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.

The almighty ampersand linking R and D

According to Wikipedia, the lowly ampersand or “&” is a logogram representing the conjunction word “and” using “a ligature of the letters in et,” which is of course the Latin word for “and.”

In my line of work I most frequently encounter the ampersand in the common phrase “R&D” for research and development, although I notice that with texting and short-form social media the ampersand is making something of a comeback in frequency of use anyway.

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Tearing the Roof off a 2-Terabyte House

I was home last night playing with the new Kinect, integrating it with Twitter, Facebook, and Zune. Particularly because of the last service, I was glad that I got the Xbox 360 model with the 250-gigabyte (gb) hard disk drive. It holds a lot more music, or photos, and of course primarily games and game data.

So we wind up with goofy scenes like my wife zooming along yesterday in Kinect Adventures’ River Rush – not only my photo (right) but in-game photos taken by the Kinect Sensor, sitting there below the TV monitor.

Later as I was waving my hands at the TV screen, swiping magically through the air to sweep through Zune’s albums and songs as if pawing through a shelf of actual LP’s, I absent-mindedly started totting up the data-storage capacity of devices and drives in my household.  Here’s a rough accounting:

  • One Zune music-player, 120gb;
  • 2 old iPods 30gb + 80gb;
  • an iPad 3G at 16gb;
  • one HP netbook 160gb;
  • an aging iMac G5 with 160gb;
  • three Windows laptops of 60gb, 150gb, and 250gb;
  • a DirecTV DVR with a 360gb disk;
  • a single Seagate 750gb external HDD;
  • a few 1gb, 2gb, and a single 32gb SD cards for cameras;
  • a handful of 2gb, 4gb, and one 16gb USB flash drives;
  • and most recently a 250gb Xbox 360, for Kinect. 

All told, I’d estimate that my household data storage capacity totals 2.5 terabytes. A terabyte, you’ll recall, is 1012 bytes, or 1,000,000,000,000 (1 trillion) bytes, or alternately a thousand gigabytes.

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Playing with virtual data in spatial reality

For the past few months, when I’ve had visitors to Microsoft Research on the Redmond campus one of the things I’ve enjoyed demonstrating is the technology behind the new system for Xbox 360 – the controller-free gaming and immersive entertainment system that Microsoft is releasing for the holiday market in a month or so. In particular, I’ve enjoyed having Andy Wilson of MSR talk with visitors about some of the future implications in non-gaming scenarios, including general information work, and how immersive augmented-reality (AR) could transform our capabilities for working with information, virtual objects, and how we all share and use knowledge among ourselves.

We’re further along in this area than I thought we’d be five years ago, and I suspect we’ll be similarly surprised by 2015.

In particular, there is great interest (both in and out of the government circles I travel in) in the “device-less” or environmental potential of new AR technologies. Not everyone will have a fancy smartphone on them at all times, or want to stare at a wall-monitor while also wearing glasses or holding a cellphone in front of them in order to access other planes of information. The really exciting premise of these new approaches is the fully immersive aspect of  “spatial AR,” and the promise of controlling a live 3D environment of realtime data. Continue reading

Four Score and Seven Years Ago

Today, August 5, has a number of interesting anniversaries in the world of technology and government. In 1858 the first transatlantic telegraph cable was completed, allowing President James Buchanan and Queen Victoria to share congratulatory messages the following week. (Unfortunately within a month the cable had broken down for good.)  The first quasar (“quasi-stellar astronomical radio object”) was discovered on Aug. 5, 1962. And exactly one year later the Nuclear Test Ban Treaty was signed on August 5, 1963, between the U.S., U.S.S.R., and Great Britain.

But one important date I’d like to commemorate was a bit different: eighty-seven years ago today, on August 5, 1923, my father was born, in Greensboro, North Carolina. Happy Birthday, Dad!

There’s a shorthand way of telling my father’s life-history which fits with the theme of technological advance: he graduated from college (his beloved N.C. State) as an early recipient of a B.S. degree in Mechanical Engineering; he worked for decades for a growing company interested in adopting new technologies to drive its business; and he capped his career as Corporate Vice President for Research and Development at a Fortune 300 company.

But that misses the fun he had along the way, and the close-up view he had of innovation. He was an early adopter, even before college. (I like to think I get that from him.)  So I thought I’d illustrate a couple of vignettes I’ve heard over the years of his interaction with computers along the way, simply to portray the thrust of radical change that has paced along during the course of one man’s life.

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DARPA crowd guru gets a new lab

It’s been a little over two years since I came back to the tech private sector from my government service, and it’s great when we have other folks take the same path, for it improves the knowledge of each side about the other. Today we’re announcing that Peter Lee, currently the leader of the Defense Advanced Research Projects Activity’s innovative Transformational Convergence Technology Office (TCTO), is joining Microsoft to run the mighty flagship Redmond labs of Microsoft Research.

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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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