double take

Last night I read this utterly depressing article about organ transplant. I used to be a huge fan of organ transplant, I’ve opted in of course and have a little dot on my driver’s license. It just seems so … obvious. Until you read that article. The big takeaway for me in that piece is how much uncertainty there is in life—very literally. That we even have the notion of a “beating heart cadaver” illustrates just how uncertain the whole venture really is.

But there is also an interesting comparison here to the Turing test, which of course tests for “intelligence” in a machine (or more accurately, a machine’s ability to imitate human intelligence). Apparently in 1968, thirteen men at Harvard Medical School decided the criteria against which human life or death can be measured. These criteria are: unreceptivity and unresponsivity, lack of spontaneous breathing or movement, lack of reflexes, and a flat EEG. An evaluation of these criteria, by another human, is in a sense a test for human intelligence. If an individual fails to demonstrate intelligence against these measures, but the beating heart still indicates life, he is deemed brain-dead (a living human, but on partially so) and can be evaluated as a potential organ donor without the same restrictions put on a living organ donor.

So I found it particularly interesting to read Nicklas’ thoughts on Cleverbot today. Apparently Cleverbot is partially human, which Nicklas observes is an odd conclusion for the Turing test to arrive at. Not only is he right that examples throughout human history show that we often think of “other” as some partial form of our own humanity, that in dehumanizing the “other” we calm our fear somewhat, but we also think of people as partially human in the context of organ donation. In the case of organ donations, though, we create this mental construct of “partial humanity” to theoretically achieve a higher end—presumably, saving other lives that will be more fully lived than one that is only “partially” lived.

All of this of course rests against the backdrop of a society that is embracing robots as our own. One of the more interesting books I read last winter was Alone Together in which author Sherry Turkle explores the ways in which humans substitute robots to fulfill needs that are otherwise not being met by human companions. The most fascinating example of human-robot connection (which I do not think was in Turkle’s book, but something I think I heard from Ryan Calo) is the soldier who dove in front of gunfire to protect his robot weapon.

We are unquestionably capable of emotional connection to non-humans—the family pet being the most obvious such example. Researchers are demonstrating that we are also to a degree capable of connecting to robots, some of which may be deemed “partially human.” At some point in the future, the question of robot rights will become a subject of public discourse, and I imagine at that point we will revisit this discussion of whether the Turing test adequately measures “humanity” for the purposes of conferring certain individual rights. Perhaps there will even be a similar set of “donation” criteria created for robot-part donations.

It interests me how the Turing test compares against our own criteria for determining brain-death. On the surface, the Turing test seems a higher standard to apply—and (as a human, in 2012) that seems appropriate. I wonder though if we will in my lifetime be having conversations about whether that’s a double standard, or whether the beloved robot companion deserves equal rights to a brain-dead patient before he is harvested for parts.

Last month I read a great book, The Power of Habit, which explores the neurological science behind habit formation. There are a lot of interesting tidbits in the book—the most frequently cited (and somehow least interesting) one I’ve seen is around whether Target can predict you’re pregnant. The book tends to have a bit more focus on marketing than I’d like, but the angle of how marketing can be used to drive habit formation does offer some insight. The author shares an example of marketers creating a habit for millions of people to brush their teeth every day by connecting a behavior (brushing one’s teeth) to a rewarding feeling (smooth teeth, clean of film). Brush teeth, get reward—no more film!

But the most interesting insight in the book was not the need to have a reward for habitual behavior, but the insight that habit formation requires inserting a new behavior into an existing routine. The author uses Febreze as an example of how (again, marketers) made this connection—the P&G marketing team was able to turn Febreze into a success once they connected a cue behavior, in this case making the bed, with the new habitual behavior, here spraying Febreze onto the sheets. In other words, they injected the new habit into an existing routine.

I may find the routine aspect of this so interesting for personal reasons—creating a routine in my own life proves to be an ongoing challenge, and this may be the elusive input to my creating new habits. But is also suggests something interesting to me about the limitations of technology to help.

This weekend I signed up for HealthMonth, a neat little tool out of the folks up at Habit Labs in Seattle. I’ve been curious about Habit Labs for a while, and figured it was time to try out one of their tools. The secret to HealthMonth seems to be gamification of challenging goals—winning the game involves sticking to new behaviors, at which point you give yourself a reward. Along the way, you get little rewards as you record your progress—more points for performing against your challenges, social feedback, etc. All this is well and good, but what it is missing (at least for me) is insertion into an existing routine. I can play the game all I want, but until I find a way to make 30min of daily exercise part of a daily routine, I’m going to have a hard time making it into a true habit.

Which leads me to wonder where technology could really help me create new habits. Perhaps I need technology to help me understand my routine better, so I can identify opportunities to inject new behaviors. For example, maybe if I monitored my detailed location history for a week I’d see that every morning at about 11am or so I wander into the microkitchen at work and grab a snack (maybe I do!?), and instead I could choose a different behavior to insert at that time. The book describes an example like this, but the individual only identifies the routine through careful manual monitoring—something that almost requires a habit of its own! But I wonder if this is a limitation, where technology can only do so much to help change our behavior, or if it’s just an opportunity that remains open for grabs.

A student in Peter Thiel’s class on start-ups blogged notes of one of the lectures, which led to a NYT article, which led to a blog post by a colleague. The general argument being put forth is that intense competition leads to competition-for-the-sake-of-competition, instead of leading to innovation.

There are many things mixed up in this piece, among them the poorly thought-through notion that war and sports are the same type of competition. First, I need to comment on why this is just not-quite-right. Playing games is an infinite endeavor. If you lose, you try again, the competition changes, you hope to win sometime. If you can’t win, you can try to change the rules of the game. You may not succeed in doing so, the rules may be set in stone. But in that case, you can find a new game to play. And if there isn’t another game you like, you can always create your own. Consider American Football, or the more recent invention of Frisbee Golf.  Or how could I forget, Quidditch.

War, however, is not the same. War involves ultimate termination, permanent occupation, subjugation. In historical terms, it often involves the outright exploitation of the weak and disempowered (in real war, women get raped, children murdered). War is not, generally speaking, an infinite endeavor. Nor is it one that enables restarts, do-overs, learning development or growth, and certainly not creation of any kind. War is destructive—and it destroys absolutely.

The other thing Thiel mixes up in all this is that he sort of suggests that competition does not drive innovation when he says maybe “competition isn’t as good as we’re told it is.” I think that’s wrong, competition is every bit as good as we’re told it is. Take Thiel’s own example—one could argue that losing a competition is the thing that prompted a creative spark. Without competition, he would not have experienced loss (of not getting the clerkship), and he would not have been forced to go through a coping process, through which he wound up starting PayPal. Any seasoned athlete knows why competition in games is a great metaphor for life: “the great accomplishment is not in never failing, but in rising again after you fall.” Thiel got back up when he lost. He just decided the next time to play a different game.

There is an implicit idea in all this that somehow, by “opting out” of competition for a legal clerkship, Thiel found his creative spark and stopped competing—but this is an absolutely absurd notion. Did he not compete when he started PayPal? Of course he did. In some sense, he entered an even more competitive endeavor. And he is still competing—today in the early-stage investment space. At some point, though, Thiel will decide he is done competing. He will become, as all athletes eventually do, a spectator. That point would have occurred at some point even had he done a a clerkship, and it will occur for all of us. It is part of being human.

All of which is a long way of making just two points: (1) I think the war analogy is a bad one—for work and for life—I much prefer we think in games; and, (2) I do think competition is the thing that spurs creativity, and in many cases losing itself can be the thing that ignites a creative spark to create a whole new game.

As a small tangent, a few months ago one of my oldest friends and I went back to our high school to talk to the PTSA about success. Ironically, we both talked an awful lot more about failure. The evening largely focused on how important it is that students experience failure in high school, and the parents were nodding right along with us the entire time. We need to compete, but just as importantly we need to fail.

So, on a related note, there is one final point to make on this Thiel piece. Brooks ends his NYT column thus: “Everybody worries about American competitiveness. That may be the wrong problem. The future of the country will probably be determined by how well Americans can succeed at being monopolists.”  This strikes me as unnecessary and slightly inaccurate simplification.

If I am right that competition in games is a good thing that drives creativity and innovation, then what do we need to focus on? Here I think the Thiel example teaches us two things, which many of us intuitively know: tolerance for failure and openness to new ideas are the necessary preconditions for innovation. Without those preconditions, you can’t easily decide to stop playing the game you’re losing and create a new one.

The folks over at Edge have a really interesting read on innovation, creativity and culture by Mark Pagel. I had a couple reactions to this:

(1) Is innovation always data-driven? Mark makes a rather compelling argument that innovation is rarely the result of someone seeing a process or tool and automatically knowing how to make it better—it’s usually a combination of copying and trial-and-error. This leads me to think the argument is actually that innovation involves a process of evaluating how well something is working (e.g., measuring its success), and iterating to make it work better. I don’t know how much I buy it as an absolute—that all innovation is data-driven—but I suspect that it holds for the vast majority of cases.

(2) How many of us should or will be innovators?  Mark makes a compelling enough argument that any given individual in a society relies more on copying than he does on innovating—this is not to say that innovation isn’t important. The argument seems to be that any given innovation scales easily, and it scales more and more easily the more connected a society becomes. With the Internet, he argues, we need even fewer individuals innovating, because an idea that pops up in southeast Asia can make its way around the world in no time. He also makes a point about language—so I think the advent of online translation tools are also part of his argument. He seems to be saying that most of us are organized to be copiers or consumers of other people’s innovations.

What interests me about these two points in combination is what that means for how we organize as a society—how we distribute resources and human capital.

On the point about how many of us should be innovators, I think Mark is both right and wrong. He’s right if when we talk about innovation we mean new idea creation, or what I’ll call isolated innovation. That’s the idea of innovation that two guys in a garage can build something revolutionary and change the world. We saw a lot of this type of innovation in the past century. But I think Mark is a little bit wrong if we want to talk about the type of innovation we need to see more of in the next century, which I’d argue is not going to be isolated but rather systemic innovation.

The biggest problems we face this century are systems problems—climate and environment, public health, sustainable urban development, etc. These types of problems won’t be solved by two guys in a garage. Instead we need data-driven innovation at scale: we need lots of well-funded scientists collecting, sharing and analyzing huge data sets about complex systems.

If you agree with me so far, I think this suggests a few different things.

• We need to divorce the idea of innovation from startups. Innovation is as much about existing, large institutions as it is smaller, new ones. Instead of talking about start-ups, we should focus on R&D policy—and make sure that it is size-agnostic.
• We need to be able to collect, share and analyze data across institutions for the purposes of innovation. This means creating open data standards, especially in the public sector. Proposals like the EU Open Data strategy and work done on Data.gov are encouraging.
• Some of the data we will need to analyze is going to be personal data, so we need mechanisms to support consent in the innovation process. This is why projects like the one John Wilbanks is leading, Consent to Research, are so important.
• Analyzing the large sets of data that will drive a lot of this innovation will mean using the cloud. It’s just not cost-effective to expect everyone to run their own data centers for this type of computation. We need to reduce barriers to access these cloud services, such as restrictions on cross-border data flow. The APEC Pathfinder project is one encouraging effort to achieve this goal.

Just a few thoughts on a very big subject. 

*Update* This post was originally titled “Teach not coding but architecture,” which is still reflected in the permalink. I have however updated the title to reflect a far better one proposed by Jonathan Zittrain on Twitter.

My colleague Nicklas Lundblad has a good post this weekend on the virtues of teaching computer science. I felt so immediately jarred by a missing piece of the argument that I was compelled to sign in and blog for the first time in months (an absence I feel tremendous guilt about). His argument is spot-in, but misses a very simple yet critical addendum.

My own education in computer science of course influences my views on the matter. I often tell people, somewhat jokingly, that despite beginning my technical studies at an all-women’s college, I have an ex-boyfriend to thank for the decision. My high school sweetheart was desperately passionate about technology, and in my freshman year of college I made an attempt to bridge the physical distance between Boston and California by bridging an intellectual one instead.  I endeavored to take the introductory CS class at Wellesley in large part because it was what he was so passionate about….but I promptly fell in love with the subject myself.

CS is a liberal art. This was a time-honored debate our professors engaged us in. Many of them came from engineering schools but wound up teaching the subject at a liberal arts school, and so had strong views on the subject. As do I, now. It is absolutely a subject that teaches you how to think—not just how to build. Building is a side effect of the discipline, a very useful one, but nonetheless a side effect.  So here, I agree 100% with Nicklas when he says:

Computer science offers a new way to understand the world, to think about it as algorithms and data structures and data sets. That is extremely powerful. So should we teach kids coding instead of teaching them to cut and paste in word processing software? It does not seem to be a very hard question does it?

Where I disagree is that I don’t think coding is enough of a prerequisite. Like advanced biology or chemistry it should be included in the standard curriculum. But the core class we aren’t teaching our kids that we need to be is Internet Architecture—that should be like government or civics classes are today: a prerequisite for graduation.

After college I somehow found my way into MIT’s Technology & Policy Program. Like most other new graduate students, I spent the first few weeks of my time at MIT on the job market looking for a RA or TA position to help pay the bills. In our case, the TPP program required that we find a research advisor also, which usually dovetailed with finding an RA position. I stumbled into Dave Clark’s office one day to ask for a job, completely unaware of his stature in the Internet community. He asked if I’d read his paper about the end-to-end principle and I said no, could he tell me about it? What followed from that initial conversation was by far the most meaningful educational experience of my life.

For the next two years I would work with Dave on research topics relating to identity and net neutrality. Each week, I would sit down with him for an hour and get an individual tutorial on how the Internet worked and the history of its design. I learned early on what an IP address was, and how TCP/IP worked—amazingly, I wound up graduating college with a CS degree without understanding those concepts. I could code when I left college, and I could think about problems through this lens—it had undoubtedly changed the way I thought about the world. But despite a college degree in CS I fundamentally had not learned the basic governance principles of the Internet.

To appreciate the mechanisms through which information can be exchanged and manipulated is to appreciate the mechanisms through which people are able to organize and communicate. To appreciate the processes through which technical standards are agreed upon is not unlike appreciating the processes through which laws and regulations are agreed upon. We teach all high school students how a bill becomes a law. Why don’t we teach them how a packet becomes an email?

I went to college with a lot of women who were interested in politics and economics, who aspired to careers in public service, and whose interests lay in questions of how society organizes itself. The women I know who are now working in these fields are having and will absolutely continue to have an impact on the world. But I would dare any of them to name a field of public policy not currently impacted by or disrupted by the Internet. Understanding how to write code that builds an isolated piece of technology is like understanding how to read and write, or knowing the ins and outs of a particular subject like Biology. But understanding how the Internet works is like understanding the way society is governed. The architectural design should be taught in high school, the same way we teach about the design of the US Constitution.

This might seem like a radical recommendation, but there it is. Lessig wrote “Code is Law” which is basically the same idea, but had the unfortunate side effect of focusing everyone’s attention on coding. And I suppose that most engineers get to a place where they understand the architecture of the net by starting out writing code. But I don’t believe that being able to write code is a prerequisite to understanding the design of the architecture, and therefore in my view teaching the architecture is the mandatory prerequisite we don’t have yet. We should be equipping people to answer the simple question: how does the Internet work?

I was really struck by this article last week describing research that used publicly available Facebook profiles to predict students likely to suffer from alcohol abuse. The article suggests toward the end that there is an open question of how appropriate it is to go scanning students’ public Facebook profiles for behavior that might be suggestive of a drinking problem—this to me misses the point, and detracts from more important questions. The more interesting questions are who gets missed if your predictive algorithm is wrong, or whether you substitute this predictive algorithm in for other more tried-and-tested measures of screening (either because it’s lower cost, or perceived to be more effective in the short run).

I suspect we’ll be seeing more and more applications like this. Those in the personal healthcare arena—e.g., personal health tracking that predicts risk for disease—will seem largely uncontroversial at first, but may pose the hardest questions in terms of how to ensure fair access to insurance and preventative care. Others like this that are more focused on public healthcare and/or social behavior modifications I suspect will provoke more ire, uncertainty, fear, and concern. For example, it probably doesn’t take much imagination to think of a world in which the social graph and one’s behavior in a social network are used as predictors of an individual’s likely political tendency—or, put another way, the likelihood that one is a terrorist from the point of view of a given state.

It would be interesting to see, for a given use case, a mapping of the pre-digital algorithm to the post-digital algorithm. That is, what inputs are used to screen for alcohol abuse today, and how do those inputs change in a digital era.

At Google we use a technique called OKRs to set goals and measure progress, which has been written about a fair bit. Since I’m spending some time at Berkman this year, I set out OKRs for my efforts there as well—one of which was to document a bibliography of all the reading I do related to predictive analytics (or anything close). Closing out the first month of the year, I’ve at least made some progress against that OKR tonight. I’ll be updating the Readings page here as I make my way through books and articles on point (and hopefully catching up to all reading to date soonish).

On an unrelated topic: as I went to document key takeaways from Incognito tonight, I realized how poor I find the digital medium for note-taking in books and generally for any sort of non-fiction reading that you may want to flip back through. I’ve noted this observation elsewhere in the past, but was struck again by this drawback tonight.

I’d welcome suggestions on better ways and tools to document said bibliography, but for now I fear it’s just going to be a semi-disorganized list.

Last week I spent a bit of time learning about the Quantified Self movement—a group I’ve been following from the shadows for a couple years now, but which I’d not really engaged with closely until recently. The group is an informal collection of self-trackers: people who use measurement and/or data collection as a means to deeper self understanding and to improve the quality of their lives. It’s a pretty neat group, and you’ll learn a lot just from perusing the blog.

The quantified self trend is one which I’ve come to think is the future of healthcare—data-driven research and self-improvement. What I find most compelling about some of the QS examples are the way in which otherwise irrelevant, mundane observations about one’s life can lead to sharp insights and improvements. Which is why I find a new project from John Wilbanks particularly interesting—as he describes it:

 The idea behind CtR is simple: make it easy for people who want to share data about themselves for scientific, medical, and health research to do so. It’s not centered on intellectual property, though it does touch on it. It’s more about privacy, and in particular, about making it possible for people to get informed about what is possible with their data and how beautiful research can emerge if enough genomes, enough biosamples, and enough other kinds of data can be shared and connected.

When we think about the future of scientific, medical and health research it’s easy to stop at the types of medical data we collect today—blood pressure, genomes, drugs consumed, etc.—but the QS movement would suggest we could learn more by going farther into the mundane and unexpected sources of data.

Which brings me to Flu Trends, an indicator for the incidence of flu in a given area based on search behavior. It can sometimes feel, especially in some privacy circles I move in, like an overused example, but an important one. Flu Trends speaks to an issue at the core of the change we need to see in how we think about privacy—the idea that data must be used for the purpose for which it was collected, otherwise called purpose specification.

In a talk I gave at last year’s OECD roundtable on the economics of personal data I emphasized this point: improving the world in the 21st century is going to depend at least in part on drawing intelligence from vast amounts of loosely collected information. More often than not, surprisingly useful intelligence will come from the most unexpected sources of information—and we need a flexible enough policy environment to support the exploration of those sources.

A couple years ago while I was at home with my family for Christmas I got a call from my credit card company. Someone had tried to charge $14,000 to my account on Christmas Eve, “was that you?” they wanted to know. It was one of the first times I sat grateful that my credit card company had a fraud detection department capable of detecting unusual charges and flagging them quickly—how smart that they could analyze the data so quickly and notice unusual activity. Over this past weekend I got a call from the fraud department for the second time ever, and this one left me wondering if the fraud detection department has just stopped investing in data analytics.

I’ve been traveling for a week, something I have been doing less and less of over the past year or so. I’d flown into Boston on Tuesday night arriving 2hrs late at roughly 2am, worked from Cambridge for a couple days, then rented a car Friday to drive to a wedding in Hanover, New Hampshire. Being the amazing advanced-planner that I am (ahem, or rather, not-so-amazing) I’d taken off from Cambridge and high-tailed it through to New Hampshire without thinking twice about cash or gas. Along the way, I’d hit a toll plaza (forgot about those!) and had to apologize profusely to the toll collector while I pecked around in my coin purse to produce 96 cents, not quite the $1 he was hoping for but he kindly let me through. I realized I may have been slightly unprepared, and was now without any cash, watching a rapidly falling gas gauge, and approaching nightfall.  An hour or so later, just before 8pm and with 20miles to go, I decided the near-empty tank wasn’t going to get me all the way there and I better pull off for gas before stations closed. I pulled into an isolated gas station a few miles off the highway only to find that my credit card company had disabled my account. Lucky for me, since I had no cash on hand, I had a different card on hand—so I paid and went my merry way.

I arrived at my hotel to find a voice mail from the fraud detection department asking for a return call. After verifying my identity, they asked about a slew of what seemed like fairly obvious charges: “Did you charge $7 to United Airlines on Tuesday? $10 to the Boston T on Wednesday? $300 to Enterprise Car Rental on Friday? What about this $30 for <some random NH gas station> earlier tonight?”

Seriously guys? I bought an airplane ticket from SFO-BOS using your credit card a month earlier. I then reserved a hotel room for Friday and Saturday nights in Hanover on your credit card. Then I made a car rental reservation from Cambridge, again with your credit card. So your detection system sounded alarms because I bought a glass of wine on a flight you knew I’d purchased, bought a subway pass in a city you knew I’d arrived in, rented a car you knew I’d made a reservation for, and bought gas for the car you knew I’d rented? Was it really that hard to connect the dots? What exactly have you been investing in or innovating over the past few years, anything at all?

I spend so much of my professional life working with people who worry about privacy invasions resulting from advanced data analytics. Never do I hear people saying things like I thought Friday night: oh dear credit card company, you had the data, you should be smart enough to make my life easier by figuring out what it means. Instead, when companies do this sort of analytics well, I hear outrage over how it is that the company knew what it did, and whether what it knew was actually accurate.

When are we as a society going to get as outraged about the lost opportunities that could have been captured using data analytics as we get about the potential privacy invasions that might arise from collection and use of the underlying data sets? When will the public pressure be of the form “Why didn’t you know better?” instead of “You shouldn’t have known that?”

Last week I stumbled across an interesting summary of a recent paper in the journal Memory and Cognition: 

By setting your predictions for the future in a familiar landscape, you allow yourself to use your memories of the past to help you predict what might go wrong in the future.  If you are only able to think abstractly about the future, then you are much less likely to find specific problems that may arise.

The paper is titled “Predicting Premeditation: Future Behavior is Seen as More Intentional than Past Behavior.” I look forward to reading it in full this week.

At a high level, this led me to ask a few questions about predictions. My anecdotal observations are that in general we “freak out” a bit about predictive analytics, the idea that we might predict a future event based on recorded history. As shown in popular culture (e.g., Minority Report), we spin up all sorts of dystopian fears about what a world driven by predictive analytics might look like.

But ask a scientist who studies humans not machines, and apparently he’ll tell you that humans predict in basically the same way machines do—based on past history. When thought about in the human capacity, my hypothesis is that predictions “freak us out” a little bit less. I have one guess as to why that might be: we understand human predictions to be simply that—predictions—whereas when data and computers are involved we perceive the predictions to have more of a discrete character, and we imagine our use of those predictions to be quite different.

In fact, predictions form the basis of the majority of decisions we make every day. In my professional capacity I’m actually encouraged to document my predictions and the fact-base I’m basing them on so that later we can review our accuracy and revise our internal (human) predictive models in the future. What I find fascinating is this prediction of my own: as predictive analytics become more and more commonplace, we will find more and more reason to fear the outcome and discourage its use. Yet, we generally recognize that better data yields better answers—so the interesting question will be this: what do we trust more, subjective interpretations of history, or data-drive analyses of history?