The NSA’s collection program in the Bahamas—code-named SOMALGET—was a small part of a larger operation, which itself is just a tiny fraction of the NSA’s global surveillance system. Whistleblowers and leaked documents have revealed that the NSA has been gathering and storing emails, phone calls, and other records on a global scale, and, apparently to capture entire nations’ communications outputs and store them for later study. And the NSA isn’t the only entity with such ambition. Other agencies and companies around the world have been collecting and creating data sets that capture one entire facet of the behavior of millions or even billions of people. The city of New York can now analyze each taxi ride taken in the five boroughs over the past several years. Google has stored every single character that anyone has entered into its search engine for more than a decade. It’s all in there, taking up much less room in memory than you might think.
A medical researcher can now download and analyze all the drug prescriptions filled in the United Kingdom; an epidemiologist can view all the deaths recorded in the United States; a civil engineer can view all airline flights taken anywhere in the world at any time in recent history. Personal genomics companies are now performing cut-rate genomic analysis of more than a million customers; at this point, it’s just cost and inclination that keeps us from capturing the genome of every individual on Earth. And as digital cameras, microphones, and other sensors are woven into every aspect of the fabric of our society, we aren’t far from being able to capture the movements and utterances of every single macroscopic creature in the places we inhabit.
Pretty much anything that can be digitized or digitally collected and numbers in the billions or trillions or quadrillions can now be archived and analyzed. All our communications, our purchases, our travels, and our daily routines are to at least some degree sitting on banks of computer memory. We no longer have to guess, to sample, to model; it’s all there for the taking. As this data begins to shine light into every corner of our society, we will recognize how much of our existence has been in darkness—and how different life will be in a world without shade.
Big Data and Better Government
Margaret Levi
Jere L. Bacharach Professor of International Studies, University of Washington; director, CHAOS (Comparative and Historical Analysis of Organizations and States) Center; co-author (with John S. Ahlquist), In the Interest of Others
Big Data gives business, government, and social scientists access to information never available before. With the right tools of analysis—which are improving exponentially as I write—Big Data will transform the way we understand the world and the means we use to fix problems. The U.S. and other governments are building the capacity to use Big Data as a basis for determining best practices; university-based research programs are generating appropriate analytic tools; and various nonprofits around the world are linking technology, data, and citizens to enhance the implementation of government programs and services.
Science can now effectively be brought to bear on public policymaking. Yet, important distinctions exist among the key players. One set of actors wants to ensure that public policies are evidence-based and a second set aims to enable citizens to complain about poor services and to get the services they need. Some are fundamentally concerned with the science and others with voice.
Evidence-based policy has become a mantra in some circles, and increasingly the focus is on assessment of policies once enacted as well as on the ex-ante crafting of good policy. Randomized experiments have gained popularity worldwide by bringing scientific rigor into the appraisal of interventions meant to improve well-being. But they are not the only tools in the toolbox. Observational analyses using Big Data are just as important, particularly where randomization of people and communities is undesirable, infeasible, unethical, inadequate, or all of the above. Political considerations often trump randomization when it comes to the location of hospital facilities, military bases, and schools. Even in politicized circumstances, new techniques of causal inference from observational data make it possible to learn about the conditions under which different policies are likely to succeed. Indeed, the progress in recent years on generating scientific inferences from observational data has been breathtaking.
Simultaneously, another group of actors are stepping up to the plate to adapt and improve current technologies, data platforms, and analytic advances in the service of citizen voice. Providing individuals with mobile phones to take pictures, send texts and emails, and otherwise document what they see offers citizens a means for reporting on where things are broken and demanding that they be fixed. It is also a new and important form of quality control over elections, services, and bureaucrats. Reporting leaking gas mains or water hydrants, photographing potholes and abandoned homes, and naming corrupt officials can lead to significantly improved government responsiveness—and in some places already has, generally as a result of the work of nonprofits such as Code for America in the U.S. and eGovernments Foundation in India, or of university-based research teams collecting evidence on how government actually functions. One recent success involves discovering and correcting the gap in the distribution and use of food stamps in California.
The amount and kind of data collected from all of us does pose dangers to privacy and misuse. Science and engineering are being mobilized to ensure that the proper protections are in place, but governments must also convince publics that they are trustworthy in how they use the data they access. At stake is the promise of better government that draws on scientific analysis of policy and scientific and technological amplifiers of voice.
This Is the Science-News Essay You Want to Read
Marti Hearst
Computer scientist, UC Berkeley School of Information; author, Search User Interfaces
Scientists and engineers continue to make progress in the battle against the overload of confusing choices that plague modern society. In response to well-known studies from the 1990s and 2000s which found that when presented with too many choices, people often opt to choose nothing at all, efforts in both the research and commercial worlds focused on mining behavioral “Big Data.”
Now intelligent systems can predict what people want before they want it, so instead of offering a choice of navigational options on a Web site and forcing the consumer to choose among them, the smart app simply shows the two or three choices that are just right for that person. And instead of scanning the news presented by reputable news outlets, readers are shown just the right article, personalized for them, so they don’t have to think about how they’ll stay up-to-date. Just the movie or video you want to watch at this moment appears before your eyes as you settle into your chair. You don’t have to give it a second thought! And of course your voting choices are arrayed for you in your favorite color scheme.
And it doesn’t stop with reading. Your vacation planning is figured out for you now as well. In the past, before Intelligent Planning, you would never have thought your dream location was a small town in Kansas, but that is indeed your top recommendation, and so of course that’s where you and your loved ones will have the best time. This way, the people who designed the system won’t feel crowded in their vacation spots in Kauai.
So the science news is all good, except for the anti-science Huxley protesters who had contrary thoughts, but that information was not in the essay you wanted to read. This was the science-news essay you wanted to read (based on essays you’ve recently read, thoughts you’ve recently had, the el grande burrito currently in your intestinal tract, and the promotion you did not get last month at work).
And this is the science-news essay I wanted to write. (AIGenerator™)
Gentle reminder: Contrary thoughts experienced during the consumption of this essay will be reported.
Those Annoying Ads? The Harbinger of Good Things to Come
Roger Schank
Cognitive psychologist; founder, Socratic Arts and XTOL; executive director, Engines for Education; author, Educatio
n Outrage
The most important news relevant to our future lives in the world of today’s technology and is not exactly news. In fact, it’s quite annoying. We all hate it. I am referring to those ads that pop up while you’re doing something on the Internet, when you least want to see them.
The annoyance with those is news every day it seems. So here is the interesting question: Why might they be a good thing?
First let’s discuss why this nuisance happens. Ads target you because of what you’re doing on the Internet. For a while, I got ads for online nursing schools, because I had checked on an online nursing school to see what it was doing (because of my interest in online education, not nursing). If a computer can even come close to figuring out your interests, expect a targeted ad. Looking at suitcases online? You will soon receive suitcase ads. Now, this seems rather stupid and it’s usually annoying. But it does work sometimes, so it will keep happening.
We’re in the keyword stage of advertising. We are being told that this is science: IBM’s Watson is doing deep learning. Don’t be fooled. It’s all keyword search and there’s no science behind it. Directed advertising is all about keywords. Anything you type online is being tracked, by a machine that can count. No science is going on.
So what is the good news?
Having someone (or something) track you might not be such a bad thing. We like it when a map program knows where we are and we can figure out how to get where we’re going. Many people like hook-up sites that tell you who’s nearby whom you might like. But, here again, no science. There could be science. Hook-up sites might figure out whom you might like and tell you what you have in common to discuss. Will this happen? We’re not that far away from it. We’d need a computer that knew about you the way a friend does (as opposed to your Web-surfing habits).
Now let’s take this idea one step further. Suppose you were trying to fix a device in your home and that the device knew you were doing so and offered help along the way. That wouldn’t be so bad. Suppose you were cooking something and the cookbook knew what you were cooking, what ingredients and equipment you had around, and could help you cook, modifying its recipes as needed. Suppose it saw you were doing it wrong and offered help? To do that, we need a model of your goals and the things that make you happy (and maybe a little physics).
Pushing the smart-machine idea even further, we can well imagine that if you were driving somewhere with a friend, the friend might say: “Hey, isn’t that restaurant you like so much near here? Why not stop for a bite?” Is that an annoying ad or helpful advice? It depends on the situation and who said it, I suppose.
Let’s move on to something more serious. My stomach hurts. I tell this to my wife and she suggests a medicine in the cabinet that she remembers I have used before and reminds me that it helped. Now, suppose this was not my wife but a computer? Is it an ad? Does it matter? Can we do this? Yes. AI technology could easily employ models of people and their needs. (But today we’re busy with keywords.)
Imagine I am really sick. I’m afraid I’m having a heart attack. Today we could go to the ER, or more likely we search “heart attack symptoms.” Maybe we call a doctor we know (assuming we know one who will answer right away). But in the future, the best and brightest cardiologists will be a click away, ready to answer your questions, offer suggestions, and maybe tell a few stories they’re reminded of by situations like yours. Is this possible? Indeed it is. It requires indexing stories the way people do to get reminded. We have programs that do this already. But, sad to say, this is not on the agenda of commercial entities in AI just yet.
Very soon, AI programs will be good enough, not because they analyze keywords or do “deep learning” but because they can model situations and match them to what people have said about those situations. Imagine a video database of hundreds of thousands of experts. Well, “How would I search through all those stories?” is the natural question. We ask that question because searching is an everyday activity now, and it has taught us to believe in search, and everyone selling AI espouses the usefulness of keywords.
But it is not keywords that will cause this breakthrough. There’s too much information to search through, and often what we need isn’t there in the first place. But this isn’t a search problem, it’s a problem not unlike the getting-the-right-ad-to-the-right-person-at-the-right-time problem. It is a question of getting computers to have a model of what you’re doing, what your goals are, and matching that to what help they might have to offer.
So instead of seeing those ads as the obnoxious things they now are, think of them as the forerunner of something exciting. Think of them as the equivalent of your friend who is wise and ready to help at any time—only right now you have a very dumb and very annoying friend. Soon you will have smarter friends, lots more of them, and machines that can pick the best advice from that being proffered. And, of course, they don’t have to actually be your friends. They can be the best and the brightest, pre-recorded and found with no effort, just in time. We understand enough of the science to do this now. Maybe soon we’ll get tired of ads and start working on important things in AI.
Biology Versus Choice
Thalia Wheatley
Associate professor of psychological and brain sciences, Dartmouth College
In neuroscience, few single discoveries have the ability to stay news for long. However, in the aggregate, all lead to the emergence of perhaps the greatest developing news story: the widespread understanding that human thought and behavior are the products of biological processes. There is no ghost in the machine. In the public sphere, this understanding is dawning.
Consider the recent sea change in public opinion on homosexuality—the growing consensus that sexual orientation is not a choice. This transformation suggests that the scale is tipping from ancient intuition to an appreciation of biology with its inherent constraints and promises.
Every year, neuroscience reveals the anatomical and functional brain differences associated with expressing a given trait or tendency, whether psychopathy, altruism, extroversion, or conscientiousness. Researchers electrically stimulating one brain area cause a patient to experience a strong surge of motivation. Zapping a different area causes another to become less self-aware. Disease can disorient a patient’s moral compass or create illusions of agency. Environmental influences, from what we eat to whom we see, provide inputs that interact with and shape our neural activity—the activity that instantiates all our thoughts, feelings, and actions. Finding by finding, the ghost in the machine is being unmasked as a native biological system.
But it is one thing to convince people that sexual orientation is not a choice. It is quite another to convince people that the whole dichotomy of biology versus choice makes no sense. Who besides the unmasked biological system is doing the “choosing”? Choosing whether to take medication is as much a biological phenomenon as the disease to be medicated. Choice is simply a fanciful shorthand for biological processes we do not yet apprehend. When we have communicated that—when references to choice occupy the same rhetorical space as the four humors—we will be poised to realize public policy in harmony with a scientific understanding of the mind.
How to Be Bad Together
Gloria Origgi
Philosopher, CNRS, Paris; author, Qu’est-ce que la confiance?
Completely unexpected—and hence interesting—was my reaction to the scientific news in Simon Gächter and Benedikt Herrmann’s compelling paper “Reciprocity, culture and human cooperation: previous insights and a new cross-cultural experiment,” in the Philosophical Transactions of the Royal Society.
The authors addressed a classic question in social science—the tragedy of the commons, or the conflict between individual interest and collective interest in dealing with common resources. This is a well-established conundrum in contemporary behavioral economics and evolutionary sociobiology, usually solved by (now) classic experimental results about cooperation, trust, and altruistic punishment. A vast literat
ure demonstrates that direct and indirect reciprocity are important tools for ensuring human cooperation. People use “altruistic punishment” to sustain cooperation—that is, they are willing to pay without receiving anything back in order to sanction those who don’t cooperate and hence promote prosocial behavior. Yet Gächter and Herrmann showed in their surprising paper that in some cultures when people were tested in cooperative games (such as the “public good game”), those who cooperated were punished, not the free-riders.
In some societies, people prefer to act antisocially, and they take actions to make sure others do the same! This means that cooperation in societies is not always for the good: You can find cartels of antisocial people who don’t care at all for the common good and prefer to cooperate in keeping a status quo that suits them, even if the collective outcome is mediocre.
As an Italian with first-hand experience of living in a country where, if you behave well, you are socially and legally sanctioned, this news was exciting, even inspiring. Perhaps cooperation is not an inherent virtue of the human species. Perhaps, in many circumstances, we prefer to side with those who share our selfishness and weaknesses and to avoid prosocial, altruistic individuals. Perhaps it’s not abnormal to live outside a circle of empathy. Perhaps cooperation for the collective worse is as widespread as cooperation for a better society!
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