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Michael Nielsen » Science beyond individual understanding

Tags: Science2.0, Interesting, DataMining on 2008-09-27 -All Annotations (10) -About

more frommichaelnielsen.org

When participants in a market are mistaken in systematic ways, markets don’t so much aggregate knowledge as they aggregate misunderstanding.
The result can be an enormous collective error in judgement; when the misjudgement is revealed, the market crashes.
In economics, many basic facts, such as prices, have an origin which isn’t completely understood by any single person, no matter how bright or well informed, because none of those people have access to all the hidden knowledge that determines those prices.
Science is no longer so simple; many important scientific facts now have justifications that are beyond the comprehension of a single person.
Science as complex as the LHC and the classification of finite simple groups is a recent arrival on the historical scene. But there are two forces that will soon make science beyond individual understanding far more common.
The first of these forces is rapid internet-fueled growth in the number of large-scale scientific collaborations. In the short term, these collaborations will mostly just crowdsource rote work, as is being done, for example, by the galaxy classification project Galaxy Zoo, and so the results will pose no challenge to individual understanding. But as the collaborations get more sophisticated we can expect to see many more online collaborations that delegate large amounts of specialized work, building up to a whole whose details aren’t fully understood by any single person.
The second of these forces is the use of computers to do scientific work. A nascent example is the proof of the four-colour theorem in mathematics. A small group of mathematicians outlined a proof, but to complete the proof, they had to check a large number of cases of the theorem, more than they could check by hand. Instead, a computer was used to check those cases. This isn’t an instance of science beyond individual understanding, though, because mathematicians familiar with the proof feel the computer was simply doing rote work. But the people doing computational science are getting cleverer in how they use computers to make discoveries. Machine learning, data mining and artificial intellgience techniques are being used in increasingly sophisticated ways to produce real insights, not just rote work. As the techniques get better, the number of insights found will increase, and we can expect to see examples of science beyond individual understanding generated this way: “I don’t understand how this discovery was made, but my computer and I do together”.
More powerful than either of these forces will be their combination: large-scale computer-assisted collaboration. The discoveries from such collaboration may well not be understood by any single individual, or even by a group. Instead, it will reside inside a combination of the group and their networked computers.
Such scientific discoveries raise challenging issues. How do we know whether they’re right or wrong?
Might we one day find ourselves in a situation like in a free market where systematic misunderstandings can infect our collective conclusions? How can we be sure the results of large-scale collaborations or computing projects are reliable? Are there results from this kind of science that are already widely believed, maybe even influencing public policy, but are, in fact, wrong?

沈黙を破ったホリエモン，ITを語る：ITpro

Tags: Japan, Interesting on 2008-09-15 and saved by9 people -All Annotations (22) -About

more fromitpro.nikkeibp.co.jp

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The Long Now Blog » Blog Archive » Daniel Suarez, “Daemon: Bot-Mediated Reality”

Tags: Interesting on 2008-09-14 -All Annotations (1) -About

more fromblog.longnow.org

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Edge: GIN, TELEVISION, AND COGNITIVE SURPLUS A Talk By Clay Shirky

Tags: Interesting on 2008-09-12 and saved by3 people -All Annotations (8) -About

more fromwww.edge.org

Social software has arrived, and if you don't pay attention and take onboard the developments at Google, Twitter, Facebook, Wikipedia, etc., you are opting out of being a serious player in the realm of 21st Century ideas.
Starting after the second world war, a whole host of factors, like rising GDP, rising educational attainment, and rising life-span, forced the industrialized world to grapple with something new: free time. Lots and lots of free time. The amount of unstructured time among the educated population ballooned, accounting for billions of hours a year. And what did we do with that time? Mostly, we watched TV.
Shirky believes that "we can take advantage of our cognitive surplus, but only if we start regarding pure consumption as an anomaly, and broad participation as the norm.
This is not to say that the idea of the social graph as the next big thing invalidates the other insights I was working with. Instead, it clarifies and expands them:
The social graph is only one instance of a class of data structure that will prove increasingly important as we build applications powered by data at internet scale. You can think of the mapping of people, businesses, and events to places as the "location graph", or the relationship of search queries to results and advertisements as the "question-answer graph."
Both Clay, who thought then that "social software" was a meaningful metaphor and I, who found it less useful then than I do today, have changed our minds. A concept is a frame, an organizing principle, a tool that helps us see. It seems to me that we all change our minds every day through the accretion of new facts, new ideas, new circumstances. We constantly retell the story of the past as seen through the lens of the present, and only sometimes are the changes profound enough to require a complete repudiation of what went before.
And television watching? Two hundred billion hours, in the U.S. alone, every year. Put another way, now that we have a unit, that's 2,000 Wikipedia projects a year spent watching television. Or put still another way, in the U.S., we spend 98 million hours every weekend, just watching the ads. This is a pretty big surplus. People asking, "Where do they find the time?" when they're looking at things like Wikipedia don't understand how tiny that entire project is, as a carve-out of the cognitive surplus that's finally being dragged into what Tim O'Reilly calls an architecture of participation.