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The Filter Bubble

Follow the Filter Bubble

Like: the enemy of love?

/ May 30th, 2011 /

I’m not sure Jonathan Franzen had filter bubbles in mind, but in yesterday’s NYT’s op-ed the author points out yet another casualty of being surrounded by things you like: love.

Excerpt from the article:

A related phenomenon is the transformation, courtesy of Facebook, of the verb “to like” from a state of mind to an action that you perform with your computer mouse, from a feeling to an assertion of consumer choice. And liking, in general, is commercial culture’s substitute for loving. The striking thing about all consumer products — and none more so than electronic devices and applications — is that they’re designed to be immensely likable. This is, in fact, the definition of a consumer product, in contrast to the product that is simply itself and whose makers aren’t fixated on your liking it. (I’m thinking here of jet engines, laboratory equipment, serious art and literature.)

Filtering ourselves by eyewear

/ May 25th, 2011 /

Personalization algorithms, especially those that factor in our friends’ preferences, have a way of clumping us into ever more homogeneous and like-minded groups. That’s one of the central ideas of The Filter Bubble.

But, as Eli freely admits, online personalization is not the only force filtering out diversity and sieving in homogeneity. We humans are very good at sorting ourselves into groups that look and think much like ourselves – without the help of algorithms.

The power of homophily, the sociological term for our self-sorting tendencies, hardly needs scholarly backing ; just glancing around any college cafeteria should be enough to convince that we flock to birds of similar feathers. But that doesn’t stop academics from supplying hundreds of studies for evidence. In two such recent papers, researchers show the breadth and depth of our self-sorting behavior.

On the high-commitment end, we marry within our political party. That may not seem surprising, but when you compare it to a weaker tendency to marry people with similar personality traits, it suggests that for the most important decisions in life we value people who think like we do more than those who act like us.

At the other – superficial – extreme, we sit near people who look like us. Based on self reports and experiments, researched subjects tend to find seats next to people of the same gender, race, hair length, hair color and general attractiveness. And, yes, eyeglass wearers prefer sitting next to each other too.

Popping bubbles at MIT

/ May 23rd, 2011 /

Tyler Cowen thinks American innovation is in trouble.

At a TEDx talk two weeks ago he listed the causes of its demise, one being that American research and development is “tinkering with the parts not the whole.” There is “intensive innovation at the margins,” with experts refining the technological advances they know well. But few have the inclination or breadth to look across research fields and come up with entirely new technologies.

It’s a criticism that’s been leveled at university departments (with their academic fiefdoms) and, of course, which Eli sees happening more and more web-wide. As we nestle into our separate information enclaves, we’re missing the opportunities for insight and invention – whether in public policy, culture or innovation – that come from a broad perspective.

Worrisome. But MIT may have an antidote – at least when it comes to what’s ailing technological innovation.

As profiled by Ed Pilkington in the Guardian last week, MIT has long made a habit of pushing its faculty to cross academic boundaries – or disregard boundaries entirely – in order to explore ideas and inventions that may, at first glance, seem hair-brained or useless. That’s how composer and inventor Tod Machover could spend years tinkering with a “hyperinstrument” for Yo-Yo Ma, developing technologies that two of his students would eventually use to build Rock Band and Guitar Hero.

It’s more than just the gestalt of the place though. MIT actively encourages cross-discipline collaboration:

MIT delights in taking brilliant minds in vastly diverse disciplines and flinging them together. You can see that in its sparkling new David Koch Institute for Integrative Cancer Research, which brings scientists, engineers and clinicians under one roof. Or in its Energy Initiative, which acts as a bridge for MIT’s combined firepower across all its five schools, channelling huge resources into the search for a solution to global warming.

With all those big brains bouncing off each other, it comes as little surprise then that MIT’s alumni have gone on to found tech companies that now account for 1/7th of US GDP – and half the ventures in Silicon Valley.

But why is MIT’s kind of innovation not happening among business behemoths, who surely have the money to fund MIT type R&D?

Daron Acemoglu, economist and MIT luminary himself, has an answer. And it’s pretty simple (although he has pages of econo-math to prove it): research only pays if you can convert it into marketable innovations today. But truly earth-shifting discoveries and innovations usually take years – or sometimes centuries – before they can be translated into marketable products. That’s why, as Cowen says, in business innovations only happen at the tips of the branches. Sprouting new technological branches simply doesn’t pay off – even with 20-year patents.

Luckily MIT isn’t dependent upon immediate profit; instead, government funding heavily subsidizes its seemingly zany, boxless and even pointless research. But we all benefit – when that freedom to explore and collaborate turns into new businesses  and jobs down the road.

Publicly subsidized research, of course, has always been recognized as key to national economic growth (unless you’re a strict libertarian). MIT just happens to be a particularly strong example of how those subsidies can foster cross-discipline innovation. The market, as Acemoglu explains, isn’t providing the incentives for ground-breaking research and collaboration – so the government needs to step in.

But what about when about the market doesn’t provide incentives for all of us to explore ideas that are new, outside our usual interests, or challenging to our current perspectives? What should be the government’s role when the market instead draws us into filter bubbles because, well, selling the familiar and habitual is more lucrative.

In his book, Eli shies away from government imposed responses to the Filter Bubble. It’s a wise caution; you want to be really careful when it comes to imposing state rules on information flows. But could we invent and subsidize an internet MIT, a place where it pays to look beyond your perspective, a counter-force to our filter bubbles? I have no idea what that would look like, but if an online venture could push us to challenge our views, explore new ideas and even learn about what is distasteful to us – I’d give my tax dollars to subsidize that check.

Guessing Google’s 57 signals

/ May 18th, 2011 /

If you watched Eli’s TED talk, you know that Google uses 57 signals to personalize your search results, even when you’re logged out of your Google account. Eli spoke to a Google engineer who revealed a few of them: the computer you’re using, your browser, and your location. While the other 54 signals remain shrouded in quintessential Google-esque mystery, Web science PhD student René Pickhardt has ventured some really interesting educated guesses. Check it out:

Genetically modified crowds

/ May 17th, 2011 /

To anyone familiar with human stampedes, financial panics or professional soccer games, the “Wisdom of Crowds” may seem more like an oxymoron than a legitimate pursuit of study.

But as James Surowiecki so adeptly describes in his book of that name, crowds can be wise.

Under certain conditions. First, they must be diverse; homogeneous groups will be limited – or aggravated – by their shared narrow perspective. Next, groups must be set up so that individuals can think independently, thus avoiding the twin traps of groupthink and mis-information cascades. Finally, they have to have a way to aggregate their ideas, inputs and decisions.

Open source communities and (some) markets are good examples that meet all three conditions. Filter bubbles, which encourage homogeneity and cascades while eschewing communal aggregation, are not.

But if filter bubbles may make online communities stupid, could we make algorithms that make us collectively smarter?

We’re probably a long way off, but the Center for Collective Intelligence at MIT is at least moving in the direction. Led by Thomas Malone, the center is looking more deeply at the “DNA” of smart groups; how the “what, who, why and how” of a group correlates to group intelligence.

In a study published last year, Malone and his colleagues discovered that average intelligence, for one, does not  predict group intelligence. Other factors, such as group cohesion, satisfaction and motivation, are only moderately correlated. What does make a group smarter? Having a few people who are “socially sensitive;” that is, members who tend to be more open and receptive.

Malone and his crew are taking results like that and mapping them onto a “genome” of group intelligence. Workplaces and organizations are taking note, but so are news sites and online government initiatives interested in harnessing the intelligence of readers and constituents. Have an under-producing team or a comment thread full of flamers? Time for some group dynamic gene-splicing.

It might be too far a reach to translate MIT’s work to the the group dynamics of the internet as a whole – at least in the near future. But perhaps one day we’ll be building algorithms to maximize collective intelligence rather than just personal relevance.






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