Can an Algorithm Spot the Next Google?

A startup analyzes tweets, patents, and lots of other data in the hopes of identifying the next big thing.

By definition, “disruptive” technologies are those that take the world by surprise. Now a startup called Quid claims that its software can make good guesses about what the next big thing will be. It does this by analyzing a store of data on existing companies, ideas, and research

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Self Learning Realtime Object Tracking

An impressive video showing off a tracking algorithm resulting from Zdenek Kalal’s phd thesis at the University of Surrey, UK. You can try it yourself by downloading a compiled application to your PC, and read more about it here. Though desktop bound right now, Kalal claims that “implementation for mobile devices is feasible”.

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Just landed – Realtime Infographics

Interesting concept.

From the author:

I was discussing H1N1 with a bioinformatics friend of mine last weekend, and we ended up talking about ways that epidemiologists model transmission of disease. I wondered how some of the information that is shared voluntarily on social networks might be used to build useful models of various kinds.

I’m also interested in visualizing information that isn’t implicitly shared – but instead is inferred or suggested.

This piece looks for tweets containing the phrases ‘just landed in…’ or ‘just arrived in…’. Locations from these tweets are located using MetaCarta’s Location Finder API. The home location for the traveling users are scraped from their Twitter pages. The system then plots these voyages over time.

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Will You Tweet This?

When a piece of news breaks online, it’s hard to predict how widely it will be discussed in blog posts or tweets and for how long.

Jure Leskovec, an assistant professor of computer science at Stanford University, is working to find a way to make it easier to predict which pieces of content will resonate for a long time. A lot of factors go into that equation, however—the content of the story itself, the popularity of the site where the story originally appeared, and the nature of the community of readers at which it’s aimed.

Two new research papers, written by Leskovec and Stanford PhD candidate Jaewon Yang, reveal patterns in the way news stories are shared online, which offer a way to predict early on how a story’s popularity will rise and fall.

[via Technology Review Will You Tweet This?]

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Datamining power tool

After purchasing Freebase the public graph database Google has also taken Freebases data mining application Gridbase under its wings. Now named Google Refine it still is a power tool for working with messy data, cleaning it up, transforming it from one format into another, extending and correcting it with web services, and linking it to databases like Freebase.
Version 2.0 has just been announced. While you’re at it. Also have a look at Freebases powerful API that provides access to an amazingly big community build ontology. You can play with it in the Query Editor.

Back on the original topic. There are some introduction videos on Google Refine online:

http://www.youtube.com/watch?v=m5ER2qRH1OQ

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