” phase of the Hype Cycle.

Gartner analyst Svetlana Sicular argues in a blog post that Big Data may have reached that point. She has been “hearing from people in the center of the Hadoop movement,” the open-source technology central to companies like Cloudera, Hortonworks and MapR. She also presents a video of a Hadoop gathering called Elephant Riders where reps from these three companies are debating its current state. (It’s about 90 minutes and if you’re so inclined, you can see it here.)

By Gartner’s standards, the trough of disillusionment may indeed have arrived, though you certainly wouldn’t be able to tell from the level of investment interest in companies like Cloudera, which late last year raised a massive $65 million round of funding.

One source of that disillusionment, she writes, is that companies are struggling with a basic problem: What questions do you attempt to answer with your data in the first place? “Several days ago, a financial industry client told me that framing a right question to express a game-changing idea is extremely challenging,” Sicular wrote. “First, selecting a question from multiple candidates; second, breaking it down to many sub-questions; and, third, answering even one of them reliably. It is hard.”

Hadoop doesn’t exactly make that process any easier. Once you’ve decided to use it, getting anything useful out of it requires some pretty specialized knowledge and training, and finding the right people to do that isn’t easy. But the industry is beginning to respond to that need: Startups like Mortar Data have sought to make Hadoop more readily accessible to mainstream programmers, while another called Trifacta makes the resulting data easier to manipulate.

And versions of Hadoop itself are getting incrementally easier to work with. Hortonworks, for example, recently released HDP 1.2, a new version of the open-source platform, but also Sandbox, a set of training tools that lets developers play around with Hadoop and get a feel for its use.

I talked with Hortonworks CEO Rob Bearden recently, and he said that, in 2011, companies had no idea what Hadoop could be used for, then spent 2012 experimenting with it, and now want to get some real-world value out of it in 2013. “This year, all the technology is coming together in a way that is consumable,” he said. “In the last quarter of last year we saw a lot of interesting production environments. Now the objectives are becoming clear for getting useful in 2013.”

The next milestone in the Hype Cycle, Sicular writes, is negative press. Eventually it’s followed by a period called the “Slope of Enlightenment,” and finally the “Plateau of Productivity.” It’s nice to know there could be a positive conclusion to all this somewhere down the road.

Important Computing Development!

Scientists have stored audio and text on fragments of DNA and then retrieved them with near-perfect fidelity a technique that eventually may provide a way to handle the overwhelming data of the digital age.They later were able to retrieve them with 99.99% accuracy. 

See:
Naik, Gautam. "Storing Digital Data in DNA." The Wall Street Journal, January 24, 2013.


Cookson, Clive. "Scientists Look to DNA for Data Storage." The Wall Street Journal, January 23, 2013. 

Goldman, Nick, et al. "Towards Practical, High-Capacity, Low-Maintenance Information Storage in Synthesized DNA." Nature, January 23, 2013.  

OF INTEREST: Max Levchin (Former CTO of Paypal)'s keynot at DLD on Big Data

This is the approximate text of the keynote I gave at DLD13 today in Munich. I felt my delivery of this (admittedly, relatively dense) material was not the best, but the content is crucially important. To that end, I am posting the notes here. They were edited for grammar beyond the basics, so you will have to forgive the occasional fifth-grade prose.

Max Levchin, January 21, 2013
Many of today’s big data companies are trying to tackle problems that just aren’t nearly big enough. 

Most are focused on marginally improving existing, digital businesses, but I believe the next big wave of opportunities exists in centralized processing of data gathered from primarily analog systems.

At PayPal, where I was the CTO, we succeeded because we gained deep understanding of the immense quantities of behavioral data that we captured in processing millions of transactions per day. We learned so much about our customers, that we could predict their intentions, and prevent vast majority of intentional fraud. 

At HVF, the project I began in 2011, we seek to create businesses that improve the analog, real world through deep understanding of data. I will tell you more about that in a bit, but first let me expand on what I mean by “digital sensors over analog data.”

Collaborative consumption is a huge current trend. To me, it started to make sense with Über — idling black limo cars put to better use. Über (where I am now an investor) created a simple piece of software to send the idle ones to price-insensitive consumers in need of a cab with a bit of flair. 

The world of real things is very inefficient: slack resources are abundant, so are the companies trying to rationalize their use. Über, AirBnB, Exec, GetAround, PostMates, ZipCar, Cherry, Housefed, Skyara, ToolSpinner, Snapgoods, Vayable, Swifto…it’s an explosion! What enabled this? Why now? It’s not like we suddenly have a larger surplus of black cars than ever before.

Examine the DNA of these businesses: resource availability and demand requests — highly analog, as this is about cars, drivers, and passengers — is captured at the edge, automatically where possible, then transmitted and stored, then processed centrally. Requests are queued at the smart center, and a marketplace/auction is used to allocate them, matches are made and feedback is given in real time. 

Utilization per dollar goes up, because there is simply less idle time! So efficient is this new approach in fact that limo companies started hiring drivers to drive for Über only.  

I am ignoring some key details, like the need for mutual trust between participants that is at least initially enabled by the presence of a trusted third party, and the feedback loop from consumers of the service, but at its core, these business all look very similar.

A key revolutionary insight here is not that the market-based distribution of resources is a great idea — it is the digitalization of analog data, and its management in a centralized queue to create amazing new efficiencies.

Consider the cab calling experience vs Über. When dialing up a cab, you are managing your own spot in the queue. If you hang up in anger, you go back to the end. If you stay on, listening to the on-call tune, you may be an infinite loop, as there is no feedback! 

And even if you are willing to pay a hundred times more than everyone else waiting ahead of you in line to speak to dispatch, you never get to express that demand. The data exists in an analog-only format, and it moves at analog-only speeds.

With Über, the queue can be managed centrally (because the information is converted to a digital format at the edge) with nearly complete transparency to you — you know when the resources you want become available, you know how long you have to wait for them, and most importantly, you are generally assured through feedback that you have not been forgotten or ignored.

So why is all this now? Cheap digital sensors over analog resources (cars, houses, humans, etc) — AT&T pays for your GPS… so you’d consume more data of course. Mobile broadband, of course. Even more key is the critical mass of pretty smart devices that are clients for real-time participation in queue management with feedback. It’s the smart-enough terminal model. 

As an aside, consumers want sexy sensors that give visual feedback to motivate action (and more data) — like the Nike Fuel band. Machines, on the other hand, just want cheap sensors to get more data from.

I sometimes imagine the low-use troughs of sinusoidal curves utilization of all these analog resources being pulled up, filling up with happy digital usage. 

Private jets spend 1h per day on average in the air. BlackJet promises to make that number closer to the commercial average — 10h/day. And not everyone in a suburban neighborhood needs their own lawn mower — they need an app to schedule one of the local kids to come by and take care of their overgrown grass. 

The defensibility of these businesses lies in their ability to build a network effect — a network effect of data. Once a business understands substantially more than any one of the resources managed in their queue, it’s effectively impossible to compete with them on price — they can always see more of the usage as it happens, and price it more efficiently, pushing any competition out. 

So what other businesses can we expect to emerge in analog-data-driven, central-intelligence queue marketplace businesses? Some interesting ones are probably already being built: a market for private neighborhood security (off-duty cops)? An auction for short-term patent licenses (litigator included)? 

Technology already enables efficient redistribution for your spare change: it’s Kickstarter and AngelList. We will definitely see dynamically-priced queues for confession-taking priests, and therapists!

How about dynamic pricing for brain cycles? We have been maximizing utilization of very high-value, very low-frequency specialists — today you can already rent the brain of a data-mining genius via Kaggle by the hour, tomorrow by brain-hour. Just like the SETI@Home screensaver “steals” CPU cycles to sift through cosmic radio noise for alien voices, your brain plug firmware will earn you a little extra cash while you sleep, by being remotely programmed to solve hard problems, like factoring products of large primes.

There is also a neat symmetry to this analog-to-digtail transformation — enabling centralization of unique analog capacities. As soon as the general public is ready for it, many things handled by a human at the edge of consumption will be controlled by the best currently available human at the center of the system, real time sensors bringing the necessary data to them in real time. The freshest, smartest pilot, most familiar with the particular complicated airport will land your plane — via remote control.   

So what’s after that? This is where it gets really interesting. These new modes of operation — remote controlled cars and planes flown by pilots you can’t see, rides in quasi-cabs with people you have never met, legal advice from lawyers whose license you cannot really check. This is going to add a huge amount of new kinds of risks. 

But as a species, we simply must take these risks, to continue advancing, to use all available resources to their maximum. Yet these risks are real, and they cannot be ignored.

The way to deal with risk is of course some form of insurance. Modeling loss from observed past events is hardly news, but dynamically changing the price of the service to reflect individual risk is a big deal. My expectation is that next decade we will see an explosion of insurance and insurance-like products and services — leveraging those very same network effects of data, providing truly dynamic resource pricing and allocation. 

This is the purpose of my new project, HVF — to bring these new products to those that will benefit from them the most. We see data-driven understanding and pricing of risk as the great opportunity to improve lives. The reason the notion of analog is so important here is because it ultimately also means “human.”

Understanding the changing risk profile of a person can deliver to them amazing opportunities they wouldn’t have in today’s world: inferring that a particular college grad is financially responsible by looking at their tweets could allow them to buy their first house on credit, at 21, without any history, and looking at someone’s heart rate monitor data could make their cardiovascular healthcare cost-free. 

These are not non-controversial topics — privacy, unfair discrimination, built-in biases are all possible, and we must be thoughtful and diligent in how we go about bringing this future. But I believe that what we can enable with data insights greatly outweighs the downsides. 

Here is what I mean, by way of a simple example.

On a Sat morning, I load my two toddlers into their respective child seats, and my car’s in-wheel strain gauges detect the weight difference and reports that the kids are with me in a moving vehicle to my insurance via a secure message through my iPhone. The insurance company duly increases today’s premium by a few dollars. 

My keepHonest app sees this too and immediately offers me up as a customer to a few competing insurance companies in the background, but nobody is willing to charge me less right now, and the phone chirps sadly to let me know I’m now paying a higher premium. Safer, but more expensive. 

But In a few hours, my car’s GPS duly reports to my insurer that I only drove two miles to the park, never sped and, and observed all traffic signs. My phone now chirps happily: not only has my rate been discounted, several companies are offering me a deal on insurance!

So to conclude: I believe that in the next decades we will see huge number of inherently analog processes captured digitally. Opportunities to build businesses that process this data and improve lives will abound. 

A Cure for Cancer? This ‘Big Data’ Startup Says It Can Deliver

Christina Farr, The Washington Post, January 17

‘Big data’ is one of the most over-used buzzwords in the startup vernacular, and founders rarely have the goods to back it up. So you’ll understand that I was intrigued — but highly skeptical — when an email with the subject line “using data to cure cancer” popped into my inbox.

But Ayasdi, a startup that closed $10 million in venture funding Wednesday, doesn’t just talk the talk. Stanford researchers have been baking the complex algorithms behind Ayasdi (its quirky name means “to seek,” in Cherokee) for over a decade, with the goal of unlocking the hidden value in human genetic data.
In 2008, the founders, Gurjeet Singh, Dr. Gunnar Carlsson, and Harlan Sexton, decided to commercialize the technology.

With the government stepping up its funding for science, they were able to pull in $3.5 million in grants from DARPA, the department of defense agency responsible for building new technology for the military, and the National Science Foundation. The result? A synthesis of machine learning technology and topological data analysis (TDA) that has impressed a score of Silicon Valley investors.

Rather than typing in search-style queries, the tools allow for automated discovery of information. As Dr. Carlsson explained in an interview, “The idea is to answer questions that you didn’t know to ask.”

This year, the 30-person team of engineers will expand its marketing and sales efforts with funding from Khosla Ventures, Floodgate, Data Collective’s Matt Ocko, serial entrepreneur, Steve Blank, and more.

Storied investor Vinod Khosla, who rocked the medical world with the statement that 80 percent of doctors would be replaced by machines, said Ayasdi’s “machine powered intelligence” has the potential to unearth “previously unattainable insights that will help solve some of our most pressing global, social, and economic issues.”

Eric Schadt, Director of the Institute for Genomics and Multiscale Biology, has a team of researchers using the technology to identify the genetic predispositions of many diseases, including cancer, which they hope will help them “glean new insights that will lead to breakthrough drug therapies.”

Related: In the burgeoning field of genomics, entrepreneurs aim to deliver more personalized treatments for life-threatening diseases.

Ayasdi is working with the nation’s top hospitals and medical researchers to uncover more targeted treatments for disease. Singh, the company’s CEO, told me that hospitals and big pharmas are routinely pulling data from public sources — medical researchers are required to publish their data  – and combine it with private data to yield new insights.

The data isn’t anything new — it’s the technology that has evolved. “We have automated the discovery of knowledge from data,” said Singh in a phone interview. “We were able to discover a new type of breast cancer without asking questions.”

Singh was referring to a recent breakthrough where Ayasdi mapped 14 variants of breast cancer. Using data collected during a 15 year period, and studied by thousands of scientists, the algorithms discovered a sub-group of patients that have a higher chance of survival based on their genetic profile.

If a patient falls into this sub-group, it is unlikely that they will require chemotherapy.

In another recent partnership, with Mount Sinai Medical Center, Ayasdi was used to point to targeted treatment options for E. Coli sufferers. E. Coli affects more than 265,000 people in the U.S. every year, and millions around the world. It is known in the medical community for developing resistance to many drugs, and doctors are never 100 percent sure if a treatment will work or not.

Mount Sinai is using Ayasdi to analyze the entire E. Coli genome sequence, which includes more than 1 million DNA variants. This will further our understanding of why some types of E. Coli develop resistance to antibiotics and how we can combat the spread of the bacteria.

Singh, a former researcher at Stanford, told me that the company has secured 20 customers in the oil and gas, government, pharmaceutical, and healthcare sectors. Big name customers include Merck, the Food and Drug Administration, and the U.S. Department of Agriculture.

Copyright 2013, VentureBeat

Facebook's Other Big Disruption

Quentin Hardy, The New York Times, January 17, 2013

Facebook just made a potentially game-changing announcement. It got less fanfare than Tuesday’s announcement that it is going into the social search business, but this other announcement may have bigger long-term implications for the technology industry.

Put simply, some of the world’s biggest computing systems just got a little cheaper, and a lot easier to configure. As a consequence, the companies that supply the hardware to these systems may have to scramble to remain as profitable. The reason is a Facebook-led open source project.

In 2011 Facebook began the Open Compute Project, an effort among technology companies to use open-source computer hardware. Tech companies similarly shared intellectual property with Linux software, which lowered costs and spurred innovation. Facebook’s project has attracted many significant participants, including Goldman Sachs, Arista Networks, Rackspace, Hewlett-Packard and Dell.

At a user summit on Wednesday Intel, another key member of the Open Compute Project, announced it would release to the group a silicon-based optical system that enables the data and computing elements in a rack of computer servers to communicate at 100 gigabits a second. That is significantly faster than conventional wire-based methods, and uses about half the power.

More important, it means that elements of memory and processing that now must be fixed closely together can be separated within a rack, and used as needed for different kinds of tasks. There is a lot of waste in data centers today simply because, when there is an upgrade in servers, lots of other associated data-processing hardware has to be changed, too.

There were other announcements, like a computer motherboard called Grouphug that allows different manufacturers’ chips to be interchanged without altering other parts of the machine. Before, they were custom made. Put together, such innovations potentially lower the cost and complexity of running big and small data centers to an extent that works for a lot of companies.

“Who wouldn’t want a cheaper, more efficient server?” said Frank Frankovsky, vice president of hardware design at Facebook, and the chairman of Open Compute. “The problem we’re solving is much larger than Facebook’s own challenges. There is a massive amount of data in the world that people expect to have processed quickly.”

To be sure, it’s in Facebook’s interest to attack expensive hardware. The company makes money from a service that requires hundreds of thousands of computer servers distributed in big centers around the world. Google and Amazon.com, which are not members of the project, maintain proprietary systems which they apparently felt gave them a competitive edge.

For Facebook, the difference seems to be more in the software. To the extent hardware costs drop, that’s great for them. Mr. Frankovsky argued that, while “this puts challenges on the incumbents” in hardware, “it also helps them. They have a finite number of engineering resources, and this way they hear from a community about whether there is an interest for a product.” Intel may hope to benefit from its open-source release, since it could see an overall rise in demand for its chips with the move toward cheaper computing.

The real test is whether Facebook can increase the number of potential buyers for Open Compute equipment. “The question is, can they extend this beyond a few Web businesses like Facebook and Rackspace, or a few financial exercises at Goldman, and bring this to industries like oil or aerospace?” said Matt Eastwood, an analyst with IDC, a technology research firm. “That will take it from 20 or 30 companies to hundreds of companies.”

The issue isn’t so much a technical one, he argues, as it is one of getting corporate information technology professionals interested in radical design changes. Mr. Frankovsky is aware of the problem. Recently he and his colleagues led a seminar in Texas for BP, Shell and other oil giants on how they could use Open Compute hardware in their data centers.

This will not change things dramatically this year, and possibly even next, but over the long haul it could remake a lot of businesses. Linux, remember, was around for several years as a minor player, but eventually undid Sun Microsystems and others.

Open Access: Aaron Swartz's illusion over research

John Gapper, The Financial Times, January 16, 2013

Deleting private companies from the equation might allow savings but could reduce efficiency
The death of the internet activist Aaron Swartz at the age of 26 has rightly evoked tributes to his creativity and selflessness. Swartz, who faced jail for illegally downloading millions of academic papers from an electronic library, committed suicide last week.

Five years ago, Swartz signed a “guerrilla open access manifesto” in which he complained of “the world’s entire scientific and cultural heritage” being “digitised and locked up by a handful of private corporations” such as Reed Elsevier. He advised computer hackers to “take information, wherever it is stored, make our copies and share them with the world”.

In 2010, he disguised his identity and exploited the electronic network of the Massachusetts Institute of Technology to download most of the database of Jstor, a non-profit group that digitises academic journals and articles. He did not share or sell the material – he later handed it back – but prosecutors took the manifesto seriously and charged him with fraud.

Mr Swartz worked on projects from the news aggregator Reddit to the Creative Commons open copyright licence, and was widely liked and admired. But, in his analysis of academic research and publishing, he suffered from an illusion.

Free access to academic research – the system Mr Swartz advocated – could bring public benefits. It would enable anyone to read, analyse and build upon privately and publicly funded research. However, someone would still need to pay for it and the costs to universities such as MIT and Oxford would rise, not fall.

Critics of the current system, under which research libraries pay up to $50,000 annually to use online databases, tend to blame profiteering by companies such as Reed Elsevier and Springer for this cost. George Monbiot, the activist and Guardian writer, describes it as “pure rentier capitalism”, arguing that people should “throw off these parasitic overlords and liberate the research that belongs to us”.

Allied to this is the belief that publishing costs have fallen heavily in the shift from print to digital. Elsevier, the scientific publishing arm of Reed Elsevier, made profits of £352m on revenue of £978m in the first half of 2012 – an operating margin of 36 per cent. Remove the capitalists and distribute research through public utilities, and surely swaths of cost would disappear?

Well, perhaps. Elsevier could certainly do with a bit more competition. Its fee structure is opaque and it publishes journals in which academics vie to be published. It has what Warren Buffett calls a moat – it is a 130-year-old business with 20 per cent of the market that is hard to attack.

It did not, however, steal this advantage. It acquired it from the 1960s and 1970s onwards as research universities saved money by outsourcing their costly and subscale publishing presses. Elsevier employs 7,000 editors, manages a network of some 500,000 peer reviewers (whom it does not pay), publishes 300,000 new articles a year and runs a 100-terabyte database.

Printing is only a small part of the cost of academic publishing. The bulk lies in the labour-intensive business of editing and reviewing submissions (rejecting two-thirds of them) and managing data. These costs are similar for open access publishers such as the Public Library of Science (Plos) in San Francisco, a competitor to Elsevier.

An independent study by the Research Information Network in the UK found that the shift from print to digital may save £1bn globally – worth having but only 12 per cent of total costs. Removing private companies from the equation might allow further savings, but it might equally reduce efficiency.

In any case, there will still be a hefty bill. About 90 per cent of the industry operates on subscription – the model Swartz so hated. The other 10 per cent is now open access, under which researchers (or research funders) have to pay journals between $1,000 and $5,000 an article to cover publishing costs. Anyone can then read it free.

Open access is appealing and is supported both by research funds, such as the US National Institutes of Health and the UK Wellcome Trust, and by the UK government. The trust believes it makes no sense to invest £700m each year on research without paying an extra £10m to make it widely available.

Research is largely read by other academics at the moment, most of whom have access through libraries. But there could be big benefits to broadening reach – Plos One, the science journal, is a trove of fascinating material.

That said, open access mostly transfers the bill. The Research Information Network estimated that, if the market moves to 90 per cent open access, total costs would fall by £560m but universities would pay more. The UK would save £128m in library subscriptions but contribute £213m in fees because its universities publish a lot of research.

Open access also has its pitfalls. In the 1970s the credit rating industry turned from investors subscribing to ratings to bond issuers paying. That established open access but also gave agencies a motive to please issuers with good ratings, culminating in the triple A rating of flimsy mortgage-backed securities.

Open access journals have a similar incentive to widen access and dilute quality. It is worth noting that Plos One publishes 24,000 pieces of research every year – it accepts any submission that meets the hurdle of “valid science” – while the most prestigious journals (including other Plos titles) publish 200.

If Swartz’s sad death shifts the balance further toward open access, that will be a worthy legacy. But someone will always pay.

john.gapper@ft.com

Vivek Kundra: Release Data, Even If It's Imperfect

Joel Schectman, The Wall Street Journal, January 15, 2013

Vivek Kundra, former CIO of the federal government, says organizations will be more innovative if their data is rapidly released and shared, even if it’s imperfect.

Real-time data sharing promotes competitiveness and accountability, Kundra told CIO Journal Editor Michael Hickins at the Wall Street Journal CIO Network conference in San Diego. Soon after joining the federal government in 2009, Kundra created a Web-based tool that allows the public to track the progress of federal IT projects. “The default is that people will go after things that may not be 100% accurate,” Kundra said. “My view is it’s much, much better for the government to put out data that’s not 100% accurate, then to hold it in a secretive and opaque way.”

Kundra, now an emerging markets chief at Salesforce.com , said the release of government data is helping the private sector create a new wave of innovative apps, like applications that will help patients choose better hospitals. Those apps are built atop anonymized Medicare information. Kundra says he had a simple litmus test for assessing the risk of releasing data, which may include imperfections. “Assessing the risk, I asked a very simple question: were we using this data to make public policy decisions or investments. And inevitably, the answer would be yes,” Kundra said. “Then the question was, we’re using it, but why can’t let the American people see it? My view was let’s put it out there because we have to be able to trust that there’s somebody much smarter than us…it becomes a feedback loop that makes the data quality improve, and it makes the processes improve internally.”

The Dunbar Number, From the Guru of Social Networks

Drake Bennett, Bloomberg Business Week, January 2013


A little more than 10 years ago, the evolutionary psychologist Robin Dunbar began a study of the Christmas-card-sending habits of the English. This was in the days before online social networks made friends and "likes" as countable as miles on an odometer, and Dunbar wanted a proxy for meaningful social connection. He was curious to see not only how many people a person knew, but also how many people he or she cared about. The best way to find those connections, he decided, was to follow holiday cards. After all, sending them is an investment: You either have to know the address or get it; you have to buy the card or have it made from exactly the right collage of adorable family photos; you have to write something, buy a stamp, and put the envelope in the mail. These are not huge costs, but most people won't incur them for just anybody.

Working with the anthropologist Russell Hill, Dunbar pieced together the average English household's network of yuletide cheer. The researchers were able to report, for example, that about a quarter of cards went to relatives, nearly two-thirds to friends, and 8 percent to colleagues. The primary finding of the study, however, was a single number: the total population of the households each set of cards went out to. That number was 153.5, or roughly 150.

This was exactly the number that Dunbar expected. Over the past two decades, he and other like-minded researchers have discovered groupings of 150 nearly everywhere they looked. Anthropologists studying the world's remaining hunter-gatherer societies have found that clans tend to have 150 members. Throughout Western military history, the size of the company—the smallest autonomous military unit—has hovered around 150. The self-governing communes of the Hutterites, an Anabaptist sect similar to the Amish and the Mennonites, always split when they grow larger than 150. So do the offices of W.L. Gore & Associates, the materials firm famous for innovative products such as Gore-Tex and for its radically nonhierarchical management structure. When a branch exceeds 150 employees, the company breaks it in two and builds a new office.

For Dunbar, there's a simple explanation for this: In the same way that human beings can't breathe underwater or run the 100-meter dash in 2.5 seconds or see microwaves with the naked eye, most cannot maintain many more than 150 meaningful relationships. Cognitively, we're just not built for it. As with any human trait, there are outliers in either direction—shut-ins on the one hand, Bill Clinton on the other. But in general, once a group grows larger than 150, its members begin to lose their sense of connection. We live on an increasingly urban, crowded planet, but we have Stone Age social capabilities. "The figure of 150 seems to represent the maximum number of individuals with whom we can have a genuinely social relationship, the kind of relationship that goes with knowing who they are and how they relate to us," Dunbar has written. "Putting it another way, it's the number of people you would not feel embarrassed about joining uninvited for a drink if you happened to bump into them in a bar."

While Dunbar has long been an influential scholar, today he is enjoying newfound popularity with a particular crowd: the Silicon Valley programmers who build online social networks. At Facebook (FB) and at startups such as Asana and Path, Dunbar's ideas are regularly invoked in the attempt to replicate and enhance the social dynamics of the face-to-face world. Software engineers and designers are basing their thinking on what has come to be called Dunbar's Number. Path, a mobile photo-sharing and messaging service founded in 2010, is built explicitly on the theory—it limits its users to 150 friends.

"What Dunbar's research represents is that no matter how the march of technology goes on, fundamentally we're all human, and being human has limits," says Dave Morin, one of Path's co-founders. To developers such as Morin, Dunbar's insistence that the human capacity for connection has boundaries is a challenge to the ethos of Facebook, where one can stockpile friends by the thousands. Dunbar's work has helped to crystallize a debate among social media architects over whether even the most cleverly designed technologies can expand the dimensions of a person's social world. As he puts it, "The question is, 'Does digital technology in general allow you to retain the old friends as well as the new ones and therefore increase the size of your social circle?' The answer seems to be a resounding no, at least for the moment."

At 65, Dunbar is thickening slightly, with a scholar's slouch, although he tends to take stairs two at a time. A professor at the University of Oxford, he lunches regularly in the senior common room of Magdalen College, where he's a fellow. The cozy space, with oil portraits of long-dead scholars in robes and wigs, looks out on a baize-like lawn. In November, over thin, gray lamb chops, he told a bit of his story. He grew up in Tanzania, where his father was an electrical engineer, and as a teenager he'd dive and sail off the coast and drive into the bush to shoot elephants. When he was at graduate school in the early 1970s, his original research interest was not human friendship but the social life of the gelada, a monkey found only in the Ethiopian highlands and closely related to the baboon.

Dunbar has a quick, ironic smile and speaks sleepily, in long, fluent dilations. What attracted him to the gelada, he says, were "the peculiarities of their social system, which is based around small family groups which come together into large herds. It's kind of vaguely similar to what you see in modern hunter-gatherers. It's called a fission-fusion social system, and it only occurs in two monkeys out of all the 300-odd primates—aside from humans."

It was the monkeys' grooming habits that really interested him. For geladas, as for many other primates, grooming is only partly about cleanliness. It's also a form of bonding. Gelada life is rife with intrigue—there are cabals and coups and uneasy alliances—and the monkeys cement friendships by picking through each other's fur for parasites and kneading the skin beneath. In an early paper, Dunbar showed that the amount of time geladas spend grooming is not a function of body size, which would suggest a solely hygienic purpose, as bigger bodies take longer to pick over. Instead, it's a function of group size. The bigger the troop, the more time its members spend trying to curry favor with each other through massage. Dunbar began to wonder what other characteristics might correlate with group size.

In 1992, Dunbar published his answer: brain size. Scientists have long been intrigued by the question of why primates have such big brains. It's nice to be smart, of course, but big brains demand an enormous amount of energy and require years to grow to full size, and the larger skulls that protect them make childbirth much more dangerous. Plenty of species have thrived on this planet without much of a brain at all.

Dunbar's argument, laid out in the Journal of Human Evolution, was that big brains evolved to solve the problem of social life. Living in large groups confers significant advantages, chief among them better protection against predators. But living together is also difficult. Members compete for food and access to mates. They have to guard against bullies and cheats—and pick their own spots to bully or cheat. "For very social species, and this applies particularly to primates, the group is an adaptation to solve particular ecological problems," Dunbar explains. "But the group itself triggers a whole series of problems at the individual level. It's essentially the social contract problem: People tread on your toes; they steal your food just as you've unearthed it."

As group size grows, a dizzying amount of data must be processed. A group of five has a total of 10 bilateral relationships between its members; a group of 20 has 190; a group of 50 has 1,225. Such a social life requires a big neocortex, the layers of neurons on the surface of the brain, where conscious thought takes place. In his 1992 paper, Dunbar plotted the size of the neocortex of each type of primate against the size of the group it lived in: The bigger the neocortex, the larger the group a primate could handle. At the same time, even the smartest primate—us—doesn't have the processing power to live in an infinitely large group. To come up with a predicted human group size, Dunbar plugged our neocortex ratio into his graph and got 147.8.

Dunbar was not the first to suggest that social dynamics explained the evolution of higher intelligence, but the simple arithmetic of his argument—bigger brains equal bigger groups—gave it resonance, and he's now seen as the father of what's known as the social brain hypothesis. "It's been very influential," says Simon Reader, an evolutionary biologist at McGill University. "It has been the dominant hypothesis."

The Dunbar Number has made its namesake an intellectual celebrity. Much of his recent writing has been for popular audiences. For a while he contributed regularly to the New Scientist magazine and the Scotsman newspaper. He has spoken at TED and written books for lay readers; the most recent of them, The Science of Love, was published in the U.S. in November. Although he's an engaging writer, his more recent books give the impression of having been written quickly. In The Science of Love there's an amusing page-long description of the erotic effects of the steroid androstadienone. That description also appears, almost word for word, in his previous book. Asked about this, he says, "You tend to slip into these sorts of standard formulations, I think. I don't think there's anything that's directly cut and pasted."

In person Dunbar retains a certain remove, not exactly aloof and not exactly shy. Sitting and speaking in his cinder-block-walled office at Oxford's department of experimental psychology, he twists metronomically in his swivel chair, leaning back and running his eyes over the spines of the books on his bookshelves. He gives the impression of someone not actively looking to increase his number of bilateral relationships. Asked whether as a scholar of social behavior he thinks of himself as a particularly social person, he says, "I guess I'm sort of about average. I'm certainly not hypersocial, that's for sure." Over the course of one afternoon, he is interrupted twice by phone calls. The first is a major book festival asking him to be a guest speaker. The second is BBC News asking him to come on that evening. He says no to both, the first one with a trace of annoyance—he'd already declined by e-mail, he explains later.

His professional network spans an array of disciplines. He's collaborating on projects with linguists, computer scientists, physicists, classicists, economists, archeologists, anthropologists, and literary scholars. All the projects are related to the social brain hypothesis. One study looks at laughter, its physiological effects, and the role it might play in cementing social bonds. Another considers, in a similar way, dancing. His collaborators universally praise him. "For me, Robin is the sort of person you can't help liking within about five minutes of meeting him," says Felix Reed-Tsochas, a theoretical physicist at Oxford who's collaborated with him. "He's full of really, really interesting ideas and insights, which just kind of gives you a buzz."

In the fall of 2010, Dunbar got a phone call from Morin, who had been the executive in charge of Facebook's app platform and co-invented Facebook's Connect feature. Earlier that year he'd left the company to help found Path. He had discovered Dunbar's work years earlier as a freshman economics major at the University of Colorado.

Morin, now 32, grew up in Helena, Mont., a town of 28,000 people, and he talks about small-town life in the key of John Mellencamp. "America was built on the backs of these small communities," he says, sitting in a conference room at Path's offices in a downtown San Francisco skyscraper with a view of the Bay. Although Morin has spent his adult life in cities, he's used online networks to create communities with the closeness of his hometown.

Path, he says, provides a way for anybody to be able to do that. The service allows people to post photos from their smartphones. Users can message each other and comment on and search through the material others have posted. One of its more intimate features allows someone to tell everyone in his network when he's going to sleep and when he's woken up. But that network cannot be larger than 150 people. Path, in essence, is for clans.

"People feel like they can put things on Path they can't put anywhere else," Morin says. "Fundamentally, once you go beyond this number of people you can keep in your head, you begin to filter yourself, you change what you share and how much, you put on your public face." The service recently passed 5 million users, and Morin says keeping its network size small has rewarded the company with a remarkably engaged user base.
Morin and Dunbar's first conversation lasted a couple of hours. Among other things, they talked about Dunbar's research on how long the average friendship can survive in the absence of face-to-face contact (6 to 12 months), and about how, according to Dunbar, a woman can have two best friends (including her romantic partner), but a man only one. Since then the two have spoken every few months. The search algorithm Path uses to find a user's closest friends is based on Dunbar's work. Morin says the service is launching several features this year that grow out of the psychologist's ideas, although he declines to describe them.

Morin likes to point out that it's misleading to talk about a single Dunbar Number. Dunbar actually describes a scale of numbers, delimiting ever-widening circles of connection. The innermost is a group of three to five, our very closest friends. Then there is a circle of 12 to 15, those whose death would be devastating to us. (This is also, Dunbar points out, the size of a jury.) Then comes 50, "the typical overnight camp size among traditional hunter-gatherers like the Australian Aboriginals or the San Bushmen of southern Africa," Dunbar writes in his book How Many Friends Does One Person Need? Beyond 150 there are further rings: Fifteen hundred, for example, is the average tribe size in hunter-gatherer societies, the number of people who speak the same language or dialect. These numbers, which Dunbar has teased out of surveys and ethnographies, grow by a factor of roughly three. Why, he isn't sure.

The venture capitalist Jerry Murdock is one of Path's investors; his firm, Insight Venture Partners, also invested in Twitter and Tumblr. Murdock, who has a numerological streak, sees Dunbar's Number as a sort of social Fibonacci sequence, a simple mathematical relationship revealing a deeper truth about the workings of the universe. He believes the two sets of numbers may be related. "What Dunbar's theory does, like all good theories, is it explains constraints, constraints in nature," he says. "And it's the constraints that make great architecture. It's the constraints that make great companies."

Just as simplicity has popularized Dunbar's ideas, it has opened him up to the charge of reductionism. "We want to apply this single monolithic idea that reduces all the complexity of the world to just one dimension and just one number," says Duncan Watts, a network theorist and research scientist at Microsoft (MSFT). As he sees it, Dunbar's model of friendship, as a series of circles of intimacy, is a massive oversimplification: In real life, people don't have better friends and worse friends, they have different sorts of friends they go tor different things. "If you're saying there's only 150 people who matter, my response is, 'Matter to what?' " he says. "Depending on what you're trying to do, the people who matter may be your co-workers, they may be your old high school friends, they may be your current social circle, they may be your family. The challenge for social networking sites is to solve that problem."

Others, anthropologists and brain scientists in particular, challenge the evolutionary story Dunbar tells, arguing that it discounts other factors that might have driven the development of the big human brain—the pressure to figure out more efficient ways to forage, or the need to surmount the defense mechanisms of the plants and animals our ancestors wanted to eat. "Ecological pressures like avoiding predators, finding food and shelter, choosing habitats—all these kinds of decisions. I think they played a role" in brain growth, says Reader, the biologist.

Researchers who've used different methods to measure the size of a person's social circle have come up with numbers that don't match Dunbar's. One set of studies by the anthropologist Russell Bernard and the network scientist Peter Killworth found a mean social network size of 291. Another paper, published this month in the Journal of the American Statistical Association, came up with 611.

Among social network architects, there are those who see the Dunbar Number less as a wall and more as a hurdle. When Morin was at Facebook, he used to discuss behavioral science with Dustin Moskovitz, one of its co-founders. In 2008, Moskovitz, along with the programmer Justin Rosenstein, left Facebook to found Asana, a company that offers task-management software meant to improve how work teams collaborate. Whereas Path fits itself to the contours of the social limits Dunbar describes, Asana seeks to explode them.
To Moskovitz and Rosenstein, a tool such as Asana—or Facebook, for that matter—is like a telescope. It's a technology that extends the range of our abilities. "It gives us more capacity for keeping track of these relationships, for annotating them, knowing what people are doing, developing an understanding of their strengths and weaknesses, without necessarily having a bunch of one-on-one conversations," says Moskovitz. Rosenstein adds: "Certainly that's one of our semisecret sub-missions: to increase Dunbar's Number."

At Facebook itself, Dunbar still comes up often. "We do talk about it. In a lot of contexts it's a compelling framing of some of the data that we have about people's relationships," says Cameron Marlow, a sociologist and the head of the company's data science team.

Dunbar is familiar with the critiques of his work, and he has responses to them. He agrees with Watts, for example, that people have different social networks for different purposes, but that doesn't mean there isn't some basic emotional bond we reserve for some people, independent of their utility to us: "Someone like your boss, or the person you borrow $50 from to pay the drug dealer, these people are meaningful in your life, but they're not meaningful to you as relationships." He also continues to find his number popping up all around him. A paper published in 2011 found that on Twitter the average number of other people a user regularly interacts with falls between 100 and 200. And though the limit on how many Facebook friends one can have is a generous 5,000, the average user has 190—more than 150, but within what Dunbar sees as the margin of error.

Dunbar himself has zero Facebook friends. He occasionally peers over his wife's shoulder when she logs on at home, but he isn't on the social network. He has a LinkedIn (LNKD) account, he says, "by mistake." He opened a Path account but never uses it.

Dunbar does not rule out the possibility that human beings might be able to reset the cognitive limits on our social lives—we've done it before. The reason we're able to function in so much larger groupings than our primate cousins, Dunbar argues, is because, tens of thousands of years ago, we taught ourselves to talk. Whereas baboons bond by taking turns picking each others' nits, we have rhetoric and gossip and half-time speeches, not to mention singing and storytelling and jokes, to bring and hold us together. Language, he says, is how humans used their big brains to get to 150. And until something as revolutionary as that comes along, 150 is where he thinks we'll stay.