Newer posts are loading.
You are at the newest post.
Click here to check if anything new just came in.

July 18 2011

To get things done, be "reasonably unreasonable"

Turing_Plaque.pngIn a recent interview, John Graham-Cumming (@jgrahamc), VP of engineering at Causata, Inc. and a speaker at OSCON 2011, said to change the world, it's often necessary to act in a "reasonably unreasonable" manner — to go just enough against the norm to effectively rock the boat.

He knows what he's talking about. In 2009, using a blend of new and old media tools and a bit of geek expertise, Graham-Cumming got the UK government to apologize for its treatment of mathematician and computer scientist Alan Turing in the 1950s. Below he discusses the techniques that produced that apology.

Your OSCON session description says people need to be "reasonably unreasonable" to change the world. What does that mean?

JohnGraham_Cumming.jpgJohn Graham-Cumming: You have to be "unreasonable" to get things done. By that, I mean that you have to go against the norm. If you are reasonable and go in the direction of society, then you don't contribute greatly. Just look at people like Richard Stallman and Linus Torvalds. Stallman's ideas were pretty "unreasonable" at a time when there was a large move to proprietary software. Torvalds was "unreasonable" in thinking that he could build his own kernel and in telling Tanenbaum where to go.

I say "reasonably" because you shouldn't take being unreasonable so far that people don't listen to you.

How did you apply that philosophy to your campaign to make the British Government apologize for the mistreatment of Alan Turing?

John Graham-Cumming: The important thing about the Turing campaign was that I felt that people were celebrating Turing without acknowledging the harm done to him by Britain's laws at the time. I didn't want people to be able to sweep this under the rug, so I decided to just tell everyone about what happened to him and ask for an apology from the UK government. That was pretty "unreasonable" in the sense that the UK government doesn't apologize for much, and I thought they would ignore my request.

Geek Lifestyle at OSCON 2011 — From fine-tuning your setup to taking the geek approach to growing your own food, we'll celebrate and explore hacker culture in all its richness in the Geek Lifestyle track at OSCON (July 25-29 in Portland, Ore.)

Save 20% on registration with the code OS11RAD

What tools did you use in your Turing campaign?

John Graham-Cumming: Twitter was very effective at getting people to hear about the campaign, but it's an echo chamber and builds pretty slowly. You tend to get the same circle of people mentioning an issue because they care about it, and it's hard to get it to a large audience.

What really works is having a definitive source of information that people can point to on Twitter. So, when the BBC wrote about the Turing campaign on its website, Twitter was able to amplify that with thousands tweeting — and ultimately signing the petition. In some ways, what Twitter needs is a Wikipedia-style "[citation needed]" so that people take what's written on it seriously.

Facebook also was helpful, but I think Twitter was much more effective.

I also appeared on countless radio programs, on TV, in print and anywhere else I could. I made myself the focus of the campaign initially, and then when celebrities started signing, I used them to get the media to talk about the campaign.

The important thing to realize with the media is that there needs to be a hook or peg onto which the story they are telling can be hung. So, initially there was some press about the campaign starting, and then I'd badger people in the press when there was a suitable hook — for example, when Richard Dawkins signed and publicly stated his support. You have to look for things to tell the press so they know what to write about.

What kind of code did you use in your campaign, and how did you use it?

John Graham-Cumming: I used a custom Perl script that downloaded the names of the signatories every hour, looked them up on Wikipedia and, using some simple techniques, figured out if they were celebrities of any kind. If they were, then I was sent an email by the script and would try to get in contact with the celebrity to see if I could use his or her name.

How do you translate technical and historical concepts into calls to action?

John Graham-Cumming: You just have to tell a human story. In the case of Turing, this was easy: he was clearly a genius, a war hero, and then he was prosecuted and he committed suicide.

This interview was edited and condensed.

Photo: Turing Plaque by Joseph Birr-Pixton, on Wikimedia Commons


  • Lessig on Culture and Change
  • Creating cultural change
  • John Graham-Cumming's project to build Babbage's Analytical Engine

  • August 12 2010

    Watson, Turing, and extreme machine learning

    One of best presentations at IBM's recent Blogger Day was given by David Ferrucci, the leader of the Watson team, the group that developed the supercomputer that recently appeared as a contestant on Jeopardy.

    To many people, the Turing test is the gold standard of artificial intelligence. Put briefly, the idea is that if you can't tell whether you're interacting with a computer or a human, a computer has passed the test.

    But it's easy to forget how subtle this criterion is. Turing proposes changing the question from "Can machines think?" to the operational criterion, "Can we distinguish between a human and a machine?" But it's not a trivial question: it's not "Can a computer answer difficult questions correctly?" but rather, "Can a computer behave in ways that are indistinguishable from human behavior?" In other words, getting the "right" answer has nothing to do with the test. In fact, if you were trying to tell whether you were "talking to" a computer or a human, and got only correct answers, you would have every right to be deeply suspicious.

    Alan Turing was thinking explicitly of this: in his 1950 paper, he proposes question/answer pairs like this:

    Q: Please write me a sonnet on the subject of the Forth Bridge.

    A: Count me out on this one. I never could write poetry.

    Q: Add 34,957 to 70,764.

    A: (Pause about 30 seconds and then give as answer) 105,621.

    We'd never think of asking a computer the first question, though I'm sure there are sonnet-writing projects going on somewhere. And the hypothetical answer is equally surprising: it's neither a sonnet (good or bad), nor a core dump, but a deflection. It's human behavior, not accurate thought, that Turing is after. This is equally apparent with the second question: while it's computational, just giving an answer (which even a computer from the early '50s could do immediately) isn't the point. It's the delay that simulates human behavior.

    Dave Ferrucci, IBM scientist and Watson project director
    Dave Ferrucci, IBM scientist and Watson project director

    While Watson presumably doesn't have delays programmed in, and appears only in a situation where deflecting a question (sorry, it's Jeopardy, deflecting an answer) isn't allowed, it's much closer to this kind of behavior than any serious attempt at AI that I've seen. It's an attempt to compete at a high level in a particular game. The game structures the interaction, eliminating some problems (like deflections) but adding others: "misleading or ambiguous answers are par for the course" (to borrow from NPR's "What Do You Know"). Watson has to parse ambiguous sentences, decouple multiple clues embedded in one phrase, to come up with a question. Time is a factor -- and more than time, confidence that the answer is correct. After all, it would be easy for a computer to buzz first on every question, electronics does timing really well, but buzzing first whether or not you know the answer would be a losing strategy for a computer, as well as for a human. In fact, Watson would handle the first of Turing's questions perfectly: if it isn't confident of an answer, it doesn't buzz, just as a human Jeopardy player.

    Equally important, Watson is not always right. While the film clip on IBM's site shows some spectacular wrong answers (and wrong answers that don't really duplicate human behavior), it's an important step forward. As Ferrucci said when I spoke to him, the ability to be wrong is part of the problem. Watson's goal is to emulate human behavior on a high level, not to be a search engine or some sort of automated answering machine.

    Some fascinating statements are at the end of Turing's paper. He predicts computers with a gigabyte of storage by 2000 (roughly correct, assuming that Turing was talking about what we now call RAM), and thought that we'd be able to achieve thinking machines in that same time frame. We aren't there yet, but Watson shows that we might not be that far off.

    But there's a more important question than what it means for a machine to think, and that's whether machines can help us to ask questions about huge amounts of ambiguous data. I was at a talk a couple of weeks ago where Tony Tyson talked about the Large Synoptic Survey Telescope project, which will deliver dozens of terabytes of data per night. He said that in the past, we'd use humans to take a first look at the data and decide what was interesting. Crowdsourcing analysis of astronomical images isn't new, but the number of images coming from the LSST is even too large for a project like GalaxyZoo. With this much data, using humans is out of the question. LSST researchers will have to use computational techniques to figure out what's interesting.

    "What is interesting in 30TB?" is an ambiguous, poorly defined question involving large amounts of data -- not that different from Watson. What's an "anomaly"? You really don't know until you see it. Just as you can't parse a tricky Jeopardy answer until you see it. And while finding data anomalies is a much different problem from parsing misleading natural language statements, both projects are headed in the same direction: they are asking for human behavior in an ambiguous situation. (Remember, Tyson's algorithms are replacing humans in a job humans have done well for years). While Watson is a masterpiece of natural language processing, it's important to remember that it's just a learning tool that will help us to solve more interesting problems. The LSST and problems of that scale are the real prize, and Watson is the next step.

    Photo credit: Courtesy of International Business Machines Corporation. Unauthorized use not permitted.


    Older posts are this way If this message doesn't go away, click anywhere on the page to continue loading posts.
    Could not load more posts
    Maybe Soup is currently being updated? I'll try again automatically in a few seconds...
    Just a second, loading more posts...
    You've reached the end.

    Don't be the product, buy the product!