Debating Big Data for Intelligence

I’m always afraid of engaging in a “battle of wits” only half-armed.  So I usually choose my debate opponents judiciously.

Unfortunately, I recently had a contest thrust upon me with a superior foe: my friend Mark Lowenthal, Ph.D. from Harvard, an intelligence community graybeard (literally!) and former Assistant Director of Central Intelligence (ADCI) for Analysis and Production, Vice Chairman of the National Intelligence Council – and as if that weren’t enough, a past national Jeopardy! “Tournament of Champions” winner.

As we both sit on the AFCEA Intelligence Committee and have also collaborated on a few small projects, Mark and I have had occasion to explore one another’s biases and beliefs about the role of technology in the business of intelligence. We’ve had several voluble but collegial debates about that topic, in long-winded email threads and over grubby lunches. Now, the debate has spilled onto the pages of SIGNAL Magazine, which serves as something of a house journal for the defense and intelligence extended communities.

SIGNAL Editor Bob Ackerman suggested a “Point/Counterpoint” short debate on the topic: “Is Big Data the Way Ahead for Intelligence?” Our pieces are side-by-side in the new October issue, and are available here on the magazine’s site.

Mark did an excellent job of marshalling the skeptic’s view on Big Data, under the not-so-equivocal title, Another Overhyped Fad.”  Below you will find an early draft of my own piece, an edited version of which is published under the title A Longtime Tool of the Community”:

Visit the National Cryptologic Museum in Ft. Meade, Maryland, and you’ll see three large-machine displays, labeled HARVEST and TRACTOR, TELLMAN and RISSMAN, and the mighty Cray XMP-24. They’re credited with helping win the Cold War, from the 1950s through the end of the 1980s. In fact, they are pioneering big-data computers.

Here’s a secret: the Intelligence Community has necessarily been a pioneer in “big data” since inception – both our modern IC and the science of big data were conceived during the decade after the Second World War. The IC and big-data science have always intertwined because of their shared goal: producing and refining information describing the world around us, for important and utilitarian purposes

What do modern intelligence agencies run on? They are internal combustion engines burning pipelines of data, and the more fuel they burn the better their mileage. Analysts and decisionmakers are the drivers of these vast engines, but to keep them from hoofing it, we need big data.

Let’s stipulate that today’s big-data mantra is overhyped. Too many technology vendors are busily rebranding storage or analytics as “big data systems” under the gun from their marketing departments. That caricature is, rightly, derided by both IT cognoscenti and non-techie analysts.

I personally get the disdain for machines, as I had the archetypal humanities background and was once a leather-elbow-patched tweed-jacketed Kremlinologist, reading newspapers and HUMINT for my data. I stared into space a lot, pondering the Chernenko-Gorbachev transition. Yet as Silicon Valley’s information revolution transformed modern business, media, and social behavior across the globe, I learned to keep up – and so has the IC. 

Twitter may be new, but the IC is no Johnny-come-lately in big data on foreign targets.  US Government funding of computing research in the 1940s and ‘50s stretched from World War II’s radar/countermeasures battles to the elemental ELINT and SIGINT research at Stanford and MIT, leading to the U-2 and OXCART (ELINT/IMINT platforms) and the Sunnyvale roots of NRO.

In all this effort to analyze massive observational traces and electronic signatures, big data was the goal and the bounty.

War planning and peacetime collection were built on collection of ever-more-massive amounts of foreign data from technical platforms – telling the US what the Soviets could and couldn’t do, and therefore where we should and shouldn’t fly, or aim, or collect. And all along, the development of analog and then digital computers to answer those questions, from Vannevar Bush through George Bush, was fortified by massive government investment in big-data technology for military and intelligence applications.

In today’s parlance big data typically encompasses just three linked computerized tasks: storing collected foreign data (think Amazon’s cloud), finding and retrieving relevant foreign data (Bing or Google), and analyzing connections or patterns among the relevant foreign data (powerful web-analytic tools).

Word CloudThose three Ft. Meade museum displays demonstrate how NSA and the IC pioneered those “modern” big data tasks.  Storage is represented by TELLMAN/RISSMAN, running from the 1960’s throughout the Cold War using innovation from Intel. Search/retrieval were the hallmark of HARVEST/TRACTOR, built by IBM and StorageTek in the late 1950s. Repetitive what-if analytic runs boomed in 1983 when Cray delivered a supercomputer to a customer site for the first time ever.

The benefit of IC early adoption of big data wasn’t only to cryptology – although decrypting enemy secrets would be impossible without it. More broadly, computational big-data horsepower was in use constantly during the Cold War and after, producing intelligence that guided US defense policy and treaty negotiations or verification. Individual analysts formulated requirements for tasked big-data collection with the same intent as when they tasked HUMINT collection: to fill gaps in our knowledge of hidden or emerging patterns of adversary activities.

That’s the sense-making pattern that leads from data to information, to intelligence and knowledge. Humans are good at it, one by one. Murray Feshbach, a little-known Census Bureau demographic researcher, made astonishing contributions to the IC’s understanding of the crumbling Soviet economy and its sociopolitical implications by studying reams of infant-mortality statistics, and noticing patterns of missing data. Humans can provide that insight, brilliantly, but at the speed of hand-eye coordination.

Machines make a passable rote attempt, but at blistering speed, and they don’t balk at repetitive mindnumbing data volume. Amid the data, patterns emerge. Today’s Feshbachs want an Excel spreadsheet or Hadoop table at hand, so they’re not limited to the data they can reasonably carry in their mind’s eye.

To cite a recent joint research paper from Microsoft Research and MIT, “Big Data is notable not because of its size, but because of its relationality to other data.  Due to efforts to mine and aggregate data, Big Data is fundamentally networked.  Its value comes from the patterns that can be derived by making connections between pieces of data, about an individual, about individuals in relation to others, about groups of people, or simply about the structure of information itself.” That reads like a subset of core requirements for IC analysis, whether social or military, tactical or strategic.

The synergy of human and machine for knowledge work is much like modern agricultural advances – why would a farmer today want to trudge behind an ox-pulled plow? There’s no zero-sum choice to be made between technology and analysts, and the relationship between CIOs and managers of analysts needs to be nurtured, not cleaved apart.

What’s the return for big-data spending? Outside the IC, I challenge humanities researchers to go a day without a search engine. The IC record’s just as clear. ISR, targeting and warning are better because of big data; data-enabled machine translation of foreign sources opens the world; correlation of anomalies amid large-scale financial data pinpoint otherwise unseen hands behind global events. Why, in retrospect, the Iraq WMD conclusion was a result of remarkably-small-data manipulation.

Humans will never lose their edge in analyses requiring creativity, smart hunches, and understanding of unique individuals or groups. If that’s all we need to understand the 21st century, then put down your smartphone. But as long as humans learn by observation, and by counting or categorizing those observations, I say crank the machines for all their robotic worth.

Make sure to read both sides, and feel free to argue your own perspective in a comment on the SIGNAL site.

Kinecting Communities

On April 16 I will be speaking at the Mobile Citizen Summit in Washington DC (registration still open), which brings together “practitioners across the  government, nonprofit, advocacy, and political spaces—the kinds of  people who develop the strategy and the tools to reach, engage, educate,  and enable citizens across the country and around the world.”

But I’m going to be talking about “mobile” in a different way than others still use the term, i.e. they focus on a handheld device, while I will be focusing on the mobile citizen. As I have said before I don’t believe our future involves experiencing “augmented reality” by always holding up little 3-inch plastic screens in front of our faces. Natural user interfaces and immersive computing offer much more to how we access computational resources – and how technology will help us interact with one another. Here’s an example, in a story from the past week.

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Air Everything

Like many people, I was very impressed by a video over the weekend of the Word Lens real-time translation app for iPhone.  It struck with a viral bang, and within a few days racked up over 2 million YouTube views. What particularly made me smile was digging backwards through the twitter stream of a key Word Lens developer whom I follow, John DeWeese, and finding this pearl of a tweet (right) from several months ago, as he was banging out the app out in my old stomping grounds of the San Francisco Bay Area. That’s a hacker mentality for you :)

But one thought I had in watching the video was, why do I need to be holding the little device in front of me, to get the benefit of its computational resources and display? I’ve seen the studies and predictions that “everything’s going mobile,” but I believe that’s taking too literally the device itself, the form-factor of a little handheld box of magic.

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Mix, Rip, Burn Your Research

You’ve done research; you’ve collected and sifted through mounds of links, papers, articles, notes and raw data. Shouldn’t there be a way to manage all that material that’s as easy and intuitive as, say, iTunes or Zune – helping you manage and share your snippets and research the way you share and enjoy your music?

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Your choice, Dataviz as event or book

A friend wrote asking if I could make it to an event happening this week near DC. I can’t make it, but fortunately he also mentioned as consolation that he has a cool new book on the cusp of release – and I’ve now ordered my copy.

The Friend: legendary visualization and HCI guru Ben Shneiderman (Wikipedia entry). Ben is a computer-science professor at the University of Maryland and the founder of its well-known Human-Computer Interaction Laboratory (HCIL), as well as an ACM Fellow and AAAS Fellow.  He has done government a million favors over the years, consulting for agencies, including his recent work on the Recovery.gov site to help that platform of data – from hundreds of thousands of sources – organize, host, and visualize the data for millions of visitors.  I first got to know Ben through his support for better intelligence analysis – he helped invent a longtime intelligence analytics tool, Spotfire (see his article “Dynamic queries, starfield displays, and the path to Spotfire“).  Ben’s also well-known for his award-winning 2002 book Leonardo’s Laptop: Human Needs and the New Computing Technologies, which I enjoyed and still think about when brainstorming new techie toys.

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Free Tools for the New Scientific Revolution

Blogs are great for supplementing real-life events, by giving space and time for specific examples and links which can’t be referenced at the time. I was invited to give a talk last week at the first-ever NASA Information Technology Summit in Washington DC, and the topic I chose was “Government and the Revolution in Scientific Computing.” That’s an area that Microsoft Research has been focusing on quite a bit lately, so below I’ll give some examples I didn’t use at my talk.

One groundrule was that invited private-sector speakers were not allowed to give anything resembling a “sales pitch” of their company’s wares. Fair enough – I’m no salesman.  The person who immediately preceded me, keynoter Vint Cerf, slightly bent the rules and talked a bit about his employer Google’s products, but gee whiz, that’s the prerogative of someone who is in large part responsible for the Internet we all use and love today.

I described in my talk the radical new class of super-powerful technologies enabling large-data research and computing on platforms of real-time and archival government data. That revolution is happening now, and I believe government could and should be playing a different and less passive role. I advocated for increased attention to the ongoing predicament of U.S. research and development funding.

Alex Howard at O’Reilly Radar covered the NASA Summit and today published a nice review of both Vint’s talk and mine.  Some excerpts: Continue reading

Using the body in new virtual ways

This is CHI 2010 week, the Association for Computing Machinery’s Conference on Human Factors in Computing Systems in Atlanta. Top researchers in human-computer-interaction (HCI) are together April 10-15 for presentations, panels, exhibits, and discussions. Partly because of our intense interest in using new levels of computational power to develop great new Natural User Interfaces (NUI), Microsoft Research is well represented at CHI 2010 as pointed out in an MSR note on the conference:

This year, 38 technical papers submitted by Microsoft Research were accepted by the conference, representing 10 percent of the papers accepted. Three of the Microsoft Research papers, covering vastly different topics, won Best Paper awards, and seven others received Best Paper nominations.

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