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concepts in a new way and create innovative ideas. For example, using a Formula 1 pit crew as a metaphor for doctors in an emergency room has helped improve medical procedures. YossarianLives aims to create new metaphors for designers, artists, writers or even scientists.

The name is derived from the anti-hero of the novel Catch-22, as the company wants to solve the catch-22 of existing search engines, which they say help us to access current knowledge but also harm us by reinforcing that knowledge above all else.

Neeley won’t reveal exactly how the engine works, but says they aren’t directly teaching the

system any metaphors. Instead, they are using statistical natural-language processing techniques similar to those employed by Google and other search engines. These methods map out the relationship between words, putting closely related concepts such as “dog” and “bone” near to each other while placing unrelated terms such as “dog” and “ironing board” further apart. While a regular search engine typically returns the nearest terms, Neeley says YossarianLives looks for words that are further

away but still share a linking conceptual structure.

The search engine’s success depends on divining such connections. Phil Blunsom, a researcher in computational linguistics at the University of Oxford, is sceptical. “Detecting metaphors is pretty difficult in itself, mapping between them is very difficult, and to do this with enough accuracy to be usable seems a bit hopeful,” he says.

One version of the search engine creates a list of words based on an internet search, but only returns a single image representing one of those words, after searching for appropriately tagged photos on Flickr.

The results can be ambiguous. When New Scientist tested the system and searched for “science” the image of a happy couple was returned, which Neeley says could indicate “chemistry”. As such, the user seems to be the one creating the metaphorical connection rather than the software – suggesting that perhaps random results might be just as good for sparking creativity, though Neeley insists that isn’t the case.

The company is now testing its algorithms to determine the best balance point between the literal results of the type returned by Google and random results. “There’s this really nice sweet spot where results are disparate but close enough that people can make that leap,” says Neeley. n

“Detecting metaphors is pretty difficult in itself, mapping between them is even more difficult”

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Magic away photo intrudersHas a stream of schoolchildren or drunken stag party just ruined your holiday snap of Rome’s Trevi fountain? A new smartphone app promises to let you edit out such intruders in your photos, leaving the beautiful view intact. Called Remove, the upcoming app, developed by Swedish photography firm Scalado, takes a burst of shots of your scene. It identifies the objects which are moving, based on their relative position in each frame. These objects are highlighted and you can delete the ones you don’t want. Scalado will showcase the app at the 2012 Mobile World Conference in Barcelona, Spain, this month.

Interactive maps let tribes keep tabsOnline games and interactive maps could help pygmy tribes in Africa fight logging and poaching in their area. In 2009, anthropologist Jerome Lewis developed a handheld device that let tribes geotag important trees in their area to create online maps. Now Lewis is building a platform for online games to teach indigenous people how to read and interact with these maps using smartphones. One game, Blindate, requires map-reading in order for two players to meet one another. Its aim is to help tribe members get used to locating themselves on maps.

Tweet me no politicsPeople on Twitter link to others who share their interests, but not always their political views. Yaneer Bar-Yam at the New England Complex Systems Institute in Cambridge, Massachusetts, collected 521,733 tweets posted by 223,950 users who tweeted at least three links to articles in The New York Times between 14 and 29 September 2011. The team then used a mathematical model to work out who tweeted to whom, where and about what. Rather than around politics, people clustered mainly according to whether their links and interests were global, US-wide, or just within New York City. Of the 10 most popular subjects US-wide, science came ninth.

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Kristensson. It should also work when applied to more standard text and speech recognition systems, he says. The work appears in the Proceedings of the 2011 Conference on Empirical Method in Natural Language Processing.

David Weir at the University of Sussex, UK, agrees that the system has potential. “Getting adequate quantities of good quality data to build statistical [language] models is one of the most significant challenges in this area,” he says. duncan Graham-rowe n–Here to help–

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