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Month: August 2010

The Google cloud

I just watched this interesting interview with Hugo Barra, director of product management at Google (G), talking about the convergence between mobile net devices and cloud computing. He is mainly answering questions on G plans for the next 2-5 years but a couple of long-term ideas seep through. First, they are thinking sensors and massively redundant cloud data-centers, and they are thinking of them as part of a constant feedback process for which low latency is the key. In other words, your phone’s camera and microphone talk directly to the G data-cloud on a latency of under 1 second – whatever you film on your camera you can voice-recall on any device within 1 second flat. The implications are huge, because G is effectively eliminating the need for local data storage. Second, to get there, they are rolling out real-time voice search by the end of next year. Real time voice search allows you to query the cloud in, well, under 1 second. Third, they are thinking of this whole process as ‘computer vision’ – a naming tactic which might seem plain semantics, but nevertheless reveals a lot. It reveals that G sees stationary computers as blind, that for them mobile computers are first and foremost sensors, and that sensors start truly seeing only when there is low latency feedback between them and the cloud. How so? The key of course is in the content – once storage, processing power and speed get taken care of by the cloud, the clients  – that is, us – start operating at a meta level of content which is quite hard to even fully conceptualize at the moment (Barra admits he has no idea where this will go in 5 years). The possibilities are orders of magnitude beyond what we are currently doing with computers and the net.

A related video, though with a more visionary perspective, is this talk by Kevin Kelly on the next 5000 days of the net. I show this to all my media students, though I don’t think any of them truly grasp what all-in-the-cloud implies. The internet of things. More on this tomorrow.

Random Links

What collapsing empire looks like by Glenn Greenwald: – The title speaks for itself. A list of bad news from all across the US – power blackouts, roads in disrepair, no streetlights, no schools, no libraries – reads like Eastern Europe after the fall of communism, only that the fall is yet to come here.

Special Operations’ Robocopter Spotted in Belize by Olivia Koski: – Super quiet rotors, synthetic-aperture radar capable of following slow moving people through dense foliage, and ability to fly autonomously through a programmed route. This article complements nicely the one above.

Open Source Tools Turn WikiLeaks Into Illustrated Afghan Meltdown by Noah Shachtman: – Meticulous graphical representation of the WikiLeaks Afghan log. The Hazara provinces in the center of the country, and the shia provinces next to the Iranian border seem strangely quiet.

Google Agonizes on Privacy as Ad World Vaults Ahead by Jessica E. Vascellaro: – A fascinating look at the inside of the Google machine. They seem to have reached a crossroad of their own making – they either start using the Aladdin’s cave of data they have gathered already, or they keep it at arm’s length and lay the foundations of their own demise. Key statement: ‘In short, Google is trying to establish itself as the clearinghouse for as many ad transactions as possible, even when those deals don’t actually involve consumer data that Google provides or sees.’

Towards a Taxonomy of Social Networking Data

Bruce Schneier has posted over at his blog the following draft of a social networking data taxonomy:

  • Service data is the data you give to a social networking site in order to use it. Such data might include your legal name, your age, and your credit-card number.
  • Disclosed data is what you post on your own pages: blog entries, photographs, messages, comments, and so on.
  • Entrusted data is what you post on other people’s pages. It’s basically the same stuff as disclosed data, but the difference is that you don’t have control over the data once you post it — another user does.
  • Incidental data is what other people post about you: a paragraph about you that someone else writes, a picture of you that someone else takes and posts. Again, it’s basically the same stuff as disclosed data, but the difference is that you don’t have control over it, and you didn’t create it in the first place.
  • Behavioral data is data the site collects about your habits by recording what you do and who you do it with. It might include games you play, topics you write about, news articles you access (and what that says about your political leanings), and so on.
  • Derived data is data about you that is derived from all the other data. For example, if 80 percent of your friends self-identify as gay, you’re likely gay yourself.

Why is this important? Because in order to develop ways to control the data we distribute in the cloud we need to first classify precisely the different types of data and their relational position within our digital footprint and the surrounding ecology. Disclosed data is of different value to Behavioral or Derived data, and most people will likely value their individual content such as pictures and posts much more than the aggregated patterns sucked out of their footprint by a social network site’s algorithms. Much to think about here.