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| DATE | 2015-01-04 |
| FROM | Ruben Safir
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| SUBJECT | Subject: [NYLXS - HANGOUT] big data
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check this out
http://www.pbs.org/wgbh/nova/next/tech/predicting-the-future/
The Inevitability of Predicting the Future
By Tim De Chant on Wed, 26 Mar 2014
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In February, while the world was watching citizens of the Ukraine topple
their government from behind barricades of flaming tires, computer
scientist Naren Ramakrishnan and his research team were intently
watching a similar situation unfold in Venezuela.
The South American nation has been a tinderbox since early February when
Leopoldo Lopez, mayor of Chacao and an opposition leader, tweeted a call
for #LaSalida on Friday, January 31. We will meet this Sunday, his tweet
read, for #TheExit. The hashtag was a thinly coded call for the ouster
of President Nicolas Maduro, Hugo Chavez’s successor. The protests,
which decry high inflation, shortages of staple goods, and the
country’s soaring homicide rate, started in Chacao and quickly
spread to the capital, Caracas. For a while, demonstrations took place
nearly every day. Since the unrest began, at least 32 people have died.
Aqui la convocatoria para este domingo en Chacaito. ¡Activemos
juntos #LaSalida! http://t.co/XC8AnMJ2md #VIDEO via -at-VoluntadPopular
— Leopoldo López (-at-leopoldolopez) February 1, 2014
For years, Ramakrishnan, a professor at Virginia Tech, and his team have
been sifting through tweets, blog posts, and news articles about Latin
America, keeping a close eye on events in ten countries, including
Venezuela. These past couple of months have been no different. But
Ramakrishnan and his colleagues haven’t been bent over newspapers or
straining their eyes scanning streams of tweets. Rather, they were
monitoring the dashboard of EMBERS, their computer program that draws on
tweets, news articles, and more to predict the future.
Lopez’s #LaSalida tweet was probably among those which EMBERS
analyzed, and the meaning of its uncoded message was almost certainly
clear to the sophisticated system. But by that point, EMBERS had already
suggested to its operators that Venezuela was ripe for civil unrest. It
had also done the same for Brazil many months earlier, accurately
predicting the June 2013 demonstrations against rising transit fares.
Venezuela protest March 22 2014
Demonstrators participate in an opposition march in Chacao, Venezuela,
on March 22, 2014.
EMBERS is the result of years’ worth of work by Ramakrishnan and his
team, which includes computer scientists, statisticians, political
scientists, social scientists, and an epidemiologist. It is the winning
entrant in the Open Source Initiative at the Intelligence Advanced
Research Projects Activity, a part of the Office of the Director of
National Intelligence. IARPA, according to its website, “invests in
high-risk, high-payoff research programs that have the potential to
provide the United States with an overwhelming intelligence advantage
over future adversaries.” The ability to accurately forecast civil
unrest, epidemics, and elections around the world could do exactly that.
Soon, EMBERS’s capabilities will expand beyond Latin America to the
Middle East. It will draw on some of the same data sources, but also add
new feeds. Its language processing routines will be adapted to new
languages, and portions of its code will be tailored to that
region’s cultures. No one is saying exactly when EMBERS and its
offspring will be used to inform decisions by intelligence agents, but
given IARPA’s role in funding it, that seems to be the plan.
From Delphi to Digital
Predicting the future is a dream as old as antiquity. People have turned
to sources as varied as the oracle at Delphi, the Bible, Nostradamus,
and the Farmer’s Almanac. Most prophecies have been just plain false
or, less damningly, coincidentally correct. But that hasn’t stopped
people from trying to guess what was coming around the bend.
Today, of course, we make forecasts all the time, and there are plenty
of times we get it right. Our lives revolve around weather forecasts,
which are startlingly accurate as many as ten days out. We try to guess
how many people will take the bus during rush hour or how many turkeys
will be sold for Thanksgiving. But when it comes to predicting most
collective human actions, we haven’t been as successful.
At least, we weren’t. Today, a wealth of data is changing the
equation. “In the past, you had traditional media, you had
newspapers,” says Dan Braha, a complexity scientist at the
University of Massachusetts, Dartmouth. “Information was delayed
from one area to another. It was very difficult to get the real-time
information about events.”
“For many hundreds of years, the ratio of people who created content
to those who consumed it was very small. Today, it has inverted.”
In the 1990s, the internet began to dismantle some of those barriers,
reducing the time it took for news to travel from, say, Caracas to New
York. Rather than get a subscription to The New York Times, all people
had to do was point their browsers to the right address. As information
flowed more freely, the amount available to any given person increased.
But even then, the web hadn’t yet changed the dynamics of content
creation and consumption. “When the web appeared, it was a total
consumption thing,” says Bernardo Huberman, director of the social
computing lab at Hewlett-Packard Laboratories. “Then Web 2.0
appeared, which essentially is the introduction of social media. Namely,
people can generate content. Wikipedia is one, Twitter is another,
Facebook, blogging, and so on. There was an explosion of generated
content from the bottom up. For many hundreds of years, the ratio of
people who created content to those who consumed it was very small.
Today, it has inverted.”
Computer scientists and statisticians began mining that data for
meaningful relationships. City managers started studying road usage to
predict traffic jams, retailers combed past purchases to entice
customers back into the store, and social media networks scoured
profiles to sell more expensive ads. The era of Big Data was born.
But simply crunching through mountains of data isn’t sufficient.
Take Google Flu Trends, which purports to predict the severity of flu
season by monitoring the number of searches for flu-related terms. Early
on, the tool performed well. But starting in August 2011, the model
overestimated the flu’s prevalence for 100 out of the next 108
weeks, according to an article recently published in the journal
Science.
Mathematical models like Google Flu Trends can help make sense of big
data, but they can also be misleading. For researchers in the era of big
data, it’s a cautionary tale. “The data is there. The question
is, what do you do with the data?” Braha says. “If you use the
wrong models, you get the wrong results.” On top of that, even
sophisticated models are limited by our ability to process natural
language using computers. Plus, as time horizons lengthen, accuracy
tends to decline.
That hasn’t slowed things down, though. If anything, the pace has
quickened. Predictive science is fueled by data, and the more that’s
available, the more it has to run with. “The more data you get, the
predictive ability of the model goes up,” Braha says. “The
availability of social media and open source data sets—this is one
of the main reasons that enabled people to develop models.”
From Movies to Mass Protests
In 2010, Huberman and his colleague Sitram Asur published a paper about
predicting box office receipts of newly released movies. Plenty of other
papers had been published on the topic, but theirs had a twist—it
relied solely on tweets. It was among the first—if not the
first—study that used social media to predict some event before it
happened. Their model proved impressively prescient, easily besting the
previous gold standard. Huberman and Asur had proved the utility of
140-character sentiments.
A year later, in April, IARPA announced the Open Source Indicators
program (OSI), which would award substantial grants to three research
groups to develop models that ingested publicly available data like
tweets, blog posts, and news articles to anticipate “significant
societal events,” such as unrest, epidemics, and economic
instability. OSI isn’t the organization’s only program—there
are dozens—but it is perhaps the most audacious.
“It’s always easy to look at things retrospectively.”
On the surface, the goals of the OSI program don’t appear much
different from what practitioners of statistics, economics, and other
disciplines have been doing for decades—that is, building models
that use past data to predict some event. The difference is, OSI wanted
researchers to predict an event that hadn’t happened yet. Previous
“prediction” algorithms had the benefit of hindsight.
Researchers had a better idea which factors precipitated an event, and
that made it easier to tune the algorithms. “It’s always easy to
look at things retrospectively,” says Ramakrishnan, the EMBERS
researcher. What makes this new breed different is that the outcomes and
their causes are unknown. The events haven’t happened yet, and that
makes it harder to tweak a model to spit out the right forecast.
To create EMBERS, dozens of scientists across a handful of disciplines
developed algorithms to scrutinize Twitter’s firehose of
information, unravel various dialects of Spanish, Portuguese, and
French, tally reservation cancellations on OpenTable, and count cars in
satellite images of hospital parking lots. None of the data sets they
use are classified, though some of them cost money to access. The team
has spent two years fine tuning the algorithms and checking their
forecasts against reports assembled by a third party.
Ukraine protestors on barricade
Protestors stand atop a barricade on Hrushevskogo Street on January 25,
2014, in Kiev, Ukraine.
By the end of February 2014, EMBERS had archived over 12 terabytes of
data, or about 3 billion messages. It currently processes about 200 to
2,000 messages per second and adds 15 gigabytes of raw data to the
archive every day. In the past, that would have required some serious
hardware to support. But thanks to cloud computing—where computing
resources are dynamically allocated and distributed across massive
server farms—the system requires just 12 virtual machines, a number
that can be easily increased without buying expensive new servers.
After EMBERS ingests the raw data, it gleans a variety of metadata,
including where a tweet originated and what locations are mentioned, the
geographic focus of news articles, the organizations being discussed,
and so on. Enriched, the data moves on to the four prediction models.
In the case of predicting civil unrest using Twitter, algorithms look
for key words or phrases that suggest a protest is in the works. When
EMBERS finds a tweet that contains a key word or phrase—like
#LaSalida—it looks for mentions of times or dates. The system then
sifts through the geographic metadata to determine where the protest
might take place.
Since it was first booted up in November 2012, EMBERS has raised over
13,000 alerts.
That’s just one stage. EMBERS also scours tweets for three or more
of over 800 specific words or phrases that serve as indicators of
unrest. “We look at words and the sentiment with which the word is
being used,” says David Mares, a professor of political science at
the University of California, San Diego and a principal investigator on
EMBERS. A tweet’s sentiment gives important context that can change
how it is interpreted, like the difference between a Venezuelan calling
for “The Exit” and someone expressing frustration over how they
can’t find the exit. The system also uses an algorithm that looks
for other meaningful words that might have been overlooked and adds them
to the list. “We’re always picking up new words,” Mares
says.
While this all this is happening, EMBERS is tracking how tweets flow
through the network—how many people are tweeting about protests, who
is retweeting them, and how many people they reach. When certain
thresholds are crossed, the system fires off an alert. The entire
system—which monitors far more than just tweets—generates about
40 alerts per day. Since it was first booted up in November 2012, EMBERS
has raised over 13,000 alerts.
Those warnings appear on the system’s dashboard, a screen in a
desktop application that looks like a mashup of a Twitter feed, Google
Maps, a basketball tournament bracket, and a cardiac monitor. Alerts
appear automatically, without any input from a human. “It sort of
gives you a global picture of what’s happening,” Ramakrishnan
says. “You can see the alerts popping up on the screen. That at
least tells you, ‘These are the most major regions that seem to be
cause for concern.’ ”
For now, according to the researchers working on it, EMBERS isn’t
involved in day-to-day intelligence activities. But it seems likely that
analysts will be using it or something like it in the near future.
Ramakrishnan says IARPA is interested in a “tunable system,” one
that analysts can tweak to receive more or fewer alerts. Much of the
work done by EMBERS is manual labor for today’s analysts, he says.
“It provides an opportunity for analysts to use this as a filter to
cut across all the chatter.”
EMBERS also makes sense of data that can help predict the outcome of
elections as well as anticipate disease outbreaks. For the latter,
EMBERS draws on standard epidemiological modeling along with a number of
unusual data sources, including restaurant reservations on OpenTable and
parking lot vacancies at hospitals. By monitoring reservations and
cancellations, the system can spot when people are staying at home
rather than eating out, a potential sign of illness. And by counting
cars in satellite images of hospital parking lots, EMBERS knows the
approximate number of visits well before official statistics trickle
out.
A Theory of Conflict
EMBERS represents just one way scientists are trying to solve to the
problem of predicting the future. Others are experimenting with
different approaches. Take the work done a team led by Neil Johnson, a
physicist at the University of Miami, for example.
Johnson and his team were also among the three groups chosen to compete
in the OSI program. They sought develop a theory of human conflict and
apply it to various confrontations. Drawing on various data sets,
including those on infant-parent relationships, protestors and their
governments, computer hackers, high-frequency traders, and terrorists,
Johnson and his colleagues distilled a single equation that they say
describes how any two-sided asymmetric conflicts—the sort where one
side has more power than another—will escalate.
Using their equation, Johnson and his colleagues can predict how a
conflict will develop based on the frequency of clashes early on. If
confrontations are infrequent at first, any subsequent escalation will
be rapid. But when two parties meet each other frequently, the
escalation will be more gradual. It’s a pattern that’s shows up
throughout their varied data sources, from infants fussing for their
mothers’ attention all the way up to the Troubles in Northern
Ireland. “The common feature of all these systems we looked at is
they’re all, like most systems are, asymmetric,” Johnson says.
“One side is trying to pick away at the other.”
While asymmetry defines most conflicts, it doesn’t define them all.
In World War II, for example, both sides were fairly evenly matched. The
same was true in the Cold War. Adding civilians to the mix also changes
the dynamic substantially, Johnson says. “That’s something
we’re actually looking at now.”
Inevitable Questions
We’re still in the early days of predictive science, but already the
field is raising as many questions as it has answered. Could these
algorithms further tip the asymmetry of power toward the already
powerful? What if systems like EMBERS are developed by oppressive
regimes? And what are the consequences of predicting the actions of your
own populace?
The answers, of course, depend. Predictive tools can be powerful
enablers of either democracy or oppression. If a democratic country is
wielding them, its government could prevent protests through preemptive
policy changes, says Braha, the complexity scientist. But, he adds,
“If a protest is predicted in Iran or China, they can use it in a
negative fashion, definitely. They can arrest people before it
happens.”
Ukraine security forces
Ukranian security forces stand ready during protests late last year.
It's possible that governments could use predictive tools to stifle
protests.
It’s also possible that governments could use this data to track
their citizens. EMBERS and other OSI participants are restricted from
tracking U.S. citizens as well as most foreign individuals, says Mares,
the political scientist; the only exception is public foreign
personalities, like politicians. “If a political candidate has a
blog and he’s using it during his campaign, we can certainly track
that. But by law we are not permitted to track individuals,” he
says. Still, the technology is there. “We’re not finding Juanito
in La Paz. But what I’m learning is that if we wanted to find
Juanito in La Paz, we could.”
EMBERS and its kind are possible, of course, because of the sheer amount
of personally identifiable information that’s available online. Much
of it is voluntarily posted to Twitter and Facebook, but plenty is
unwittingly provided to marketing companies and advertisers. Many of
those data sources are unregulated, and many are available for the right
price.
In theory, anyone with sufficient resources and brainpower could build
their own predictive software similar to EMBERS. “Pick your favorite
baddie—to what degree are they invested in the same kinds of
things?” Mares asks rhetorically.
For millennia, predicting the future seemed far fetched. Today, it seems
inevitable. Predictive science is in its infancy, but as we grow more
connected—and more of our worlds become exposed—systems that
anticipate our actions, both individually and in aggregate, will only
grow more sophisticated and more accurate. Mares puts it best:
“We’re just scratching the surface here.”
Tell us what you think on Twitter #novanext, Facebook, or email.
Photo credits: © MANUEL HERNANDEZ/Xinhua Press/Corbis, Sasha
Maksymenko/Flickr (CC BY)
Sources:
Neil F. Johnson, et al. 2013 "Simple mathematical law benchmarks human
confrontations." Scientific Reports 3:3463. DOI: 10.1038/srep03463
Naren Ramakrishnan, et al. 2014. "'Beating the news' with EMBERS:
Forecasting Civil Unrest using Open Source Indicators." arXiv: 1402.7035
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