Dopplr. Where next?

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i’ve taken the plunge and signed-up to Dopplr.

Matt Jones from Dopplr came in to do a lunchtime session at glue and having seen some of the cool stuff they’re doing with data it was only a matter of time.

Dopplr lets you share your future travel plans privately with friends and colleagues. The service then highlights coincidence, for example, telling you that three people you know will be in Paris when you will be there too. You can use Dopplr on your computer or mobile, and it links with online calendars and social networks.

The UI and design of the site is simple yet fantastic, the use of advanced coding tricks is clever but never excessive, and it’s another great example of a “connected” app, talking to Gmail, Hotmail, Yahoo!, LinkedIn, Twitter, Flickr, Facebook and Fire Eagle.

You can SMS, Twitter and email your tips into Dopplr and there are loads of nice touches which build over time, such as Personal Velocity.

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Take a look at my trips:

Read more about Dopplr and the technology behind it on their blog or take a tour.

Process and workflow. Walt Disney Pictures 1943 style.


How do they make those drawings move?

This circular chart from 1943 shows the development process of an animated film through the different roles within the Disney organization.

Not exactly an org chart, this is more of a process map. This chart explains the whole process. You can see it all starts with “Walt” and his main focus was always “Story” and “Direction”.

via Cool Infographics

Visualisation of box office charts

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A visualization of the 2008 US movie box office revenues. each graph shows the trends in the top 25 movies at the box office for each weekend in a year. the color is based on the movie’s debut week so that long-running movies gradually start to stand out from newer movies with different colors.

See more charts:

via Information Aesthetics

Number Of Men In The United States Who Will Die In 2008


This infographic features statistics of how men in the United States will die in 2008. There are various causes covered including suicide, heatstroke, and electrocutions. The data was collected from the CDC’s WONDER Database.

WONDER stands for Wide-ranging Online Data for Epidemiologic Research which is a collection of data that’s available to public health professionals and the public at large, boasting a wide array of public health information.

Grim eh. Looks nice though.

Show Us a Better Way. What would you create with public information?

The UK Power of Information Taskforce is challenging developers to mash up their public data. Today it launched Show Us a Better Way, collecting ideas and offering £20,000 to develop the best ones. There have already been 50 ideas submitted. The list of ideas is public.

To show they are serious, the Government is making available gigabytes of new or previously invisible public information especially for people to use in this competition.

Will large amounts of information and data change how we learn?

Data Centre

Will extremely large databases of information, starting in the petabyte level, change how we learn. It may turn out that tremendously large volumes of data are sufficient to skip the theory in order to make a predicted observation.

Google was one of the first to notice this. For instance, take Google’s spell checker. When you misspell a word when googling, Google suggests the proper spelling. How does it know this? How does it predict the correctly spelled word? It is not because it has a theory of good spelling, or has mastered spelling rules. In fact Google knows nothing about spelling rules at all.

Instead Google operates a very large dataset of observations which show that for any given spelling of a word, x number of people say “yes” when asked if they meant to spell word “y.” Google’s spelling engine consists entirely of these datapoints, rather than any notion of what correct English spelling is. That is why the same system can correct spelling in any language.

In fact, Google uses the same philosophy of learning via massive data for their translation programs. They can translate from English to French, or German to Chinese by matching up huge datasets of humanly translated material. For instance, Google trained their French/English translation engine by feeding it Canadian documents which are often released in both English and French versions. The Googlers have no theory of language, especially of French, no AI translator. Instead they have zillions of datapoints which in aggregate link “this to that” from one language to another. 

Continue reading

The Google Way of Science

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete